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Quantitative Research – Methods, Types and Analysis

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Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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What is quantitative research.

Quantitative methodologies use statistics to analyze numerical data gathered by researchers to answer their research questions. Quantitative methods can be used to answer questions such as:

• What are the relationships between two or more variables? 
• What factors are at play in an environment that might affect the behavior or development of the organisms in that environment?

Quantitative methods can also be used to test hypotheses by conducting quasi-experimental studies or designing experiments.

Independent and Dependent Variables

In quantitative research, a variable is something (an intervention technique, a pharmaceutical, a temperature, etc.) that changes. There are two kinds of variables:  independent variables and dependent variables . In the simplest terms, the independent variable is whatever the researchers are using to attempt to make a change in their dependent variable.

* This is a real, repeatable experiment you can try on your plants.

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

• Köse S., & Murat, M. (2021). Examination of the relationship between smartphone addiction and cyberchondria in adolescents. Archives of Psychiatric Nursing, 35(6): 563-570.
• Pilger et al. (2021). Spiritual well-being, religious/spiritual coping and quality of life among the elderly undergoing hemodialysis: a correlational study. Journal of Religion, Spirituality & Aging, 33(1): 2-15.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal. 

• Lakshmi, E. (2021). Food consumption pattern and body mass index of adolescents: A descriptive study. International Journal of Nutrition, Pharmacology, Neurological Diseases, 11(4), 293–297.
• Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gašević, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems, 127, 194–207.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually. 

• Jinzhang Jia, Yinuo Chen, Guangbo Che, Jinchao Zhu, Fengxiao Wang, & Peng Jia. (2021). Experimental study on the explosion characteristics of hydrogen-methane premixed gas in complex pipe networks. Scientific Reports, 11(1), 1–11.
• Sasaki, R. et al. (2021). Effects of cryotherapy applied at different temperatures on inflammatory pain during the acute phase of arthritis in rats. Physical Therapy, 101(2), 1–9.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y,  and  Z are also playing a role.

• Jafari, A., Alami, A., Charoghchian, E., Delshad Noghabi, A., & Nejatian, M. (2021). The impact of effective communication skills training on the status of marital burnout among married women. BMC Women’s Health, 21(1), 1-10.
• Phillips, S. W., Kim, D.-Y., Sobol, J. J., & Gayadeen, S. M. (2021). Total recall?: A quasi-experimental study of officer’s recollection in shoot - don’t shoot simulators. Police Practice and Research, 22(3), 1229–1240.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses. 

• Harries et al. (2021). Effects of the COVID-19 pandemic on medical students: A multicenter quantitative study. BMC Medical Education, 21(14), 1-8.
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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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• Chapter Four: Quantitative Methods (Part 1)

Once you have chosen a topic to investigate, you need to decide which type of method is best to study it. This is one of the most important choices you will make on your research journey. Understanding the value of each of the methods described in this textbook to answer different questions allows you to be able to plan your own studies with more confidence, critique the studies others have done, and provide advice to your colleagues and friends on what type of research they should do to answer questions they have. After briefly reviewing quantitative research assumptions, this chapter is organized in three parts or sections. These parts can also be used as a checklist when working through the steps of your study. Specifically, part 1 focuses on planning a quantitative study (collecting data), part two explains the steps involved in doing a quantitative study, and part three discusses how to make sense of your results (organizing and analyzing data).

• Chapter One: Introduction
• Chapter Two: Understanding the distinctions among research methods
• Chapter Three: Ethical research, writing, and creative work
• Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
• Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
• Chapter Five: Qualitative Methods (Part 1)
• Chapter Five: Qualitative Data (Part 2)
• Chapter Six: Critical / Rhetorical Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 2)
• Chapter Seven: Presenting Your Results

Quantitative Worldview Assumptions: A Review

In chapter 2, you were introduced to the unique assumptions quantitative research holds about knowledge and how it is created, or what the authors referred to in chapter one as "epistemology." Understanding these assumptions can help you better determine whether you need to use quantitative methods for a particular research study in which you are interested.

Quantitative researchers believe there is an objective reality, which can be measured. "Objective" here means that the researcher is not relying on their own perceptions of an event. S/he is attempting to gather "facts" which may be separate from people's feeling or perceptions about the facts. These facts are often conceptualized as "causes" and "effects." When you ask research questions or pose hypotheses with words in them such as "cause," "effect," "difference between," and "predicts," you are operating under assumptions consistent with quantitative methods. The overall goal of quantitative research is to develop generalizations that enable the researcher to better predict, explain, and understand some phenomenon.

Because of trying to prove cause-effect relationships that can be generalized to the population at large, the research process and related procedures are very important for quantitative methods. Research should be consistently and objectively conducted, without bias or error, in order to be considered to be valid (accurate) and reliable (consistent). Perhaps this emphasis on accurate and standardized methods is because the roots of quantitative research are in the natural and physical sciences, both of which have at their base the need to prove hypotheses and theories in order to better understand the world in which we live. When a person goes to a doctor and is prescribed some medicine to treat an illness, that person is glad such research has been done to know what the effects of taking this medicine is on others' bodies, so s/he can trust the doctor's judgment and take the medicines.

As covered in chapters 1 and 2, the questions you are asking should lead you to a certain research method choice. Students sometimes want to avoid doing quantitative research because of fear of math/statistics, but if their questions call for that type of research, they should forge ahead and use it anyway. If a student really wants to understand what the causes or effects are for a particular phenomenon, they need to do quantitative research. If a student is interested in what sorts of things might predict a person's behavior, they need to do quantitative research. If they want to confirm the finding of another researcher, most likely they will need to do quantitative research. If a student wishes to generalize beyond their participant sample to a larger population, they need to be conducting quantitative research.

So, ultimately, your choice of methods really depends on what your research goal is. What do you really want to find out? Do you want to compare two or more groups, look for relationships between certain variables, predict how someone will act or react, or confirm some findings from another study? If so, you want to use quantitative methods.

A topic such as self-esteem can be studied in many ways. Listed below are some example RQs about self-esteem. Which of the following research questions should be answered with quantitative methods?

• Is there a difference between men's and women's level of self- esteem?
• How do college-aged women describe their ups and downs with self-esteem?
• How has "self-esteem" been constructed in popular self-help books over time?
• Is there a relationship between self-esteem levels and communication apprehension?

What are the advantages of approaching a topic like self-esteem using quantitative methods? What are the disadvantages?

For more information, see the following website: Analyse This!!! Learning to analyse quantitative data

Answers:  1 & 4

Quantitative Methods Part One: Planning Your Study

Planning your study is one of the most important steps in the research process when doing quantitative research. As seen in the diagram below, it involves choosing a topic, writing research questions/hypotheses, and designing your study. Each of these topics will be covered in detail in this section of the chapter.

Topic Choice

Decide on topic.

How do you go about choosing a topic for a research project? One of the best ways to do this is to research something about which you would like to know more. Your communication professors will probably also want you to select something that is related to communication and things you are learning about in other communication classes.

When the authors of this textbook select research topics to study, they choose things that pique their interest for a variety of reasons, sometimes personal and sometimes because they see a need for more research in a particular area. For example, April Chatham-Carpenter studies adoption return trips to China because she has two adopted daughters from China and because there is very little research on this topic for Chinese adoptees and their families; she studied home vs. public schooling because her sister home schools, and at the time she started the study very few researchers had considered the social network implications for home schoolers (cf.  http://www.uni.edu/chatham/homeschool.html ).

When you are asked in this class and other classes to select a topic to research, think about topics that you have wondered about, that affect you personally, or that know have gaps in the research. Then start writing down questions you would like to know about this topic. These questions will help you decide whether the goal of your study is to understand something better, explain causes and effects of something, gather the perspectives of others on a topic, or look at how language constructs a certain view of reality.

Review Previous Research

In quantitative research, you do not rely on your conclusions to emerge from the data you collect. Rather, you start out looking for certain things based on what the past research has found. This is consistent with what was called in chapter 2 as a deductive approach (Keyton, 2011), which also leads a quantitative researcher to develop a research question or research problem from reviewing a body of literature, with the previous research framing the study that is being done. So, reviewing previous research done on your topic is an important part of the planning of your study. As seen in chapter 3 and the Appendix, to do an adequate literature review, you need to identify portions of your topic that could have been researched in the past. To do that, you select key terms of concepts related to your topic.

Some people use concept maps to help them identify useful search terms for a literature review. For example, see the following website: Concept Mapping: How to Start Your Term Paper Research .

Narrow Topic to Researchable Area

Once you have selected your topic area and reviewed relevant literature related to your topic, you need to narrow your topic to something that can be researched practically and that will take the research on this topic further. You don't want your research topic to be so broad or large that you are unable to research it. Plus, you want to explain some phenomenon better than has been done before, adding to the literature and theory on a topic. You may want to test out what someone else has found, replicating their study, and therefore building to the body of knowledge already created.

To see how a literature review can be helpful in narrowing your topic, see the following sources.  Narrowing or Broadening Your Research Topic  and  How to Conduct a Literature Review in Social Science

Research Questions & Hypotheses

Write Your Research Questions (RQs) and/or Hypotheses (Hs)

Once you have narrowed your topic based on what you learned from doing your review of literature, you need to formalize your topic area into one or more research questions or hypotheses. If the area you are researching is a relatively new area, and no existing literature or theory can lead you to predict what you might find, then you should write a research question. Take a topic related to social media, for example, which is a relatively new area of study. You might write a research question that asks:

"Is there a difference between how 1st year and 4th year college students use Facebook to communicate with their friends?"

If, however, you are testing out something you think you might find based on the findings of a large amount of previous literature or a well-developed theory, you can write a hypothesis. Researchers often distinguish between  null  and  alternative  hypotheses. The alternative hypothesis is what you are trying to test or prove is true, while the null hypothesis assumes that the alternative hypothesis is not true. For example, if the use of Facebook had been studied a great deal, and there were theories that had been developed on the use of it, then you might develop an alternative hypothesis, such as: "First-year students spend more time on using Facebook to communicate with their friends than fourth-year students do." Your null hypothesis, on the other hand, would be: "First-year students do  not  spend any more time using Facebook to communication with their friends than fourth-year students do." Researchers, however, only state the alternative hypothesis in their studies, and actually call it "hypothesis" rather than "alternative hypothesis."

Process of Writing a Research Question/Hypothesis.

Once you have decided to write a research question (RQ) or hypothesis (H) for your topic, you should go through the following steps to create your RQ or H.

Name the concepts from your overall research topic that you are interested in studying.

RQs and Hs have variables, or concepts that you are interested in studying. Variables can take on different values. For example, in the RQ above, there are at least two variables – year in college and use of Facebook (FB) to communicate. Both of them have a variety of levels within them.

When you look at the concepts you identified, are there any concepts which seem to be related to each other? For example, in our RQ, we are interested in knowing if there is a difference between first-year students and fourth-year students in their use of FB, meaning that we believe there is some connection between our two variables.

• Decide what type of a relationship you would like to study between the variables. Do you think one causes the other? Does a difference in one create a difference in the other? As the value of one changes, does the value of the other change?

Identify which one of these concepts is the independent (or predictor) variable, or the concept that is perceived to be the cause of change in the other variable? Which one is the dependent (criterion) variable, or the one that is affected by changes in the independent variable? In the above example RQ, year in school is the independent variable, and amount of time spent on Facebook communicating with friends is the dependent variable. The amount of time spent on Facebook depends on a person's year in school.

If you're still confused about independent and dependent variables, check out the following site: Independent & Dependent Variables .

Express the relationship between the concepts as a single sentence – in either a hypothesis or a research question.

For example, "is there a difference between international and American students on their perceptions of the basic communication course," where cultural background and perceptions of the course are your two variables. Cultural background would be the independent variable, and perceptions of the course would be your dependent variable. More examples of RQs and Hs are provided in the next section.

APPLICATION: Try the above steps with your topic now. Check with your instructor to see if s/he would like you to send your topic and RQ/H to him/her via e-mail.

Types of Research Questions/Hypotheses

Once you have written your RQ/H, you need to determine what type of research question or hypothesis it is. This will help you later decide what types of statistics you will need to run to answer your question or test your hypothesis. There are three possible types of questions you might ask, and two possible types of hypotheses. The first type of question cannot be written as a hypothesis, but the second and third types can.

Descriptive Question.

The first type of question is a descriptive question. If you have only one variable or concept you are studying, OR if you are not interested in how the variables you are studying are connected or related to each other, then your question is most likely a descriptive question.

This type of question is the closest to looking like a qualitative question, and often starts with a "what" or "how" or "why" or "to what extent" type of wording. What makes it different from a qualitative research question is that the question will be answered using numbers rather than qualitative analysis. Some examples of a descriptive question, using the topic of social media, include the following.

"To what extent are college-aged students using Facebook to communicate with their friends?"

"Why do college-aged students use Facebook to communicate with their friends?"

Notice that neither of these questions has a clear independent or dependent variable, as there is no clear cause or effect being assumed by the question. The question is merely descriptive in nature. It can be answered by summarizing the numbers obtained for each category, such as by providing percentages, averages, or just the raw totals for each type of strategy or organization. This is true also of the following research questions found in a study of online public relations strategies:

"What online public relations strategies are organizations implementing to combat phishing" (Baker, Baker, & Tedesco, 2007, p. 330), and

"Which organizations are doing most and least, according to recommendations from anti- phishing advocacy recommendations, to combat phishing" (Baker, Baker, & Tedesco, 2007, p. 330)

The researchers in this study reported statistics in their results or findings section, making it clearly a quantitative study, but without an independent or dependent variable; therefore, these research questions illustrate the first type of RQ, the descriptive question.

Difference Question/Hypothesis.

The second type of question is a question/hypothesis of difference, and will often have the word "difference" as part of the question. The very first research question in this section, asking if there is a difference between 1st year and 4th year college students' use of Facebook, is an example of this type of question. In this type of question, the independent variable is some type of grouping or categories, such as age. Another example of a question of difference is one April asked in her research on home schooling: "Is there a difference between home vs. public schoolers on the size of their social networks?" In this example, the independent variable is home vs. public schooling (a group being compared), and the dependent variable is size of social networks. Hypotheses can also be difference hypotheses, as the following example on the same topic illustrates: "Public schoolers have a larger social network than home schoolers do."

Relationship/Association Question/Hypothesis.

The third type of question is a relationship/association question or hypothesis, and will often have the word "relate" or "relationship" in it, as the following example does: "There is a relationship between number of television ads for a political candidate and how successful that political candidate is in getting elected." Here the independent (or predictor) variable is number of TV ads, and the dependent (or criterion) variable is the success at getting elected. In this type of question, there is no grouping being compared, but rather the independent variable is continuous (ranges from zero to a certain number) in nature. This type of question can be worded as either a hypothesis or as a research question, as stated earlier.

Test out your knowledge of the above information, by answering the following questions about the RQ/H listed below. (Remember, for a descriptive question there are no clear independent & dependent variables.)

• What is the independent variable (IV)?
• What is the dependent variable (DV)?
• What type of research question/hypothesis is it? (descriptive, difference, relationship/association)
• "Is there a difference on relational satisfaction between those who met their current partner through online dating and those who met their current partner face-to-face?"
• "How do Fortune 500 firms use focus groups to market new products?"
• "There is a relationship between age and amount of time spent online using social media."

Answers: RQ1  is a difference question, with type of dating being the IV and relational satisfaction being the DV. RQ2  is a descriptive question with no IV or DV. RQ3  is a relationship hypothesis with age as the IV and amount of time spent online as the DV.

Design Your Study

The third step in planning your research project, after you have decided on your topic/goal and written your research questions/hypotheses, is to design your study which means to decide how to proceed in gathering data to answer your research question or to test your hypothesis. This step includes six things to do. [NOTE: The terms used in this section will be defined as they are used.]

• Decide type of study design: Experimental, quasi-experimental, non-experimental.
• Decide kind of data to collect: Survey/interview, observation, already existing data.
• Operationalize variables into measurable concepts.
• Determine type of sample: Probability or non-probability.
• Decide how you will collect your data: face-to-face, via e-mail, an online survey, library research, etc.
• Pilot test your methods.

Types of Study Designs

With quantitative research being rooted in the scientific method, traditional research is structured in an experimental fashion. This is especially true in the natural sciences, where they try to prove causes and effects on topics such as successful treatments for cancer. For example, the University of Iowa Hospitals and Clinics regularly conduct clinical trials to test for the effectiveness of certain treatments for medical conditions ( University of Iowa Hospitals & Clinics: Clinical Trials ). They use human participants to conduct such research, regularly recruiting volunteers. However, in communication, true experiments with treatments the researcher controls are less necessary and thus less common. It is important for the researcher to understand which type of study s/he wishes to do, in order to accurately communicate his/her methods to the public when describing the study.

There are three possible types of studies you may choose to do, when embarking on quantitative research: (a) True experiments, (b) quasi-experiments, and (c) non-experiments.

For more information to read on these types of designs, take a look at the following website and related links in it: Types of Designs .

The following flowchart should help you distinguish between the three types of study designs described below.

True Experiments.

The first two types of study designs use difference questions/hypotheses, as the independent variable for true and quasi-experiments is  nominal  or categorical (based on categories or groupings), as you have groups that are being compared. As seen in the flowchart above, what distinguishes a true experiment from the other two designs is a concept called "random assignment." Random assignment means that the researcher controls to which group the participants are assigned. April's study of home vs. public schooling was NOT a true experiment, because she could not control which participants were home schooled and which ones were public schooled, and instead relied on already existing groups.

An example of a true experiment reported in a communication journal is a study investigating the effects of using interest-based contemporary examples in a lecture on the history of public relations, in which the researchers had the following two hypotheses: "Lectures utilizing interest- based examples should result in more interested participants" and "Lectures utilizing interest- based examples should result in participants with higher scores on subsequent tests of cognitive recall" (Weber, Corrigan, Fornash, & Neupauer, 2003, p. 118). In this study, the 122 college student participants were randomly assigned by the researchers to one of two lecture video viewing groups: a video lecture with traditional examples and a video with contemporary examples. (To see the results of the study, look it up using your school's library databases).

A second example of a true experiment in communication is a study of the effects of viewing either a dramatic narrative television show vs. a nonnarrative television show about the consequences of an unexpected teen pregnancy. The researchers randomly assigned their 367 undergraduate participants to view one of the two types of shows.

Moyer-Gusé, E., & Nabi, R. L. (2010). Explaining the effects of narrative in an entertainment television program: Overcoming resistance to persuasion.  Human Communication Research, 36 , 26-52.

A third example of a true experiment done in the field of communication can be found in the following study.

Jensen, J. D. (2008). Scientific uncertainty in news coverage of cancer research: Effects of hedging on scientists' and journalists' credibility.  Human Communication Research, 34,  347-369.

In this study, Jakob Jensen had three independent variables. He randomly assigned his 601 participants to 1 of 20 possible conditions, between his three independent variables, which were (a) a hedged vs. not hedged message, (b) the source of the hedging message (research attributed to primary vs. unaffiliated scientists), and (c) specific news story employed (of which he had five randomly selected news stories about cancer research to choose from). Although this study was pretty complex, it does illustrate the true experiment in our field since the participants were randomly assigned to read a particular news story, with certain characteristics.

Quasi-Experiments.

If the researcher is not able to randomly assign participants to one of the treatment groups (or independent variable), but the participants already belong to one of them (e.g., age; home vs. public schooling), then the design is called a quasi-experiment. Here you still have an independent variable with groups, but the participants already belong to a group before the study starts, and the researcher has no control over which group they belong to.

An example of a hypothesis found in a communication study is the following: "Individuals high in trait aggression will enjoy violent content more than nonviolent content, whereas those low in trait aggression will enjoy violent content less than nonviolent content" (Weaver & Wilson, 2009, p. 448). In this study, the researchers could not assign the participants to a high or low trait aggression group since this is a personality characteristic, so this is a quasi-experiment. It does not have any random assignment of participants to the independent variable groups. Read their study, if you would like to, at the following location.

Weaver, A. J., & Wilson, B. J. (2009). The role of graphic and sanitized violence in the enjoyment of television dramas.  Human Communication Research, 35  (3), 442-463.

Benoit and Hansen (2004) did not choose to randomly assign participants to groups either, in their study of a national presidential election survey, in which they were looking at differences between debate and non-debate viewers, in terms of several dependent variables, such as which candidate viewers supported. If you are interested in discovering the results of this study, take a look at the following article.

Benoit, W. L., & Hansen, G. J. (2004). Presidential debate watching, issue knowledge, character evaluation, and vote choice.  Human Communication Research, 30  (1), 121-144.

Non-Experiments.

The third type of design is the non-experiment. Non-experiments are sometimes called survey designs, because their primary way of collecting data is through surveys. This is not enough to distinguish them from true experiments and quasi-experiments, however, as both of those types of designs may use surveys as well.

What makes a study a non-experiment is that the independent variable is not a grouping or categorical variable. Researchers observe or survey participants in order to describe them as they naturally exist without any experimental intervention. Researchers do not give treatments or observe the effects of a potential natural grouping variable such as age. Descriptive and relationship/association questions are most often used in non-experiments.

Some examples of this type of commonly used design for communication researchers include the following studies.

• Serota, Levine, and Boster (2010) used a national survey of 1,000 adults to determine the prevalence of lying in America (see  Human Communication Research, 36 , pp. 2-25).
• Nabi (2009) surveyed 170 young adults on their perceptions of reality television on cosmetic surgery effects, looking at several things: for example, does viewing cosmetic surgery makeover programs relate to body satisfaction (p. 6), finding no significant relationship between those two variables (see  Human Communication Research, 35 , pp. 1-27).
• Derlega, Winstead, Mathews, and Braitman (2008) collected stories from 238 college students on reasons why they would disclose or not disclose personal information within close relationships (see  Communication Research Reports, 25 , pp. 115-130). They coded the participants' answers into categories so they could count how often specific reasons were mentioned, using a method called  content analysis , to answer the following research questions:

RQ1: What are research participants' attributions for the disclosure and nondisclosure of highly personal information?

RQ2: Do attributions reflect concerns about rewards and costs of disclosure or the tension between openness with another and privacy?

RQ3: How often are particular attributions for disclosure/nondisclosure used in various types of relationships? (p. 117)

All of these non-experimental studies have in common no researcher manipulation of an independent variable or even having an independent variable that has natural groups that are being compared.

Identify which design discussed above should be used for each of the following research questions.

• Is there a difference between generations on how much they use MySpace?
• Is there a relationship between age when a person first started using Facebook and the amount of time they currently spend on Facebook daily?
• Is there a difference between potential customers' perceptions of an organization who are shown an organization's Facebook page and those who are not shown an organization's Facebook page?

[HINT: Try to identify the independent and dependent variable in each question above first, before determining what type of design you would use. Also, try to determine what type of question it is – descriptive, difference, or relationship/association.]

Answers: 1. Quasi-experiment 2. Non-experiment 3. True Experiment

Data Collection Methods

Once you decide the type of quantitative research design you will be using, you will need to determine which of the following types of data you will collect: (a) survey data, (b) observational data, and/or (c) already existing data, as in library research.

Using the survey data collection method means you will talk to people or survey them about their behaviors, attitudes, perceptions, and demographic characteristics (e.g., biological sex, socio-economic status, race). This type of data usually consists of a series of questions related to the concepts you want to study (i.e., your independent and dependent variables). Both of April's studies on home schooling and on taking adopted children on a return trip back to China used survey data.

On a survey, you can have both closed-ended and open-ended questions. Closed-ended questions, can be written in a variety of forms. Some of the most common response options include the following.

Likert responses – for example: for the following statement, ______ do you strongly agree agree neutral disagree strongly disagree

Semantic differential – for example: does the following ______ make you Happy ..................................... Sad

Yes-no answers for example: I use social media daily. Yes / No.

One site to check out for possible response options is  http://www.360degreefeedback.net/media/ResponseScales.pdf .

Researchers often follow up some of their closed-ended questions with an "other" category, in which they ask their participants to "please specify," their response if none of the ones provided are applicable. They may also ask open-ended questions on "why" a participant chose a particular answer or ask participants for more information about a particular topic. If the researcher wants to use the open-ended question responses as part of his/her quantitative study, the answers are usually coded into categories and counted, in terms of the frequency of a certain answer, using a method called  content analysis , which will be discussed when we talk about already-existing artifacts as a source of data.

Surveys can be done face-to-face, by telephone, mail, or online. Each of these methods has its own advantages and disadvantages, primarily in the form of the cost in time and money to do the survey. For example, if you want to survey many people, then online survey tools such as surveygizmo.com and surveymonkey.com are very efficient, but not everyone has access to taking a survey on the computer, so you may not get an adequate sample of the population by doing so. Plus you have to decide how you will recruit people to take your online survey, which can be challenging. There are trade-offs with every method.

For more information on things to consider when selecting your survey method, check out the following website:

Selecting the Survey Method .

There are also many good sources for developing a good survey, such as the following websites. Constructing the Survey Survey Methods Designing Surveys

Observation.

A second type of data collection method is  observation . In this data collection method, you make observations of the phenomenon you are studying and then code your observations, so that you can count what you are studying. This type of data collection method is often called interaction analysis, if you collect data by observing people's behavior. For example, if you want to study the phenomenon of mall-walking, you could go to a mall and count characteristics of mall-walkers. A researcher in the area of health communication could study the occurrence of humor in an operating room, for example, by coding and counting the use of humor in such a setting.

One extended research study using observational data collection methods, which is cited often in interpersonal communication classes, is John Gottman's research, which started out in what is now called "The Love Lab." In this lab, researchers observe interactions between couples, including physiological symptoms, using coders who look for certain items found to predict relationship problems and success.

Take a look at the YouTube video about "The Love Lab" at the following site to learn more about the potential of using observation in collecting data for a research study:  The "Love" Lab .

Already-Existing Artifacts.

The third method of quantitative data collection is the use of  already-existing artifacts . With this method, you choose certain artifacts (e.g., newspaper or magazine articles; television programs; webpages) and code their content, resulting in a count of whatever you are studying. With this data collection method, researchers most often use what is called quantitative  content analysis . Basically, the researcher counts frequencies of something that occurs in an artifact of study, such as the frequency of times something is mentioned on a webpage. Content analysis can also be used in qualitative research, where a researcher identifies and creates text-based themes but does not do a count of the occurrences of these themes. Content analysis can also be used to take open-ended questions from a survey method, and identify countable themes within the questions.

Content analysis is a very common method used in media studies, given researchers are interested in studying already-existing media artifacts. There are many good sources to illustrate how to do content analysis such as are seen in the box below.

See the following sources for more information on content analysis. Writing Guide: Content Analysis A Flowchart for the Typical Process of Content Analysis Research What is Content Analysis?

With content analysis and any method that you use to code something into categories, one key concept you need to remember is  inter-coder or inter-rater reliability , in which there are multiple coders (at least two) trained to code the observations into categories. This check on coding is important because you need to check to make sure that the way you are coding your observations on the open-ended answers is the same way that others would code a particular item. To establish this kind of inter-coder or inter-rater reliability, researchers prepare codebooks (to train their coders on how to code the materials) and coding forms for their coders to use.

To see some examples of actual codebooks used in research, see the following website:  Human Coding--Sample Materials .

There are also online inter-coder reliability calculators some researchers use, such as the following:  ReCal: reliability calculation for the masses .

Regardless of which method of data collection you choose, you need to decide even more specifically how you will measure the variables in your study, which leads us to the next planning step in the design of a study.

Operationalization of Variables into Measurable Concepts

When you look at your research question/s and/or hypotheses, you should know already what your independent and dependent variables are. Both of these need to be measured in some way. We call that way of measuring  operationalizing  a variable. One way to think of it is writing a step by step recipe for how you plan to obtain data on this topic. How you choose to operationalize your variable (or write the recipe) is one all-important decision you have to make, which will make or break your study. In quantitative research, you have to measure your variables in a valid (accurate) and reliable (consistent) manner, which we discuss in this section. You also need to determine the level of measurement you will use for your variables, which will help you later decide what statistical tests you need to run to answer your research question/s or test your hypotheses. We will start with the last topic first.

Level of Measurement

Level of measurement has to do with whether you measure your variables using categories or groupings OR whether you measure your variables using a continuous level of measurement (range of numbers). The level of measurement that is considered to be categorical in nature is called nominal, while the levels of measurement considered to be continuous in nature are ordinal, interval, and ratio. The only ones you really need to know are nominal, ordinal, and interval/ratio.

Nominal  variables are categories that do not have meaningful numbers attached to them but are broader categories, such as male and female, home schooled and public schooled, Caucasian and African-American.  Ordinal  variables do have numbers attached to them, in that the numbers are in a certain order, but there are not equal intervals between the numbers (e.g., such as when you rank a group of 5 items from most to least preferred, where 3 might be highly preferred, and 2 hated).  Interval/ratio  variables have equal intervals between the numbers (e.g., weight, age).

For more information about these levels of measurement, check out one of the following websites. Levels of Measurement Measurement Scales in Social Science Research What is the difference between ordinal, interval and ratio variables? Why should I care?

Validity and Reliability

When developing a scale/measure or survey, you need to be concerned about validity and reliability. Readers of quantitative research expect to see researchers justify their research measures using these two terms in the methods section of an article or paper.

Validity.   Validity  is the extent to which your scale/measure or survey adequately reflects the full meaning of the concept you are measuring. Does it measure what you say it measures? For example, if researchers wanted to develop a scale to measure "servant leadership," the researchers would have to determine what dimensions of servant leadership they wanted to measure, and then create items which would be valid or accurate measures of these dimensions. If they included items related to a different type of leadership, those items would not be a valid measure of servant leadership. When doing so, the researchers are trying to prove their measure has internal validity. Researchers may also be interested in external validity, but that has to do with how generalizable their study is to a larger population (a topic related to sampling, which we will consider in the next section), and has less to do with the validity of the instrument itself.

There are several types of validity you may read about, including face validity, content validity, criterion-related validity, and construct validity. To learn more about these types of validity, read the information at the following link: Validity .

To improve the validity of an instrument, researchers need to fully understand the concept they are trying to measure. This means they know the academic literature surrounding that concept well and write several survey questions on each dimension measured, to make sure the full idea of the concept is being measured. For example, Page and Wong (n.d.) identified four dimensions of servant leadership: character, people-orientation, task-orientation, and process-orientation ( A Conceptual Framework for Measuring Servant-Leadership ). All of these dimensions (and any others identified by other researchers) would need multiple survey items developed if a researcher wanted to create a new scale on servant leadership.

Before you create a new survey, it can be useful to see if one already exists with established validity and reliability. Such measures can be found by seeing what other respected studies have used to measure a concept and then doing a library search to find the scale/measure itself (sometimes found in the reference area of a library in books like those listed below).

Reliability .  Reliability  is the second criterion you will need to address if you choose to develop your own scale or measure. Reliability is concerned with whether a measurement is consistent and reproducible. If you have ever wondered why, when taking a survey, that a question is asked more than once or very similar questions are asked multiple times, it is because the researchers one concerned with proving their study has reliability. Are you, for example, answering all of the similar questions similarly? If so, the measure/scale may have good reliability or consistency over time.

Researchers can use a variety of ways to show their measure/scale is reliable. See the following websites for explanations of some of these ways, which include methods such as the test-retest method, the split-half method, and inter-coder/rater reliability. Types of Reliability Reliability

To understand the relationship between validity and reliability, a nice visual provided below is explained at the following website (Trochim, 2006, para. 2). Reliability & Validity

Self-Quiz/Discussion:

Take a look at one of the surveys found at the following poll reporting sites on a topic which interests you. Critique one of these surveys, using what you have learned about creating surveys so far.

http://www.pewinternet.org/ http://pewresearch.org/ http://www.gallup.com/Home.aspx http://www.kff.org/

One of the things you might have critiqued in the previous self-quiz/discussion may have had less to do with the actual survey itself, but rather with how the researchers got their participants or sample. How participants are recruited is just as important to doing a good study as how valid and reliable a survey is.

Imagine that in the article you chose for the last "self-quiz/discussion" you read the following quote from the Pew Research Center's Internet and American Life Project: "One in three teens sends more than 100 text messages a day, or 3000 texts a month" (Lenhart, 2010, para.5). How would you know whether you could trust this finding to be true? Would you compare it to what you know about texting from your own and your friends' experiences? Would you want to know what types of questions people were asked to determine this statistic, or whether the survey the statistic is based on is valid and reliable? Would you want to know what type of people were surveyed for the study? As a critical consumer of research, you should ask all of these types of questions, rather than just accepting such a statement as undisputable fact. For example, if only people shopping at an Apple Store were surveyed, the results might be skewed high.

In particular, related to the topic of this section, you should ask about the sampling method the researchers did. Often, the researchers will provide information related to the sample, stating how many participants were surveyed (in this case 800 teens, aged 12-17, who were a nationally representative sample of the population) and how much the "margin of error" is (in this case +/- 3.8%). Why do they state such things? It is because they know the importance of a sample in making the case for their findings being legitimate and credible.  Margin of error  is how much we are confident that our findings represent the population at large. The larger the margin of error, the less likely it is that the poll or survey is accurate. Margin of error assumes a 95% confidence level that what we found from our study represents the population at large.

For more information on margin of error, see one of the following websites. Answers.com Margin of Error Stats.org Margin of Error Americanresearchgroup.com Margin of Error [this last site is a margin of error calculator, which shows that margin of error is directly tied to the size of your sample, in relationship to the size of the population, two concepts we will talk about in the next few paragraphs]

In particular, this section focused on sampling will talk about the following topics: (a) the difference between a population vs. a sample; (b) concepts of error and bias, or "it's all about significance"; (c) probability vs. non-probability sampling; and (d) sample size issues.

Population vs. Sample

When doing quantitative studies, such as the study of cell phone usage among teens, you are never able to survey the entire population of teenagers, so you survey a portion of the population. If you study every member of a population, then you are conducting a census such as the United States Government does every 10 years. When, however, this is not possible (because you do not have the money the U.S. government has!), you attempt to get as good a sample as possible.

Characteristics of a population are summarized in numerical form, and technically these numbers are called  parameters . However, numbers which summarize the characteristics of a sample are called  statistics .

Error and Bias

If a sample is not done well, then you may not have confidence in how the study's results can be generalized to the population from which the sample was taken. Your confidence level is often stated as the  margin of error  of the survey. As noted earlier, a study's margin of error refers to the degree to which a sample differs from the total population you are studying. In the Pew survey, they had a margin of error of +/- 3.8%. So, for example, when the Pew survey said 33% of teens send more than 100 texts a day, the margin of error means they were 95% sure that 29.2% - 36.8% of teens send this many texts a day.

Margin of error is tied to  sampling error , which is how much difference there is between your sample's results and what would have been obtained if you had surveyed the whole population. Sample error is linked to a very important concept for quantitative researchers, which is the notion of  significance . Here, significance does not refer to whether some finding is morally or practically significant, it refers to whether a finding is statistically significant, meaning the findings are not due to chance but actually represent something that is found in the population.  Statistical significance  is about how much you, as the researcher, are willing to risk saying you found something important and be wrong.

For the difference between statistical significance and practical significance, see the following YouTube video:  Statistical and Practical Significance .

Scientists set certain arbitrary standards based on the probability they could be wrong in reporting their findings. These are called  significance levels  and are commonly reported in the literature as  p <.05  or  p <.01  or some other probability (or  p ) level.

If an article says a statistical test reported that  p < .05 , it simply means that they are most likely correct in what they are saying, but there is a 5% chance they could be wrong and not find the same results in the population. If p < .01, then there would be only a 1% chance they were wrong and would not find the same results in the population. The lower the probability level, the more certain the results.

When researchers are wrong, or make that kind of decision error, it often implies that either (a) their sample was biased and was not representative of the true population in some way, or (b) that something they did in collecting the data biased the results. There are actually two kinds of sampling error talked about in quantitative research: Type I and Type II error.  Type 1 error  is what happens when you think you found something statistically significant and claim there is a significant difference or relationship, when there really is not in the actual population. So there is something about your sample that made you find something that is not in the actual population. (Type I error is the same as the probability level, or .05, if using the traditional p-level accepted by most researchers.)  Type II error  happens when you don't find a statistically significant difference or relationship, yet there actually is one in the population at large, so once again, your sample is not representative of the population.

For more information on these two types of error, check out the following websites. Hypothesis Testing: Type I Error, Type II Error Type I and Type II Errors - Making Mistakes in the Justice System

Researchers want to select a sample that is representative of the population in order to reduce the likelihood of having a sample that is biased. There are two types of bias particularly troublesome for researchers, in terms of sampling error. The first type is  selection bias , in which each person in the population does not have an equal chance to be chosen for the sample, which happens frequently in communication studies, because we often rely on convenience samples (whoever we can get to complete our surveys). The second type of bias is  response bias , in which those who volunteer for a study have different characteristics than those who did not volunteer for the study, another common challenge for communication researchers. Volunteers for a study may very well be different from persons who choose not to volunteer for a study, so that you have a biased sample by relying just on volunteers, which is not representative of the population from which you are trying to sample.

Probability vs. Non-Probability Sampling

One of the best ways to lower your sampling error and reduce the possibility of bias is to do probability or random sampling. This means that every person in the population has an equal chance of being selected to be in your sample. Another way of looking at this is to attempt to get a  representative  sample, so that the characteristics of your sample closely approximate those of the population. A sample needs to contain essentially the same variations that exist in the population, if possible, especially on the variables or elements that are most important to you (e.g., age, biological sex, race, level of education, socio-economic class).

There are many different ways to draw a probability/random sample from the population. Some of the most common are a  simple random sample , where you use a random numbers table or random number generator to select your sample from the population.

There are several examples of random number generators available online. See the following example of an online random number generator:  http://www.randomizer.org/ .

A  systematic random sample  takes every n-th number from the population, depending on how many people you would like to have in your sample. A  stratified random sample  does random sampling within groups, and a  multi-stage  or  cluster sample  is used when there are multiple groups within a large area and a large population, and the researcher does random sampling in stages.

If you are interested in understanding more about these types of probability/random samples, take a look at the following website: Probability Sampling .

However, many times communication researchers use whoever they can find to participate in their study, such as college students in their classes since these people are easily accessible. Many of the studies in interpersonal communication and relationship development, for example, used this type of sample. This is called a convenience sample. In doing so, they are using a non- probability or non-random sample. In these types of samples, each member of the population does not have an equal opportunity to be selected. For example, if you decide to ask your facebook friends to participate in an online survey you created about how college students in the U.S. use cell phones to text, you are using a non-random type of sample. You are unable to randomly sample the whole population in the U.S. of college students who text, so you attempt to find participants more conveniently. Some common non-random or non-probability samples are:

• accidental/convenience samples, such as the facebook example illustrates
• quota samples, in which you do convenience samples within subgroups of the population, such as biological sex, looking for a certain number of participants in each group being compared
• snowball or network sampling, where you ask current participants to send your survey onto their friends.

For more information on non-probability sampling, see the following website: Nonprobability Sampling .

Researchers, such as communication scholars, often use these types of samples because of the nature of their research. Most research designs used in communication are not true experiments, such as would be required in the medical field where they are trying to prove some cause-effect relationship to cure or alleviate symptoms of a disease. Most communication scholars recognize that human behavior in communication situations is much less predictable, so they do not adhere to the strictest possible worldview related to quantitative methods and are less concerned with having to use probability sampling.

They do recognize, however, that with either probability or non-probability sampling, there is still the possibility of bias and error, although much less with probability sampling. That is why all quantitative researchers, regardless of field, will report statistical significance levels if they are interested in generalizing from their sample to the population at large, to let the readers of their work know how confident they are in their results.

Size of Sample

The larger the sample, the more likely the sample is going to be representative of the population. If there is a lot of variability in the population (e.g., lots of different ethnic groups in the population), a researcher will need a larger sample. If you are interested in detecting small possible differences (e.g., in a close political race), you need a larger sample. However, the bigger your population, the less you have to increase the size of your sample in order to have an adequate sample, as is illustrated by an example sample size calculator such as can be found at  http://www.raosoft.com/samplesize.html .

Using the example sample size calculator, see how you might determine how large of a sample you might need in order to study how college students in the U.S. use texting on their cell phones. You would have to first determine approximately how many college students are in the U.S. According to ANEKI, there are a little over 14,000,000 college students in the U.S. ( Countries with the Most University Students ). When inputting that figure into the sample size calculator below (using no commas for the population size), you would need a sample size of approximately 385 students. If the population size was 20,000, you would need a sample of 377 students. If the population was only 2,000, you would need a sample of 323. For a population of 500, you would need a sample of 218.

It is not enough, however, to just have an adequate or large sample. If there is bias in the sampling, you can have a very bad large sample, one that also does not represent the population at large. So, having an unbiased sample is even more important than having a large sample.

So, what do you do, if you cannot reasonably conduct a probability or random sample? You run statistics which report significance levels, and you report the limitations of your sample in the discussion section of your paper/article.

Pilot Testing Methods

Now that we have talked about the different elements of your study design, you should try out your methods by doing a pilot test of some kind. This means that you try out your procedures with someone to try to catch any mistakes in your design before you start collecting data from actual participants in your study. This will save you time and money in the long run, along with unneeded angst over mistakes you made in your design during data collection. There are several ways you might do this.

You might ask an expert who knows about this topic (such as a faculty member) to try out your experiment or survey and provide feedback on what they think of your design. You might ask some participants who are like your potential sample to take your survey or be a part of your pilot test; then you could ask them which parts were confusing or needed revising. You might have potential participants explain to you what they think your questions mean, to see if they are interpreting them like you intended, or if you need to make some questions clearer.

The main thing is that you do not just assume your methods will work or are the best type of methods to use until you try them out with someone. As you write up your study, in your methods section of your paper, you can then talk about what you did to change your study based on the pilot study you did.

Institutional Review Board (IRB) Approval

The last step of your planning takes place when you take the necessary steps to get your study approved by your institution's review board. As you read in chapter 3, this step is important if you are planning on using the data or results from your study beyond just the requirements for your class project. See chapter 3 for more information on the procedures involved in this step.

Conclusion: Study Design Planning

Once you have decided what topic you want to study, you plan your study. Part 1 of this chapter has covered the following steps you need to follow in this planning process:

• decide what type of study you will do (i.e., experimental, quasi-experimental, non- experimental);
• decide on what data collection method you will use (i.e., survey, observation, or already existing data);
• operationalize your variables into measureable concepts;
• determine what type of sample you will use (probability or non-probability);
• pilot test your methods; and
• get IRB approval.

At that point, you are ready to commence collecting your data, which is the topic of the next section in this chapter.

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Quantitative Research Methods

What is quantitative research, about this guide, introduction, quantitative research methodologies.

• Key Resources
• Quantitative Software
• Finding Qualitative Studies

 The purpose of this guide is to provide a starting point for learning about quantitative research. In this guide, you'll find:

• Resources on diverse types of quantitative research.
• An overview of resources for data, methods & analysis
• Popular quantitative software options
• Information on how to find quantitative studies

Research involving the collection of data in numerical form for quantitative analysis. The numerical data can be durations, scores, counts of incidents, ratings, or scales. Quantitative data can be collected in either controlled or naturalistic environments, in laboratories or field studies, from special populations or from samples of the general population. The defining factor is that numbers result from the process, whether the initial data collection produced numerical values, or whether non-numerical values were subsequently converted to numbers as part of the analysis process, as in content analysis.

Citation: Garwood, J. (2006). Quantitative research. In V. Jupp (Ed.), The SAGE dictionary of social research methods. (pp. 251-252). London, England: SAGE Publications. doi:10.4135/9780857020116

Watch the following video to learn more about Quantitative Research:

(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database)

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y, and Z are also playing a role.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses.

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Quantitative Methods

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• First Online: 11 June 2021
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• Juwel Rana 2 , 3 , 4 ,
• Patricia Luna Gutierrez 5 &
• John C. Oldroyd 6  

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Quantitative analysis ; Quantitative research methods ; Study design

Quantitative method is the collection and analysis of numerical data to answer scientific research questions. Quantitative method is used to summarize, average, find patterns, make predictions, and test causal associations as well as generalizing results to wider populations. It allows us to quantify effect sizes, determine the strength of associations, rank priorities, and weigh the strength of evidence of effectiveness.

Introduction

This entry aims to introduce the most common ways to use numbers and statistics to describe variables, establish relationships among variables, and build numerical understanding of a topic. In general, the quantitative research process uses a deductive approach (Neuman 2014 ; Leavy 2017 ), extrapolating from a particular case to the general situation (Babones 2016 ).

In practical ways, quantitative methods are an approach to studying a research topic. In research, the...

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Babones S (2016) Interpretive quantitative methods for the social sciences. Sociology. https://doi.org/10.1177/0038038515583637

Balnaves M, Caputi P (2001) Introduction to quantitative research methods: an investigative approach. Sage, London

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Brenner PS (2020) Understanding survey methodology: sociological theory and applications. Springer, Boston

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Creswell JW (2014) Research design: qualitative, quantitative, and mixed methods approaches. Sage, London

Leavy P (2017) Research design. The Gilford Press, New York

Mertens W, Pugliese A, Recker J (2018) Quantitative data analysis, research methods: information, systems, and contexts: second edition. https://doi.org/10.1016/B978-0-08-102220-7.00018-2

Neuman LW (2014) Social research methods: qualitative and quantitative approaches. Pearson Education Limited, Edinburgh

Treiman DJ (2009) Quantitative data analysis: doing social research to test ideas. Jossey-Bass, San Francisco

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Department of Public Health, School of Health and Life Sciences, North South University, Dhaka, Bangladesh

Department of Biostatistics and Epidemiology, School of Health and Health Sciences, University of Massachusetts Amherst, MA, USA

Department of Research and Innovation, South Asia Institute for Social Transformation (SAIST), Dhaka, Bangladesh

Independent Researcher, Masatepe, Nicaragua

Patricia Luna Gutierrez

School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia

John C. Oldroyd

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Rana, J., Gutierrez, P.L., Oldroyd, J.C. (2021). Quantitative Methods. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-31816-5_460-1

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers. It uses sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research. It helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics. And hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants. It makes the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can explore a broader spectrum of variables and relationships within the data. It can enable a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including:

• The collection of reliable and accurate data
• Quick data collection
• A broader scope of data analysis
• The elimination of bias

These advantages makes it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Frequently Asked Questions (FAQs)

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Research methods--quantitative, qualitative, and more: overview.

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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

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Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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Quantitative and Qualitative Research

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What is Quantitative Research?

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Choosing the Right Research Methodology: A Guide for Researchers

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Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job. 

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

Need a helping hand?

Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon. 

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

Very useful and informative especially for beginners

Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.

I find the article very informative especially for my decitation it has been helpful and an eye opener.

Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I struggle so much with designs of the research for sure!

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

Thank you so much this site is such a life saver. How I wish 1-1 coaching is available in our country but sadly it’s not.

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Quantitative Methods
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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What Is Quantitative Research? Types, Characteristics & Methods

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Step into the fascinating world of quantitative research , where numbers reveal extraordinary insights!

By gathering and studying data in a systematic way, quantitative research empowers us to understand our ever-changing world better. It helps understand a problem or an already-formed hypothesis by generating numerical data. The results don’t end here, as you can process these numbers to get actionable insights that aid decision-making.

You can use quantitative research to quantify opinions, behaviors, attitudes, and other definitive variables related to the market, customers, competitors, etc. The research is conducted on a larger sample population to draw predictive, average, and pattern-based insights.

Here, we delve into the intricacies of this research methodology, exploring various quantitative methods, their advantages, and real-life examples that showcase their impact and relevance.

Ready to embark on a journey of discovery and knowledge? Let’s go!

What Is Quantitative Research?

Quantitative research is a method that uses numbers and statistics to test theories about customer attitudes and behaviors. It helps researchers gather and analyze data systematically to gain valuable insights and draw evidence-based conclusions about customer preferences and trends.

Researchers use online surveys, questionnaires , polls , and quizzes to question a large number of people to obtain measurable and bias-free data.

In technical terms, quantitative research is mainly concerned with discovering facts about social phenomena while assuming a fixed and measurable reality.

Offering numbers and stats-based insights, this research methodology is a crucial part of primary research and helps understand how well an organizational decision is going to work out.

Let’s consider an example.

Suppose your qualitative analysis shows that your customers are looking for social media-based customer support. In that case, quantitative analysis will help you see how many of your customers are looking for this support.

If 10% of your customers are looking for such a service, you might or might not consider offering this feature. But, if 40% of your regular customers are seeking support via social media, then it is something you just cannot overlook.

Characteristics of Quantitative Research

Quantitative research clarifies the fuzziness of research data from qualitative research analysis. With numerical insights, you can formulate a better and more profitable business decision.

Hence, quantitative research is more readily contestable, sharpens intelligent discussion, helps you see the rival hypotheses, and dynamically contributes to the research process.

Let us have a quick look at some of its characteristics.

1. Measurable Variables

The data collection methods in quantitative research are structured and contain items requiring measurable variables, such as age, number of family members, salary range, highest education, etc.

These structured data collection methods comprise polls, surveys, questionnaires, etc., and may have questions like the ones shown in the following image:

As you can see, all the variables are measurable. This ensures that the research is in-depth and provides less erroneous data for reliable, actionable insights.

2. Sample Size

No matter what data analysis methods for quantitative research are being used, the sample size is kept such that it represents the target market.

As the main aim of the research methodology is to get numerical insights, the sample size should be fairly large. Depending on the survey objective and scope, it might span hundreds of thousands of people.

3. Normal Population Distribution

To maintain the reliability of a quantitative research methodology, we assume that the population distribution curve is normal.

This type of population distribution curve is preferred over a non-normal distribution as the sample size is large, and the characteristics of the sample vary with its size.

This requires adhering to the random sampling principle to avoid the researcher’s bias in result interpretation. Any bias can ruin the fairness of the entire process and defeats the purpose of research.

4. Well-Structured Data Representation

Data analysis in quantitative research produces highly structured results and can form well-defined graphical representations. Some common examples include tables, figures, graphs, etc., that combine large blocks of data.

This way, you can discover hidden data trends, relationships, and differences among various measurable variables. This can help researchers understand the survey data and formulate actionable insights for decision-making.

5. Predictive Outcomes

Quantitative analysis of data can also be used for estimations and prediction outcomes. You can construct if-then scenarios and analyze the data for the identification of any upcoming trends or events.

However, this requires advanced analytics and involves complex mathematical computations. So, it is mostly done via quantitative research tools that come with advanced analytics capabilities.

Types of Quantitative Research Methods

Quantitative research is usually conducted using two methods. They are-

• Primary quantitative research methods
• Secondary quantitative research methods

1. Primary quantitative research methods

Primary quantitative research is the most popular way of conducting market research. The differentiating factor of this method is that the researcher relies on collecting data firsthand instead of relying on data collected from previous research.

There are multiple types of primary quantitative research. They can be distinguished based on three distinctive aspects, which are:

1.1. Techniques & Types of Studies:

• Survey Research

Surveys are the easiest, most common, and one of the most sought-after quantitative research techniques. The main aim of a survey is to widely gather and describe the characteristics of a target population or customers. Surveys are the foremost quantitative method preferred by both small and large organizations.

They help them understand their customers, products, and other brand offerings in a proper manner.

Surveys can be conducted using various methods, such as online polls, web-based surveys, paper questionnaires, phone calls, or face-to-face interviews. Survey research allows organizations to understand customer opinions, preferences, and behavior, making it crucial for market research and decision-making.

You can watch this quick video to learn more about creating surveys.

Watch: How to Create a Survey Using ProProfs Survey Maker

Surveys are of two types:

• Cross-Sectional Surveys Cross-sectional surveys are used to collect data from a sample of the target population at a specific point in time. Researchers evaluate various variables simultaneously to understand the relationships and patterns within the data.
• Cross-sectional surveys are popular in retail, small and medium-sized enterprises (SMEs), and healthcare industries, where they assess customer satisfaction, market trends, and product feedback.
• Longitudinal Surveys Longitudinal surveys are conducted over an extended period, observing changes in respondent behavior and thought processes.
• Researchers gather data from the same sample multiple times, enabling them to study trends and developments over time. These surveys are valuable in fields such as medicine, applied sciences, and market trend analysis.

Surveys can be distributed via various channels. Some of the most popular ones are listed below:

• Email: Sending surveys via email is a popular and effective method. People recognize your brand, leading to a higher response rate. With ProProfs Survey Maker’s in-mail survey-filling feature, you can easily send out and collect survey responses.
• Embed on a website: Boost your response rate by embedding the survey on your website. When visitors are already engaged with your brand, they are more likely to take the survey.
• Social media: Take advantage of social media platforms to distribute your survey. People familiar with your brand are likely to respond, increasing your response numbers.
• QR codes: QR codes store your survey’s URL, and you can print or publish these codes in magazines, signs, business cards, or any object to make it easy for people to access your survey.
• SMS survey: Collect a high number of responses quickly with SMS surveys. It’s a time-effective way to reach your target audience.

1.2. Correlational Research:

Correlational research aims to establish relationships between two or more variables.

Researchers use statistical analysis to identify patterns and trends in the data, but it does not determine causality between the variables. This method helps understand how changes in one variable may impact another.

Examples of correlational research questions include studying the relationship between stress and depression, fame and money, or classroom activities and student performance.

1.3. Causal-Comparative Research:

Causal-comparative research, also known as quasi-experimental research, seeks to determine cause-and-effect relationships between variables.

Researchers analyze how an independent variable influences a dependent variable, but they do not manipulate the independent variable. Instead, they observe and compare different groups to draw conclusions.

Causal-comparative research is useful in situations where it’s not ethical or feasible to conduct true experiments.

Examples of questions for this type of research include analyzing the effect of training programs on employee performance, studying the influence of customer support on client retention, investigating the impact of supply chain efficiency on cost reduction, etc.

1.4. Experimental Research:

Experimental research is based on testing theories to validate or disprove them. Researchers conduct experiments and manipulate variables to observe their impact on the outcomes.

This type of research is prevalent in natural and social sciences, and it is a powerful method to establish cause-and-effect relationships. By randomly assigning participants to experimental and control groups, researchers can draw more confident conclusions.

Examples of experimental research include studying the effectiveness of a new drug, the impact of teaching methods on student performance, or the outcomes of a marketing campaign.

2. Data collection methodologies

After defining research objectives, the next significant step in primary quantitative research is data collection. This involves using two main methods: sampling and conducting surveys or polls.

2.1Sampling methods:

In quantitative research, there are two primary sampling methods: Probability and Non-probability sampling.

2.2Probability Sampling

In probability sampling, researchers use the concept of probability to create samples from a population. This method ensures that every individual in the target audience has an equal chance of being selected for the sample.

There are four main types of probability sampling:

• Simple random sampling: Here, the elements or participants of a sample are selected randomly, and this technique is used in studies that are conducted over considerably large audiences. It works well for large target populations.
• Stratified random sampling: In this method, the entire population is divided into strata or groups, and the sample members get chosen randomly from these strata only. It is always ensured that different segregated strata do not overlap with each other.
• Cluster sampling: Here, researchers divide the population into clusters, often based on geography or demographics. Then, random clusters are selected for the sample.
• Systematic sampling: In this method, only the starting point of the sample is randomly chosen. All the other participants are chosen using a fixed interval. Researchers calculate this interval by dividing the size of the study population by the target sample size.

2.3Non-probability Sampling

Non-probability sampling is a method where the researcher’s knowledge and experience guide the selection of samples. This approach doesn’t give all members of the target population an equal chance of being included in the sample.

There are five non-probability sampling models:

• Convenience sampling: The elements or participants are chosen on the basis of their nearness to the researcher. The people in close proximity can be studied and analyzed easily and quickly, as there is no other selection criterion involved. Researchers simply choose samples based on what is most convenient for them.
• Consecutive sampling: Similar to convenience sampling, researchers select samples one after another over a significant period. They can opt for a single participant or a group of samples to conduct quantitative research in a consecutive manner for a significant period of time. Once this is over, they can conduct the research from the start.
• Quota sampling: With quota sampling, researchers use their understanding of target traits and personalities to form groups (strata). They then choose samples from each stratum based on their own judgment.
• Snowball sampling: This method is used where the target audiences are difficult to contact and interviewed for data collection. Researchers start with a few participants and then ask them to refer others, creating a snowball effect.
• Judgmental sampling: In judgmental sampling, researchers rely solely on their experience and research skills to handpick samples that they believe will be most relevant to the study.

3. Data analysis techniques

To analyze the quantitative data accurately, you’ll need to use specific statistical methods such as:

• SWOT Analysis: This stands for Strengths, Weaknesses, Opportunities, and Threats analysis. Organizations use SWOT analysis to evaluate their performance internally and externally. It helps develop effective improvement strategies.
• Conjoint Analysis: This market research method uncovers how individuals make complex purchasing decisions. It involves considering trade-offs in their daily activities when choosing from a list of product/service options.
• Cross-tabulation: A preliminary statistical market analysis method that reveals relationships, patterns, and trends within various research study parameters.
• TURF Analysis: Short for Totally Unduplicated Reach and Frequency Analysis, this method helps analyze the reach and frequency of favorable communication sources. It provides insights into the potential of a target market.
• By using these statistical techniques and inferential statistics methods like confidence intervals and margin of error, you can draw meaningful insights from your primary quantitative research that you can use in making informed decisions.

2. Secondary Quantitative Research Methods

• Secondary quantitative research, also known as desk research, is a valuable method that uses existing data, called secondary data.
• Instead of collecting new data, researchers analyze and combine already available information to enhance their research. This approach involves gathering quantitative data from various sources such as the internet, government databases, libraries, and research reports.
• Secondary quantitative research plays a crucial role in validating data collected through primary quantitative research. It helps reinforce or challenge existing findings.

Here are five commonly used secondary quantitative research methods:

A. Data Available on the Internet:

The Internet has become a vast repository of data, making it easier for researchers to access a wealth of information. Online databases, websites, and research repositories provide valuable quantitative data for researchers to analyze and validate their primary research findings.

B. Government and Non-Government Sources:

Government agencies and non-government organizations often conduct extensive research and publish reports. These reports cover a wide range of topics, providing researchers with reliable and comprehensive data for quantitative analysis.

C. Public Libraries:

While less commonly used in the digital age, public libraries still hold valuable research reports, historical data, and publications that can contribute to quantitative research.

D. Educational Institutions:

Educational institutions frequently conduct research on various subjects. Their research reports and publications can serve as valuable sources of information for researchers, validating and supporting primary quantitative research outcomes.

E. Commercial Information Sources:

Commercial sources such as local newspapers, journals, magazines, and media outlets often publish relevant data on economic trends, market research, and demographic analyses. Researchers can access this data to supplement their own findings and draw better conclusions.

Advantages of Quantitative Research Methods

Quantitative research data is often standardized and can be easily used to generalize findings for making crucial business decisions and uncover insights to supplement the qualitative research findings.

Here are some core benefits this research methodology offers.

Direct Result Comparison

As the studies can be replicated for different cultural settings and different times, even with different groups of participants, they tend to be extremely useful. Researchers can compare the results of different studies in a statistical manner and arrive at comprehensive conclusions for a broader understanding.

Replication

Researchers can repeat the study by using standardized data collection protocols over well-structured data sets. They can also apply tangible definitions of abstract concepts to arrive at different conclusions for similar research objectives with minor variations.

Large Samples

As the research data comes from large samples, the researchers can process and analyze the data via highly reliable and consistent analysis procedures. They can arrive at well-defined conclusions that can be used to make the primary research more thorough and reliable.

Hypothesis Testing

This research methodology follows standardized and established hypothesis testing procedures. So, you have to be careful while reporting and analyzing your research data , and the overall quality of results gets improved.

Proven Examples of Quantitative Research Methods

Below, we discuss two excellent examples of quantitative research methods that were used by highly distinguished business and consulting organizations. Both examples show how different types of analysis can be performed with qualitative approaches and how the analysis is done once the data is collected.

1. STEP Project Global Consortium / KPMG 2019 Global Family Business survey

This research utilized quantitative methods to identify ways that kept the family businesses sustainably profitable with time.

The study also identified the ways in which the family business behavior changed with demographic changes and had “why” and “how” questions. Their qualitative research methods allowed the KPMG team to dig deeper into the mindsets and perspectives of the business owners and uncover unexpected research avenues as well.

It was a joint effort in which STEP Project Global Consortium collected 26 cases, and KPMG collected 11 cases.

The research reached the stage of data analysis in 2020, and the analysis process spanned over 4 stages.

The results, which were also the reasons why family businesses tend to lose their strength with time, were found to be:

• Family governance
• Family business legacy

2. EY Seren Teams Research 2020

This is yet another commendable example of qualitative research where the EY Seren Team digs into the unexplored depths of human behavior and how it affected their brand or service expectations.

The research was done across 200+ sources and involved in-depth virtual interviews with people in their homes, exploring their current needs and wishes. It also involved diary studies across the entire UK customer base to analyze human behavior changes and patterns.

The study also included interviews with professionals and design leaders from a wide range of industries to explore how COVID-19 transformed their industries. Finally, quantitative surveys were conducted to gain insights into the EY community after every 15 days.

The insights and results were:

• A culture of fear, daily resilience, and hopes for a better world and a better life – these were the macro trends.
• People felt massive digitization to be a resourceful yet demanding aspect as they have to adapt every day.
• Some people wished to have a new world with lots of possibilities, and some were looking for a new purpose.

8 Best Practices to Conduct Quantitative Research

Here are some best practices to keep in mind while conducting quantitative research:

1. Define Research Objectives

There can be many ways to collect data via quantitative research methods that are chosen as per the research objective and scope. These methods allow you to build your own observations regarding any hypotheses – unknown, entirely new, or unexplained. 

You can hypothesize a proof and build a prediction of outcomes supporting the same. You can also create a detailed stepwise plan for data collection, analysis, and testing. 

Below, we explore quantitative research methods and discuss some examples to enhance your understanding of them.

2. Keep Your Questions Simple

The surveys are meant to reach people en-masse, and that includes a wide demographic range with recipients from all walks of life. Asking simple questions will ensure that they grasp what’s being asked easily.

3. Develop a Solid Research Design

Choose an appropriate research design that aligns with your objectives, whether it’s experimental, quasi-experimental, or correlational. You also need to pay attention to the sample size and sampling technique such that it represents the target population accurately.

4. Use Reliable & Valid Instruments

It’s crucial to select or develop measurement instruments such as questionnaires, scales, or tests that have been validated and are reliable. Before proceeding with the main study, pilot-test these instruments on a small sample to assess their effectiveness and make any necessary improvements.

5. Ensure Data Quality

Implement data collection protocols to minimize errors and bias during data gathering. Double-check data entries and cleaning procedures to eliminate any inconsistencies or missing values that may affect the accuracy of your results. For instance, you might regularly cross-verify data entries to identify and correct any discrepancies.

6. Employ Appropriate Data Analysis Techniques

Select statistical methods that match the nature of your data and research questions. Whether it’s regression analysis, t-tests, ANOVA, or other techniques, using the right approach is important for drawing meaningful conclusions. Utilize software tools like SPSS or R for data analysis to ensure the accuracy and reproducibility of your findings.

7. Interpret Results Objectively

Present your findings in a clear and unbiased manner. Avoid making unwarranted causal claims, especially in correlational studies. Instead, focus on describing the relationships and patterns observed in your data.

8. Address Ethical Considerations

Prioritize ethical considerations throughout your research process. Obtain informed consent from participants, ensuring their voluntary participation and confidentiality of data. Comply with ethical guidelines and gain approval from a governing body if necessary.

Enhance Your Quantitative Research With Cutting-Edge Software

While no single research methodology can produce 100% reliable results, you can always opt for a hybrid research method by opting for the methods that are most relevant to your objective.

This understanding comes gradually as you learn how to implement the correct combination of qualitative and quantitative research methods for your research projects. For the best results, we recommend investing in smart, efficient, and scalable research tools that come with delightful reporting and advanced analytics to make every research initiative a success.

These software tools, such as ProProfs Survey Maker, come with pre-built survey templates and question libraries and allow you to create a high-converting survey in just a few minutes.

So, choose the best research partner, create the right research plan, and gather insights that drive sustainable growth for your business.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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Quantitative Methodology: Measurement and Statistics, P.B.C.

• Spring 

June 30, 2025

The Quantitative Methodology: Measurement and Statistics—Post-Baccalaureate Certificate program is designed for doctoral students enrolled at the University of Maryland who seek specialized training in quantitative methods to complement their primary area of study. This highly ranked certificate program equips you with essential skills and knowledge in advanced statistical analysis, providing a valuable quantitative specialization applicable to many academic and professional pursuits.

Key Features

• Customized Curriculum : Tailor your course selection to align with your research interests and career goals in consultation with your assigned QMMS certificate advisor.
• Core Course Foundation : Build a strong quantitative foundation through completion of four common core courses, covering applied measurement, general linear models, causal inference, and evaluation methods.
• Interdisciplinary Opportunities : Collaborate with faculty and students from diverse academic backgrounds, enriching your learning experience and broadening your perspective on quantitative research methodologies.
• Demonstrate proficiency in the application of general and generalized linear models for analyzing data across various research contexts.
• Understand and critically evaluate different research designs, ensuring the selection of appropriate methodologies to address research questions effectively.
• Gain a solid understanding of the fundamentals of measurement, including reliability, validity, and scale construction, to inform data collection and interpretation.
• Acquire practical skills in utilizing data analytics software packages to conduct statistical analysis and interpret research findings accurately.

If you are a prospective student who has questions about this program, please contact a QMMS faculty member or Dr. Gregory R. Hancock, [email protected]

Complete the Enrollment Form

Per University policy , you cannot graduate and obtain a Ph.D. before completing the QMMS certificate. However, they can be completed within the same semester.

Yi Feng, student, Quantitative Methodology: Measurement and Statistics  

Courses in this certificate are carefully selected. Your specific program of study will be structured to take into account your background and goals.

QMMS Graduate Student Handbook Program of Study The certificate requires completion of 21 credits, including the following four common core courses:

• QMMS 623 Applied Measurement: Issues and Practices (3)
• QMMS 646 General Linear Models I (3)
• QMMS 647 Causal Inference and Evaluation Methods (3)
• QMMS 651 General Linear Models II (3)

The courses to be taken for the remaining nine credits should be chosen in consultation with your assigned QMMS certificate advisor.

You must maintain a grade point average of at least 3.5 in courses taken for the certificate. Also, you must complete each course in the certificate with a grade of B or better. If a lower grade is obtained in a given course, then the course in question must be repeated until this grade requirement is achieved or not counted in the certificate and another course substituted.

Sep 17 Graduate Fair Expo Sep 17, 2024 4:00 – 6:00 pm

Home » Blog » Comprehensive Guide to Research Methodology – Design | Methods | Best Practices

Comprehensive Guide to Research Methodology – Design | Methods | Best Practices

• Other Laws | Blog |
• 19 Min Read
• Last Updated on 16 September, 2024

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Table of Contents

• Introduction
• Steps in Research Process
• Classification of Research Design

1. Introduction

This article describes the research process and different research designs in detail. Management and social science research, like other forms of scientific inquiry, require a structured sequence of highly interrelated steps (Zigmund et al., 2010). The research process involves a series of steps or actions essential for the smooth conduct of any research. The figure below illustrates the sequence of the research process. It is to be noted that these steps are not a road map to all kinds of research. Basically, it is applicable for deductive or functionalist research, and it can or needs to be revised to suit the requirements of a specific project. The research process doesn’t need to be followed successively; rather, the steps overlap frequently and are interrelated. The research process offers a comprehensive guideline that can be referred to for any management and social science research. It may happen that later stages can be accomplished before the earlier stages.

The steps involved in the research process are neither mutually exclusive nor separate and distinct. The selection of a research topic at the outset, defining the research problem and objectives, influences the selection of a sample and data collection. The sample selection may affect the design of questionnaire items. For example, suppose an organization wants to know the cause of attrition among lower-category employees with low educational qualifications. In that case, the wording for the questionnaire will be easier than for people in top management positions with professional educational qualifications. The steps may differ based on the objectives of the research. However, research based on deductive logic should follow the steps outlined below:

2. Steps in Research Process

• Problem Identification
• Literature Review
• Formulating Research Questions
• Research Design
• Data Collection
• Data Analysis
• Conclusions and Report Writing.

The quest for research must always be triggered by the longing to explore and gain more knowledge and understanding. The management dilemma encourages the need for a decision. The need may arise owing to the cause that the researchers want to discover or reestablish certain relationships. The orientation might be to solve immediate management issues, discover something new, or have purely academic intentions. For instance, in an organization, the manager may want to know the reason for high attrition and lack of job satisfaction, or a retail store may survey the post-purchase satisfaction among the customers.

2.1 Research Problem Identification

Defining the research problem is the first step in the research process. The researchers get the proper direction to conduct their research by first understanding the research problems. Hence, a well-defined research problem is crucial. When the problem is discovered, researchers and management can take further steps to define the problem clearly and precisely. A problem defined with accuracy and conscience helps the researchers utilize the available resources effectively. It is imperative for researchers to explore what exactly is the problem and what are the objectives of the research. The rule generally followed to define the research problem is that the definition should permit the researchers to acquire all details required to address the managerial issues and show guidelines for finding a solution. The researcher should be careful not to define the problem too broadly or narrowly. Examples of broad managerial problems are defining a strategy for enhancing organizational performance and a strategy to elevate the organization’s brand equity. An example of a narrow definition of a problem is how to match competitors’ recruitment strategies. To overcome the possibility of both errors while defining the research problem, the researchers must define the problem with broad, popular terms and devise its components. The broad general statement helps the researchers get a sound perspective on the research problem and avoid the error of defining the problem narrowly. On the other side, the specific component helps to identify the key aspects of the research problem, extend a transparent guideline to proceed further and avoid the error of defining the problem too broadly. In management and social science research, broad management problems need to be converted to information-oriented research problems that focus more on the cause than the symptoms. Some examples of managerial problems converted to research problems are presented in Table below. The conversion of management dilemma to managerial questions and further to research questions can be carried out through exploratory research. Such research incorporates an examination of past research studies, a review of extant literature and organizational records and interviewing experts (Cooper et al., 2016).

2.2 Literature Review

Exploring the existing literature is critical in the research process. Researchers must explore and investigate extant literature to observe whether other researchers have already addressed the identified research problem. A literature review is a systematic search of published work, including periodicals, books, journal papers (conceptual and empirical), and reports, representing theory and empirical work about the research problem and topic at hand. A survey of existing literature is customary in applied research and is an elementary requirement of a basic research report. The internet, electronic databases, websites, and e-library help the researcher to carry out literature surveys systematically and easily.

The literature review aims to study the existing state of knowledge in the domain of interest, to picture key authors, theories, methods, topics, and findings in that domain, and to explore the gaps in knowledge in that domain. A literature review conducted systematically reveals whether initial research questions have already gained substantial attention in the extant literature, whether more interesting newer research questions are available, whether past studies have consistent findings or contradictions exist, flaws in the body of research that the researchers can address, and whether the initial research questions need to be revised as per the findings of the literature review. Furthermore, the review can answer the proposed research questions and help identify theories used in previous studies to address similar research questions. For example, for an organization interested in determining the true cause of turnover, the researcher will study extensively the existing literature on attrition and its causes. By studying relevant journal articles, books, and book chapters, the researcher will discover the causes of attrition in general, find out the existing gaps, and suggest the management carry forward the research to find causes specific to the organization.

As deductive research primarily involves theory testing, the researchers must identify one or more theories that can illuminate the proposed research questions. Through an extensive literature review, researchers may uncover various concepts and constructs related to the phenomenon of interest. A theory will extend support to constructs/variables that are logically relevant to the chosen phenomenon. In the deductive approach, researchers use theory/theories as the logical basis for hypothesis testing. However, researchers must carefully select the theories appropriate for the identified problem to be studied. The hypotheses need to be logically formulated and connected to the research objectives.

2.3 Formulating Research Questions

After problem identification and clarification, with or without an exploratory research approach, the researchers should derive the research objectives. Cautious attention to problem definition helps the researchers devise proper research objectives. Research objectives are the goal to be achieved through research. The research objective drives the research process further. A well-devised research objective enhances the possibility of gathering, relevant information and avoiding unwanted information. The research objectives can be properly developed with the consensus of the researchers and management on the actual managerial and business problems. The researcher should ensure that the research objectives are clearly stated, appropriate, and will yield germane information. The research objective may involve exploring the likelihood of venturing into a new market or may necessitate examining the effect of a new organizational policy on employee performance. The nature and types of objectives lead to choosing an appropriate research design.

Research Objectives:  Research objectives represent the goal of the research the researchers want to accomplish.

2.3.1 Suitable Research Questions

Research questions are important to conduct effective research. Without a clear research question, the researcher may face the risk of unfocused research and will not be sure of what the research is about. Research questions are refined descriptions of the components of the research problem. These are questions related to behavior, events or phenomena of interest that the researchers search for answers in their research. Examples include what factors motivate the employees in an organization to apply the gained knowledge back to their jobs or what needs to be done to enhance the creativity of school-going students. Research questions can best state the objectives of the research. Each component of the research problem needs to be broken down into sub-parts or research questions. Research questions inquire about the information essential concerning the problem components. Properly answered research questions will lead to effective decision-making. While formulating research questions, researchers should be guided by the problem statement, theoretical background, and analytical framework.

Sources of Research Questions

• Extant Literature
• Personal experience
• Societal issues
• Managerial problems
• New theories
• Technological advancement
• Empirical cases
• Contradictory finding

2.3.1.1 Significance of Research Questions

Research questions are critical because they guide scientific and systematic literature search, the decision about appropriate research design, the decision about data collection and target audience, data analysis, selection of right tools and techniques and overall to move in the right direction.

The researcher can utilize different sources for formulating research questions, such as extant literature, personal experience, societal issues, managerial problems, new theories, technological advancement, and contradictory findings. The research question must portray certain attributes. Research questions in quantitative research are more specific compared to qualitative research. Sometimes, some qualitative research follows an open approach without any research questions. The main steps involved in formulating research questions are illustrated in Figure below.

Criteria of Effective Research Questions

• Rateability
• Systematic and logical
• Significant
• Fascinating
• Logical association among variables

The sequence in selecting research questions suggests that the researchers are engrossed in a process of progressive focusing down when developing the research questions. It helps them to slide down from the general research area to research questions. While formulating the research questions, the researchers should understand that ending a research question with a question mark is essential. Without a question mark, a statement cannot be considered as a research question. It is quite possible that the researchers may not get answers to all research questions. The research questions need to be related to each other.

2.4 Planning the Research Design

After formulating research problems and literature surveys, the next stage in the research process is to develop the research design. Research design is the blueprint of research activities to answer research questions. It is a master plan that includes research methods and procedures for gathering and analyzing the relevant information with minimum cost, time, and effort. A research design extends a plan for carrying out the research. The researchers need to decide the source to collect information, the techniques of research design (survey or experiment), sampling techniques, and the cost and schedule of the research. The success of these objectives depends on the purpose of the research. Usually, research purposes are segregated into four types: exploration, description, diagnosis, and experimentation.

There are varied designs, such as experimental or non-experimental hypotheses testing (details of different research designs are outlined in section 2.3 in this chapter). There are four primary research methods for descriptive and causal research: survey, experiments, secondary data, and observations. The selection of an appropriate research method relies on the research objectives, available data sources, the cost and effort of collecting data, and the importance of managerial decisions. If the research objective is exploration, a flexible research design can extend better opportunities to investigate different aspects of the research problem. On the other hand, if the intention is simply to describe any situation or phenomena of interest to examine the relationship between two or more variables, the appropriate design should prioritize minimizing bias and maximizing reliability in data collection and analysis. For example, suppose a researcher wants to conduct exploratory research to know the different types of arthritis common in India. In that case, it may require a flexible design relying on secondary data from hospital records or discussions with doctors or other experts to reach conclusions. However, to invent COVID-vaccination and medicine for the COVID-19 virus, the researchers conducted varied experiments to reach a conclusion.

2.4.1 Hypotheses Development

Exploratory research helps the researchers define the research questions, key variables, and theoretical underpinnings and formulate hypotheses if required in the research. The hypotheses must be logically derived based on the research questions and linked to research objectives. A hypothesis is a tentative proposition regarding a research phenomenon. It may be a tentative statement that indicates an association between two or more variables, guided by any supportive theory, theoretical framework, or analytical model. It is a viable answer to the research questions framed by the researchers. Hypotheses are statements of relationships or propositions that are declarative and can be tested with empirical data. Some examples are:

H 1 : Training influences organizational performance.

H 2 : Training enhances employee performance.

For two more research questions i.e., “to what extent does brand love determine purchase intention?” and “does age and family background moderate the relationship?”, the hypotheses are:

H 1 : Brand love is related to purchase intention.

H 2 : Age and Family status moderate the association between brand love and purchase intention. Figure below provides a pictorial representation of the hypotheses drawn.

However, it is not always feasible for researchers to formulate hypotheses in all situations. Sometimes, researchers may lack all relevant information, and theoretical support may not be available to formulate the hypotheses.

2.5 Sampling Design

This stage of the research process involves an investigation of the population under study. A complete investigation of the population under study is known as a census inquiry. Usually, in census investigation, all units or items of the population are studied with high accuracy and reliability. However, it is usually not practicable and feasible for the researchers to study the entire population. Researchers usually prefer to investigate small, representative subgroups from the population known as sample. The procedure to select the sub-groups/samples is called sampling design. Sampling entails the process of drawing conclusions based on a subgroup of the population. Hence, the sample is a subset of the population. The first question that needs to be addressed in sampling is “who is to be included in the sample?” and this requires the identification of the target population under study. It is difficult for the researcher to define the population and sampling unit. For example, if a researcher wants to investigate the financial savings and vehicle loan association survey. In that case, individuals with existing accounts will be taken, and this sample unit represents the existing customers and not the potential customers. Hence, it is critical in sampling design to determine the specific target population.

Secondly, the issue that concerns the researchers in sampling design is selecting an appropriate sample size, and the third concern is selecting the sampling units. Researchers need to address these concerns to justify the research. Samples can be selected either using probability sampling techniques or non-probability sampling techniques. There are four types of probability sampling such as simple random, systematic, stratified, and cluster sampling. Non-probability sampling includes convenience, judgmental, quota, and snowball sampling. Depending on the objective, researchers should select the appropriate sampling techniques for their study.

2.6 Fieldwork and Gathering Data

After the formalization of the sampling plan, the fieldwork and data-gathering stage begins. The researcher gathers data after finalizing what to research, among whom, and which method to use. Data gathering involves the process of information collection. Different data collection instruments are available for researchers to collect information or data. Broadly, there are two ways to collect data, such as primary and secondary data collection methods. Primary data include data collected firsthand and are original. Varied methods are available for primary data collection, such as structured and unstructured interviews, focused group discussion, observation, and survey using a structured questionnaire. The data can be collected offline or online. Secondary data included information collected from published or unpublished sources that were already available. Some secondary data collection sources are articles, magazines, company records, expert opinion survey data, feedback of customers, government data, and past research on the subject. For example, to conduct a survey of job satisfaction in an organization, the researcher may circulate a printed questionnaire offline or mail the questionnaire to the selected respondents following an appropriate sampling technique.

Another example could be a study that investigates the purchase preference for luxury cars, and the base model demands primary and empirical information. However, another study that intended to describe the financial investment behavior of existing customers will use secondary data. At this stage, the researchers need to ensure the reliability and validity of the data obtained for the study.

2.7 Data Processing and Analysis

After data gathering, the data needs to be converted or properly coded to answer the research question under study. The information gathered in the data collection phase should be mined from the primary raw data. Data processing starts with data editing, coding, and tabulation. First, it is vital for the researchers to check the data collection forms for missing data, clarity, and consistency in categorization. The editing process involves problems associated with data, such as respondents’ response errors. Editing improves the quality of the data and makes the data usable for tabulation, analysis, and interpretation. Tabulation is a technical process in which classified data are presented in tables. Researchers use computers to feed data to a computer spreadsheet for data analysis. The preparation of a spreadsheet also requires lots of expertise and experience.

After coding the data, the next step is to analyze the data. Data analysis is the utilization of reasoning to make sense of data gathered. Ample statistical techniques are available for the researchers to analyze the data. Based on the research questions, objectives, study types, sampling framework used, data types, and degree of accuracy involved in the research, one can choose from parametric or non-parametric techniques for data analysis. Researchers may adopt univariate, bi-variate or multi-variate methods for data analysis. The analysis may include simple frequency analysis, multiple regression, or structural equation modeling. Different techniques are available for qualitative data, presented in Part 3 of this book.

2.8 Drawing Conclusion and Preparing a Report

After data analysis, the final stage in the research process is the interpretation of the results. The researcher requires analytical skills to interpret the statistical results, link the output with the research objectives, and state the implications of the result.

Research Design:  Research design is the blueprint/systematic steps to carry out research smoothly

Finally, researchers must communicate the result in the form of a report. The preparation of the final report needs to be done with the utmost care. The final report should include the identified research questions, research approach, data collection method, data analysis techniques, study findings, and implications for theory and practice. The structure of the report will be discussed in the last section of this book. The report should be prepared comprehensively to be usable by management or organizations for decision-making.

3. Classification of Research Design

This section highlights the classification of research design. As mentioned in the previous section, research design is the framework for carrying out management and other research. After the identification of a problem, the researchers formulate the research design. A good research design ensures the effectiveness of the research work. The choice of selecting an appropriate design relies on the research objectives. The broad categorization of research design with sub-categorization is detailed in various sub-sections.

3.1 Exploratory Research Design

Methods to Conduct Exploratory Research

• Literature survey
• Secondary sources of data
• Experience survey
• Focused group discussions
• Observations
• Structured and unstructured interviews
• Pilot surveys
• Case Studies

Exploratory research design is the simplest form of research design. The researchers explore the true nature of the problem. When researchers aim to study a new area or examine a new interest, exploratory design is a good option. This research design is flexible and versatile in approach. The information required by the researchers is defined loosely and unstructured. Researchers carrying out qualitative research usually adopt exploratory research design. Exploratory research design serves three purposes (a) it helps the researchers to address their inquisitiveness and quest for better understanding (b) to assess the practicality of carrying out border research (c) and devise methods for further studies.

Methods to Conduct Descriptive Research

• Self-administered survey
• Phone survey
• Mail survey/online survey
• Observation
• Personal interview
• Telephone interview

Exploratory research design has paramount significance in management and social science research. They are crucial for researchers who want to study something new. To cite an example, during the COVID-19 pandemic, physical health, mental health, and safety of school and college-going children were a concern for most people. The online education system was the new normal at that time. Research studying the impact of digitalization, long time spent in online studies on students’ health and mental well-being during the COVID-19 pandemic, is of an exploratory kind. One of the disadvantages of exploratory research design is that researchers rarely get specific answers to the research questions.

3.2 Descriptive Research Design

The prime objective of descriptive research design is to describe certain situations or events. This type of design provides an extensive explanation of the research phenomena under study. In descriptive research, the researchers possess prior knowledge about the problem situations. The information is defined with clarity. This kind of research is preplanned and more structured than exploratory research. Researchers must formulate research questions properly and have clarity regarding the types of data needed and the procedure to be followed to achieve the research objectives. Researchers have the luxury of covering a large representative sample. Researchers must answer five Ws and one H – what, who, when, where, why, and how of research issues. What kind of information is required for the research, who are the target respondents, when the information will be collected, where to interact with the respondents, why information is collected from the respondents and how to collect data from the respondents. Descriptive research studies can be cross-sectional or longitudinal. The major objectives for the following descriptive research are given below.

• To explain the characteristics of certain groups such as the Indian population, employees, students, marketing personnel, organizations, sales persons. For example, a university to design a customized online higher studies course for working professionals needs a holistic profile of the interested population.
• To evaluate the portion of individuals in a specific population portraying a typical behavior. For instance, when a researcher is inclined to know the percentage of employees not interested in an online platform introduced for them in their organization.
• To predict for future. For instance, to know the future of physical retail stores due to the widespread expansion of online stores.
• To examine the extent to which management research variables relate to each other. For example, to what extent does work-life balance, salary, and conducive work environment enhance employee job satisfaction?

3.3 Causal Research Design

Usually, causal research design is adopted by researchers to explain causal relationships among phenomena under study. Causal research examines cause-and-effect relationships among variables. Causal research has certain criteria, as already discussed in Chapter 1. Causal research follows a planned and structured design like descriptive research. Though the magnitude of the relationship among variables is examined in descriptive research, the causal association cannot be explained through such research. Experimentation is one of the methods for carrying out causal research.

In causal research, the researchers usually examine the impact of one variable on another. The researchers try to explore the cause-and-effect relationship (nomothetic explanation). How can the researcher know whether cause and effect are associated? There are three criteria for a nomothetic causal relationship when (1) two or more variables are correlated, (2) the cause precedes the effect and (3) the absence of a plausible alternative explanation for the effect other than the proposed cause (Babbie, 2020). First, without establishing a correlation among two or more variables, causation cannot exist. Second, the cause should happen before the effect in time. For instance, it is more sensible to say that children’s religious affiliation is caused by their parents than to reflect that parents’ religious affiliation is due to children; even in some cases, it is plausible that children may convert to other religions later with their parent’s permission. The third significant condition for a causal relationship is that the effect cannot be attributed to any external third variable for establishing causation.

To cite one classic example, there is a causal association between sales of ice cream and death owing to drowning. Intake of more ice creams in summer does lead to a higher death rate due to drowning. The third intervening variable that causes higher death is season or temperature. In summer, higher deaths occur due to swimming and not because of taking ice-creams. The intervening variable season or temperature causes a higher death rate.

To establish a reliable causal relationship among two or more variables, other influencing variables must be controlled to neutralize their impact on the studied variables. For example, to study the effect of factors influencing training transfer in soft skill training, the other intervening variables such as age, gender, and educational qualification need to be controlled. This kind of research sometimes demands experimentation to establish causality. In most cases, causal research is quantitative and needs statistical hypothesis testing.

3.4 Experimental Research Design

Experimental research aims to examine the cause-effect relationship in a controlled setting by isolating the cause from the effect in time. The three criteria suggested by John Stuart Mill mirror in experimental research. In experimental research, the cause is administered to one group of subjects, known as the treatment group and not to the control group, and the researchers observe the difference in mean effect among the subjects of both groups. Whether variation in the cause is connected to variation in effect is observed. To be more specific, the researcher manipulates the independent variable and examines the change in the dependent variable, keeping other variables constant. Researchers used varied methods during the experiments to reduce the plausible effect of other explanations for the effect, along with ancillary methods to investigate the plausibility of those that cannot be ruled out. It is vital in experimental studies to control the extraneous and confounding variables while carrying out the experiments. Ignorance of such variables may lead to spurious relationships among studied variables. However, bringing many of the variables under experimental control is impossible. For example, personal characteristics of the subject like age, sex, intelligence, beliefs and persona. In such cases, the researchers must observe natural variations in the variables of concern. Then, statistical procedures are used to rule out the plausible impact of uncontrolled factors.

Experimental Research Design:  An experiment is a method of collecting evidence to indicate the effect of one variable on another.

Experimental research design can be conducted in a laboratory setting (laboratory experiment) or in a field setting (field experiments) where the phenomena of research interest happen. As an example, one of the most talked about and controversial experiments conducted on understanding human behavior has been the Stanford Prison Experiments, which took place at Stanford University in 1971. The experiments were funded by the US Office of Naval Research, and the principal investigator for the same was Prof Phillip Zimbardo. The major purpose of these experiments was to understand how norms develop and social expectations about roles shape group behavior. Experimental studies are segregated into four categories such as pre-experimental, true-experimental, quasi-experimental and statistical design.

3.4.1 Correlation, Causation and Cofounds

Correlation cannot be treated as causation, and correlation does not always prove causation. In correlation, it is unclear which variable comes first or whether any alternative explanation exists for the assumed effect. Two variables may be correlated due to chance. Correlation is symmetric, while causation is asymmetric. Two variables may be co-related, but their relationship may be affected by a third variable called cofounds. For example, let’s say that high salary and high educational qualifications are correlated. It is difficult to say with confirmation which comes first. Whether a high educational qualification leads to a high salary, or a high salary leads to a high educational qualification. Both possibilities can hold true and necessitate further investigation. Until researchers conclude through their investigation, a mere correlation among these two variables will not give a clear picture of their causal relationship. There is also the possibility of an alternative explanation for the relationship between high salary and high educational qualifications. The link may be due to a third variable called intellect, which results in high salary and high educational qualifications.

In management research, social science, and natural science, three significant pairs of components are required for experimentation: Experimental and control group, independent and dependent variable, and pre-test and post-test.

3.4.1.1 Experimental and Control Group

The group in which an experimental treatment is administered is known as the experimental or treatment group. In contrast, the group in which no experiment is administered is known as the control group. Using control groups enables the researchers to assess the experiment’s effects. For example, suppose a researcher wants to study the impact of rewards on employee productivity in an organization. In that case, the researcher can experiment with two groups of employees. One group will be given external rewards, known as the experimental group, and the other group (control group) will provide no external rewards. Then, the researcher can investigate the causal association between rewards on employees’ productivity through this experiment. The use of a control group is quite common in medical science research. In social science and management research, the use of control groups and experimental studies became popular with several experiments conducted in the late 1920s and early 1930s by F. J. Roethlisberger and W. J. Dickson (1939) to discover the changes required in working conditions to enhance employee satisfaction and productivity. Their series of experiments resulted in the Hawthorne effect.

3.4.1.2 Independent and Dependent Variables

In experimental research, the researchers study the impact of an independent variable on the dependent variable. Usually, experimental stimuli, whether present or absent, are considered independent variables. Independent variables are manipulated in the study, and their effects are assessed and compared. The researchers compare outcomes when the stimulus is present and not present. Hence, the independent variable is the cause, and the dependent variable is the presumed effect. It is to be noted that the independent variable in one study may serve as a dependent variable in another study. For example, an experiment intends to explore the causality between high salary and job satisfaction, job satisfaction is the dependent variable. However, in another experiment designed to explore the causality between job satisfaction and employee productivity, job satisfaction is the independent variable.

3.4.1.3 Pre- and Post-test

In an experiment, the experimenters measure the variable before conducting the experiment on the group known as the pre-test and measure the variable after conducting the experiments is called as post-test. Hence, subjects are exposed to a stimulus called a dependent variable (pre-testing), then exposed to a stimulus, i.e., independent variable, and again assessed with a dependent variable (post-testing). Any discrepancies between the two measurements of dependent variables are ascribed to the independent variable.

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• Published: 17 September 2024

Exploring the impact of perceived early marriage on women’s education and employment in Bangladesh through a mixed-methods study

• Md. Nuruzzaman Khan 1 , 2 ,
• Shimlin Jahan Khanam 1 ,
• Md. Mostaured Ali Khan 3 ,
• Md Arif Billah 4 &
• Shahinoor Akter 5  

Scientific Reports volume  14 , Article number:  21683 ( 2024 ) Cite this article

Metrics details

• Epidemiology
• Medical research

Child marriage negatively affects women’s socio-economic empowerment, particularly in education and employment. This study aimed to explore women’ perspectives on the timing of their marriages, considering their educational and employment status at the time. It also sought to identify factors influencing early married women’s perception of their marriages as timely. We analyzed both quantitative and qualitative data. The quantitative data included a sample of 5,596 women aged 15–24 from the 2017/18 Bangladesh Demographic and Health Survey. Additionally, we collected qualitative data through six in-depth interviews, two focus group discussions, and 13 key informant interviews. We used a multilevel mixed-effects Poisson regression model to examine the relationship between women’s formal employment, education, and child marriage. Thematic analysis was employed for the qualitative data. Around 62% of the total women analysed reported their married occurred early with the mean age at marriage was 15.2 years. Approximately 55% of the total early married women believed their marriages occurred at the right time, especially among those who were employed at the time of their marriage. Among this subset, we also noticed a higher likelihood of discontinuing work and education following marriage. Qualitative findings revealed reasons behind this perception, such as escaping poverty, safety concerns, limited job prospects, and the impact of non-marital relationship and societal norms. While many early-married women perceived their marriage as timely, particularly those initially employed, this decision often coincides with a subsequent withdrawal from work and education. This underscores the pressing need for policies and programs aimed at educating women about the legal age for marriage and the negative consequences associated with early marriage while also equipping them with knowledge and resources for informed decision-making.

Introduction

Child marriage is a pervasive issue in low- and middle-income countries (LMICs), especially in South Asia and sub-Saharan Africa 1 . The 2022 UNICEF report on child marriage revealed that approximately 12 million girls in LMICs are married before the age of 18 each year, which translates to one in every five girls in those settings 2 . In Bangladesh, the situation is even more alarming, with recent estimates indicating that about 59% of women aged 20–24 years were married before turning 18 3 , and 22% were married before the age of 15 4 . The rate is further higher in rural areas, particularly those with widespread poverty, low education enrolment and significant concerns about family’s reputation 4 , 5 , 6 .

Child marriage can have devastating consequences for girls and their families, as it often leads to a cycle of poverty and disempowerment 7 . Girls who are forced into early marriage are often forced to drop out of school, which reduces and restricts their opportunities for education and limits their economic prospects 8 . Additionally, child marriage has been associated with higher rates of domestic violence and divorce, which can have significant negative impacts on girls’ physical and mental health, hampering the development and wellbeing of girls 9 , 10 . Moreover, child marriage has serious implications for maternal health. Child brides are more likely to experience complications during pregnancy and childbirth, such as obstetric fistula and maternal mortality 11 , 12 . Children born to child brides are also at a higher risk of mortality and malnutrition 13 . This leads to an intergenerational effect because malnourished children are often more likely to drop out of school and subsequently become child brides themselves 14 . These adverse consequences associated with child marriage, coupled with the high number of girls affected, highlight a significant burden in LMICs 1 . Therefore, it poses challenges to achieving Sustainable Development Goals (SDGs) related to health and well-being (SDG 3), gender equality (SDG 5), education (SDG 4), and poverty reduction (SDG 1) 15 .

Socio-demographic factors associated with child marriage have been extensively studied in LMICs, including Bangladesh 16 , 17 , 18 , 19 . Although such observational studies played a crucial role in developing relevant policies and programs to reduce the occurrence of child marriage, they fall short comprehensively addressing the issue. Girls’ views over their marriage along with marriage age can carry a significant weight and override all socio-demographic factors by mediating their roles 20 . It is commonly assumed that every child marriage occurs on parental wishes and is not desired by the girls. However, this is not always the case in reality. The underlying reasons for girls choosing to marry at an early age instead of continuing education and work has remained understudied 21 . Therefore, it is crucial to explore the percentage of girls who perceive their marital age as appropriate or not, as this can inform the development of more effective policies and programs. However, undertaking this requires comprehensive research, integrating determinant factors of early marriage and perceptions of those who marry early. Unfortunately, such information is largely absent in existing literature due to the nature of available data in LMICs, including Bangladesh, where DHS surveys serve as primary data sources 16 , 17 , 18 , 19 , 22 . The survey provides important information on prevalence of early marriage and its socio-demographic predictors, however, lacking content related to girls’ views over early marriage as it typically requires qualitative study. This indicates a need for mixed-methods studies, which are mostly lacking in LMICs, with none conducted in Bangladesh 23 , 24 , 25 , 26 . To address these limitations, we aimed to investigate women’s perceptions of getting married at an earlier age, taking into account their education and employment status at the time of marriage. We also sought to identify the factors that influenced earlier-aged women to perceive their marriages as occurring at the right age.

Study design

The study applied a sequential explanatory mixed-methods design where analysis of secondary quantitative data were followed by the collection and analysis of qualitative data 27 . The qualitative findings, therefore, aimed to explain and interpret the findings of the quantitative study.

Quantitative study

Data source and sampling.

This study analysed data of most recent 2017/18 Bangladesh Demography and Health Survey (BDHS). The survey employed a two-stage stratified random sampling method to select the respondents. In the first stage, 672 Primary Sampling Units (PSUs) were selected from a list of 293,579 PSUs generated during the 2011 National Population Census of Bangladesh, excluding three PSUs due to extreme floods. In the second stage, 30 households were randomly selected from each of the PSUs, using probability proportional to PSU size. This generated a list of 20,160 households, and 19,457 of these households were interviewed. There were 20,376 respondents eligible in the selected households with the eligibility criteria: (i) being a married woman of reproductive age and (ii) spent the previous night of the survey day in the selected households. Of them data were collected from 20,127 women. Details of the BDHS survey procedure were published elsewhere 3 . A sub-sample, 5,596 women aged 15–24, of them was analysed in this study selected based on the following two inclusion criteria: (i) aged 15–24 years (to ensure inclusion of only recently married women following the recommendation of global literature 2 , 6 , 7 , 9 , 24 , 28 ) and (ii) married at the time of survey.

Outcome variable

The focus of our quantitative study was child marriage, which we defined as a binary outcome variable (yes or no). To collect this data, the BDHS asked women to report the age at which they first began living with their spouse or first spouse in case of more than one marriage. We categorized responses as either child marriage (1, if the marriage occurred before the woman turned 18) or normal-aged marriage (0, if the marriage occurred at age 18 or later) according to the universal recommendation which is also followed in Bangladesh 2 .

Exposure variables

Working status and educational status of respondents at the time of their marriage as well as following marriage were our primary exposure variables. The BDHS collected this data by asking whether the respondents were working or studying in school just before they got married. If the response was affirmative, the respondents were then asked two follow-up questions to determine their work or education continuity and the number of years of continuity. These follow-up questions were: (i) Did you continue working/studying after marriage? and (ii) If yes, for how long? Based on the responses, we created four variables: (i) study before marriage (yes, no), (ii) work before marriage (yes, no), (iii) after marriage study (no, continue less than a year, continue less than five years), and (iv) after marriage work (no, continue less than a year, less than five years). We also considered additional exposure variables by reviewing the available literature for Bangladesh and other LMICs 16 , 17 , 18 , 19 . These variables included respondents’ age, education level, partner’s education level, partner’s occupation, wealth quintile, place of residence, and region of residence.

Statistical analysis

Descriptive statistics, including frequency and percentage, were used to describe the characteristics of the respondents. Multilevel mixed-effect Poisson regression model was utilized to explore the association between early marriage and the working and studying status of the respondents, as well as the continuity of their working and studying status following child marriage. The reason for using a multilevel Poisson regression model was higher prevalence of child marriage (> 10%) and the clustering structure of the BDHS data. Previous studies have found that simple logistic regression analysis produces less precise findings when the prevalence of the outcome variable is high and the data come from a clustered structure 29 . Both adjusted and unadjusted models were run, where one particular exposure variable was considered with the child marriage variable in the unadjusted model, and other factors were adjusted in the adjusted model. Multicollinearity was checked before running each model. Results were reported as unadjusted or adjusted Prevalence Ratios (PRs) and corresponding 95% confidence intervals. Stata version 18.0 was used for data analysis.

Qualitative exploration

In our quantitative study, we identified a higher prevalence of child marriage among working women. To explore why working women get married at an early age, we conducted a qualitative study during January 2023 and January 2024 as this information was not available in the BDHS survey data. The Gazipur district of Dhaka division was purposively selected as the study area. This area has a higher concentration of ready-made garments and small-scale industries where the majority of workers are women and married 30 . Two focus group discussions (FGDs) involving 16 participants (8 in each FGD), six in-depth interviews (IDIs), and 13 key informant interviews (KIIs) were conducted using two pre-developed interview topic guides. The topic guides covered several areas, including marriage experience, perceptions at the time of marriage, education and employment after marriage, respondents’ perspectives on marriage over time, and community and religious norms related to early marriage. The length of these interviews ranged from 75 to 90 min.

The participants included in the FGD and IDI were selected purposively selected based on the following criteria: (i) currently aged between 15 and 24 years, (ii) married before their 18th birthday, (iii) involved in either work or education just before marriage, and (iv) currently either continuing education or work or have left them to become housewives. These criteria aimed to ensure that the qualitative study participants were similar to those in the quantitative study. To recruit the participants, data collectors first approached them by sharing the details of the study plan and collected data on their preferred date. To protect privacy of the participants and ensure limited interruption during interview process, the participant and interviewer choose a private location. Prior to the qualitative data collection, participants were again briefed about its objectives and assured of the confidentiality of their responses. Informed consent was obtained from participants above 18 years old, while for participants under 18, informed consent was obtained from their legal guardians (father or husband).

Additionally, 13 key informant interviews (KIIs, male = 9; female = 4) were conducted with managers of ready-made garment factories ( n  = 2), small-scale industries ( n  = 1), local leaders (members of the Pourosova, n  = 3 [male = 2, female = 1]), the Upazila Nirbahi officer ( n  = 1, female), and parents of girls who married at an early age ( n  = 6 [male = 4, female = 2]). Their perspectives on early marriage were sought due to their significant involvement in the issue, including shaping cultural norms and exerting social pressures that perpetuate early marriage practices.

Participation was completely voluntary, and no participants were provided with any gifts or incentives to participate in this study. Experienced social researchers were involved in the qualitative data collection. The FGDs and IDIs were conducted by a female interviewer (second author). The KIIs were conducted by two male interviewers (first and third author). All conversations during FDGs, IDIs and KIIs were audio recorded with consent. The recorded audio files were subsequently reviewed and translated into English by the respective research team members. Relevant sections on the reasons behind early marriage and its impact on work and educational pursuits were extracted and discussed among the team to ensure accurate interpretation and presentation of the data. Qualitative data were thematically analysed 31 , using Nvivo version 12.10 32 . Key themes identified in the analyses were synthesized and presented as study findings. Ethical approval for the qualitative study was obtained from the Institutional Review Board of the University of Rajshahi.

Background characteristics of the respondents

Table  1 presents the background characteristics of the respondents included in the quantitative exploration. The mean age at marriage was 15.2 years (SD, ± 1.41) and the mean years of education were 6.9 years (SD, ± 2.95). About 32.8% of the respondents reported being engaged in a formal job. Of the respondents analyzed, 61.7% reported their marriage occurred before reaching their age 18 years. Over half of the (55.1%) total 61.7% who reported being married before 18 years reported that their marriage occurred at the right time, while 44.9% of them felt that they should have delayed their marriage.

Respondents’ perception about their marriage timing

Table  2 illustrates the distribution of respondents’ perception about their marriage timing as per their socio-demographic characteristics. Among the respondents who believed that their marriage occurred earlier, and they should delay, 49.8% were aged between 15 and 19 years, and 41.2% were aged between 20 and 24 years. Earlier-aged married women who believe their marriage occurred earlier and should be delayed, 36.6% were illiterate. This number was half to 63.4% of illiterate women who thought their marriage occurred at right age.

How women’s education and employment status before marriage influences their perception of marriage age

The unadjusted associations suggest that earlier-married women who thought their marriage occurred at the right time were more likely to have worked before marriage (PR = 1.88, 95% CI = 1.34–2.64) and less likely to have studied before marriage (PR = 0.55, 95% CI = 0.47–0.66) compared to women who thought they should delay their marriage (Table  3 ). After adjusting for confounding variables, the associations remain significant, but the effect sizes are attenuated. Earlier aged women who thought their marriage occurred at the right time were still more likely to have worked before marriage (aPR = 1.47, 95% CI = 1.01–2.18), but the association was no longer significant. However, adjusted likelihood of early marriage was found lower among studying girls (aPR = 0.43, 95% CI = 0.35–0.53).

Impact of early marriage on the continuation of education and employment

Table  4 presents the results of the analysis of the association between the women’s perception of their marriage timing and their continuation of education and work after marriage. In the unadjusted analysis, earlier aged women who think that their marriage occurred at right time were more likely to report continuation of their education up to five years (PR = 1.73, 95% CI 1.31–2.28) as compared to the women who did not continue their education. However, this association was not significant for those who continued their education for less than one year. In the adjusted analysis, the association between continuing education and women’s perception of their marriage timing was attenuated, with women who continued their education for less than five years having a non-significant lower likelihood of perceiving their marriage as occurring at the right time (aPR = 0.92, 95% CI 0.65–1.28). The association between continuing work and women’s perception of their marriage timing remained non-significant in the adjusted analysis.

Through qualitative investigation, we conducted an extensive exploration into the reasons why working women who married at an earlier age believed that their marriage took place at the right time, and also examined why studying women who married at an earlier age perceived their marriage as occurring earlier, as revealed in our quantitative analysis. The characteristics of the participants participated in qualitative interviews are presented in supplementary Tables 1 , 2 and 3 . Our comprehensive findings have uncovered a multitude of factors that can be categorized into distinct thematic patterns (supplementary Table 4). These include: (i) getting married is a way to recover from poverty, (ii) marriage was perceived as a means to ensure the safety and security of young unmarried girls, (iii) less hope for job prospects, and (iv) intimate relationships and social norms.

Getting married way to recover from poverty

Participants reported that a common influencing factor behind early marriage among young girls was their perception that it would help them escape poverty.

“I had dreams of studying and becoming a government service holder, but poverty choked those dreams before they could bloom. Marriage was the only path open to me, even if it means leaving those dreams behind.” (IDI participant 3, age 20). “My parents couldn’t afford to keep me in school anymore, and marriage seemed like the only way to have a roof over my head and food on the table.” (IDI participant 2, age 19).

Most female participants reported that they entered the labour market before reaching their 18th birthday. Since their families were from lower socio-economic backgrounds, they started looking for a job to support their families. They often engaged in low-wage occupations, such as house cleaning or garment factories.

“I worked in house cleaning before my marriage and received a very low wage, which was not enough for my living. As a result, I could not send money to my parents, even though they expected me to do so.” (FGD 1 participant, age 19).

According to them, like many young girls, they also moved to the city from rural areas leaving their families behind in search of jobs and started living in rental accommodations. The income derived from these jobs proved insufficient to meet their daily expenses, including paying for food, rent and utilities while providing financial assistance to their families. This issue was also highlighted in the KIIs. One ready-made garment manager reported that young female ready-made garment factory workers’ wages are not enough to support themselves and their parents. Therefore, they usually decide or agree to get married at an early age to overcome their financial struggles.

“Girls who started working here usually work on a daily basis and earn only 150–200 BDT (1.5-2 USD) per day, which is not enough to maintain their daily expenses. What’s even worse is that many of these girls migrated from rural areas to work here, meaning they have to pay for their rent and other associated costs. It’s no surprise that many of them end up choosing to get married, as it seems like the only way out of this financial struggle”. (Ready-garment manager, male, age 45).

Marriage was perceived as a means to ensure the safety and security of young unmarried girls

In the context of working girls who opt for early marriage, safety and security emerge as crucial factors. Participants reported that young girls working in different industries (such as ready-made garments factories) usually work alongside male workers. Due to the demands of their jobs, they usually spend prolonged hours working together at workplace. The nature of their joint work and spending prolonged hours at workplace often create tensions and a sense of insecurity among themselves and within their families. They fear being exposed to physical and sexual abuse and/or violence at workplace, thereby reinforcing the inclination towards early marriage.

“My parents were concerned about the potential risks of sexual and physical abuse when working outside the home, especially during evening hours and interacting with male colleagues. I would hold the same belief and take similar precautions if I had a young daughter engaged in employment, as there are multiple reasons to support this perspective”. (FGD 1 participant, age 18). “Living alone as a young woman in this city felt dangerous. Marriage, even if it’s not ideal, meant having someone to protect me and a family to belong to.” (IDI participant 2, age 20).

This fear also reflected in the KIIs with fathers. Families of young girls working in ready-made garment factories tend to marry off their young girls to protect their daughters or girls from any future risks of abuse.

“Soon after starting working at age 15, I arranged her marriage. Although it was not my intention, we did not feel secure leaving my young daughter outside the house, as she could be at risk of rape or harassment from strangers. My neighbours also suggested that I do so”. (Father of a married and working young girl, age 55).

One key informant, a supervisor of a ready-made garments industry, also confirmed that young girls working in the industries often become victims of physical and sexual harassments. He also added that the prevalence of sexual violence and harassment had been underreported like in other sectors in Bangladesh and remained a concerning reality. However, the families of the young girls are aware of these risks, even if based on limited evidence, which significantly influenced marry off their girls at a young age against the law.

“A significant number of girls who start working at a young age face violence from their male counterparts, including sexual violence. We are aware of this, and our organization has very strict laws against it. However, these incidents often go unreported, similar to other sectors in Bangladesh”. (A supervisor of a garment factory, male, age 38).

Even parents of young school or college-going girls also voiced their concern regarding their daughters’ safety while traveling to school or college. Sometimes local boys or men harassed these young girls on the way to home or college, and parents of these young girls usually perceived that marrying the young girls off was the only solution.

“I wanted my daughter to continue her education. However, a mischievous boy started following her to school. I contacted his parents and asked them to discipline their son and prevent him from following my daughter, but it did not work. Eventually, I had to marry off my daughter when she was only 16 years old”. (Father of earlier aged girls who marry off while studying, age 38).

However, during the FGDs, female participants expressed their viewpoints that the claims regarding safety concerns and incidents of violence were not always accurate. They argued that this phenomenon might stem from parents’ genuine apprehensions regarding the safety and their desire to uphold societal prestige, their parents mostly forced them to get married at an early age against their will. This highlights the need to delve deeper into the complexities surrounding early marriage and the factors that shape this decision.

“I started working as a house cleaner when I was 15 and got married at age 16. I wasn’t intending to get married at that time, but my parents pressured me to do so. They had heard that young girls working as house cleaners often face sexual and physical violence from homeowners. Though my homeowners treated me like their daughter. Therefore, they were strict in their decision to marry me off”. (FGD 2 participant, age 20).

Less hope for job prospects

Several female participants highlighted the interconnectedness between school dropout, limited employment opportunities for people with lower educational attainment, early entry into workforce, and early marriage. According to them, education could not guarantee job prospects in Bangladesh. Securing a job could be more challenging for individuals with low academic achievements. Therefore, they perceived that entering the job market early would be more worthwhile for them and their families than continuing their education, as it required financial resources.

“Studying seemed pointless when I knew there were barely any jobs at the end. Marriage offered a chance at some stability, even if it wasn’t the kind I hoped for.” (IDI participant 5, age 21).

One FGD participant reported that she perceived that she did not have the necessary qualities to compete in the increasingly competitive job market. She felt early employment would give her practical work experience, opening opportunities for better job prospects. Hence, she decided to dropout from school. Other FGD participants agreed with her, indicating they shared similar perceptions.

“Obtaining a job after completing education is only possible for highly meritorious students. I did not fall into that category. Hence, I decided to start working with the hope that by the time I finished my education, I would have accumulated several years of work experience, which would undoubtedly enhance my chances of securing a comparatively better job”. (FGD 2 participant, age 19).

Some guardians of young school or college-going girls also perceived that education would not guarantee any employment for their daughters and married them off at an earlier age.

“Why would I continue my daughter’s education? What hope was there? Even many educated and meritorious students are now unemployed. Therefore, I married off my daughter when she was only 15, and she is now leading her own life. I have no concern now”. (Father of earlier aged girls who marry off while studying, age 40).

Intimate relationships and social norms

Several female participants revealed that working girls were found to be more susceptible to developing relationships with their colleagues. These close bonds often evolve into sexual relationships, contributing to early marriage among working girls.

“Shortly after I began working, I entered into a relationship with one of my colleagues, which later turned into a sexual relationship. We then decided to marry, although our parents were unhappy with our decision. However, we did it without their approval because we knew what we did was not right according to our religion”. (FGD 1 participant, age 18).

The issue of having a relationship at the workplace without parents’ permission was identified as a growing concern for early marriage, even in the absence of an intimate relationship.

“Parents of the earlier-aged working girls are mostly uneducated and strongly influenced by the social norms and misconceptions. They believe female and male could not be co-worker. Therefore, they prefer to marry off their daughters earlier”. (Upazila Nirbahi officer, age 38).

Importantly, this perception was found to be common among parents of both working and studying girls. However, it does not pose a significant concern for the parents of studying girls. This is because their awareness of the negative effects of child marriage motivates them to prioritize the education of their daughters and prioritize their daughters’ safety above all else.

“I had concerns about the possibility of my school-going daughters engaging in intimate relationships, which have become more common due to modernization. However, my intention was not to abruptly end her education and arrange her marriage solely based on this risk. I believed she was young and had a promising future ahead. Eventually, I did make the decision to marry her, but it was primarily motivated by the opportunity to find a comparatively better groom within my family lineage”. (Father of earlier aged girls who marry off while studying, age 38).

Social norms were highlighted as significant reasons for early marriage among the women who married at a younger age.

“Marriage is a way to uphold our traditions, to show respect to our families and ancestors. Even if I had doubts, I knew I had to follow the path laid out for me.” (IDI participant 4, age 22).

One participant mentioned that she felt she did not belong in her society and remained a minor in society’s eyes when she found many of peers were getting married at an earlier age than her.

“Everyone around me was getting married young, building families. It felt like I was the only one left behind, stuck in a childhood that was no longer fitting. Marriage was a way to belong, to be seen as a responsible adult.” (IDI participant 5, age 21).

Another participant explicitly mentioned why marriage was important for them to become important in the society.

“Marriage is a social currency here. It defines your status, your worth. Choosing a career over marriage felt like choosing shame over acceptance, a path less traveled and less understood.” (IDI participant 3, age 19).

The primary objective of this study was to explore the perspectives of girls regarding the timing of their own marriages, taking into consideration their educational and employment backgrounds at the time of marriage. Furthermore, we aimed to investigate the factors that influenced earlier-aged women to perceive their marriages as occurring at the appropriate time. Our findings indicate that among the total population of earlier-aged women, 55% believe that their marriages took place at the right time, with a higher percentage observed among women who were employed at the time of marriage. Among those who held this perception, there was a notable trend of discontinuing work and education after getting married. Through qualitative analysis, we gained insights into the underlying reasons why these women considered their marriages to be timely, including the desire to escape poverty, concerns regarding safety and security, and the influence of intimate relationships and societal norms.

The study findings convey three significant messages concerning early marriage in the country. Firstly, a substantial portion of early marriages are a result of girls’ choice. Secondly, the engagement of girls in formal employment contributes to an increase in early marriage rates, unless measures are implemented to ensure economic security and safety. Lastly, early-married girls who believe that their marriages occurred at the right time are more likely to discontinue their education and withdraw from the workforce.

The perception of working girls who marry early that their marriage occurred at the right time can be understood from two distinct directions. Firstly, these girls may lack awareness regarding the appropriate age for marriage and the potential negative consequences associated with marrying at a young age. Secondly, their working environment and the challenges they face may have influenced their decision to marry early, despite being aware of the adverse outcomes of early marriage. This may include parental pressure for marriage once girls start working, especially if they work alongside male colleagues or develop intimate relationships with them, which can conflict with societal norms 33 . Traditional patriarchal values in Bangladesh further reinforce these pressures 33 . Regardless of the direction, these perceptions indicate a failure of policies and programs, which can result in long-term burdens for the country.

If the first direction holds true, it suggests that a portion of women have not received the message regarding the correct age for marriage at early ages and consequences of early marriage. Factors such as early dropout from education to enter the workforce and limited exposure to mass media due to work obligations may contribute to this lower level of awareness 28 , 29 , 34 . These directions are influenced by various socio-demographic and socio-cultural factors. While leaving the parental home to enter the workforce can indicate a degree of freedom for girls, this is not always the case in many LMICs, including Nepal and India 8 , 18 . In these contexts, girls who start working early, often without continuing their education, typically come from low-income families where they are expected to support their families. Early marriage remains a long-standing norm in these communities, perpetuated by the fact that their mothers and grandmothers also married at a young age. Furthermore, these girls often move from their parental homes to their workplace, which is often seen as opposite of social norms in Bangladesh as well as other LMICs 35 .

On the other hand, if the second directions is true, it indicates a failure of long-standing governmental priorities to ensure continuing girls’ education and prevent child labour, as well as a failure to ensure the safety of working girls 18 . Though Bangladesh has made remarkable progress in reducing violence against women, incidents still occur frequently with one in three women faces it 36 . Importantly, any such incidents are usually spread widely with additional rumours, causing concern for the girls’ security and motivating their parents to marry them off early. However, the above explanations may not be true for the girls who continue education. They are usually better aware off about the right age of marriage and risk of early marriage as well as usually stay at home with less security issue. They are also from a comparatively better family status.

In this study, we have also found that there is a lower likelihood of continuing work after marriage. This change is mainly due to the presence of family pressure or intention to have a child, as well as the desire to give more time to the family 37 , 38 . Moreover, we found that while women are employed, their wages in the factories are very low. This financial constraint limits their opportunities, often pushing them towards early marriage or leading them to leave work altogether to assume traditional roles as wives, mothers, and homemakers 39 . However, this practice can have several adverse consequences on women’s economic flexibility, empowerment and decision making abilities in the family 40 , 41 . For instance, stopping work can lead to face earlier aged pregnancy, which is associated with various adverse maternal and child health outcomes, including lower utilization of maternal healthcare services, pregnancy complications, and maternal and child mortality 40 , 42 . Furthermore, this trend indicates a significant dropout rate of girls from continuing their education and working status, which negatively affects women’s empowerment and decision-making abilities 40 , 41 . These consequences can lead to higher household poverty, greater sensitivity to economic shocks, and less income diversification 41 . These factors, in turn, can have significant intergenerational impacts, resulting in poorer health among children and lower investment in education and other forms of human capital accumulation 43 , 44 . All of these factors increase the likelihood of early marriage in subsequent generations 43 .

Regardless of the explanation provided, these findings indicate challenges for the country to achieve relevant SDG’s targets related to the improvement of sexual and reproductive health rights as well as equity. This highlights the need for policies and programs to educate and raise awareness among studying and working girls about the correct age of marriage and the adverse effects of early marriage. Increasing the wages of working girls at the initial level is also important. Nevertheless, the existing initiatives remain ineffective unless appropriate engagement of multiple stakeholders including girl’s parents and local leaders for implementation of target-oriented policies and programs to reduce early marriage. Reducing gender-based violence and improving their safety in the workplace are also crucial to reducing early marriage.

This study exhibits several notable strengths as well as a few limitations. It stands as the first investigation in Bangladesh and other LMICs that delves into women’s perceptions of their marriage age, accounting for their educational and employment statuses, and utilizing nationally representative quantitative survey data. Additionally, the qualitative survey findings offer insight into the motivating factors behind the marriage decisions of working and educated girls. The qualitative interviews were conducted by the authors of this study, who have extensive experience in academia and public health research. They hold postgraduate degrees in population science, public health, and anthropology, and possess substantial expertise in conducting research in LMICs, including Bangladesh. All authors agreed on and approved the interpretation presented in the manuscript. The study employed appropriate statistical modelling techniques to analyze the data, incorporating a diverse range of confounding variables. As a result, the reported findings possess sufficient robustness to inform national-level policies and programs. However, one key limitation of this study is that the quantitative data analyzed in this study were derived from a cross-sectional survey, which restricts the ability to establish causal relationships. While the study explored cultural factors associated with early marriage through qualitative analysis, these factors were not adjusted for in the quantitative analysis due to their unavailability within the survey. We were also unable to account for other important factors, such as spousal age differences and the extent of early marriage within the women’s families, due to a lack of data, despite their relevance to the occurrence of early marriage. Furthermore, the women’s age of marriage was self-reported, introducing the potential for recall bias. Nevertheless, any such bias is expected to be random in nature and should not significantly skew the reported results in any particular direction. Conducting qualitative study was another strength of this study, where representation of women (in IDIs and FGDs) and men (in the KIIs) allowed us to capture diverse perspectives on this complex issue. We conducted the qualitative study in a purposively selected district and utilized data to explain and interpret findings from the nationally representative quantitative data. However, this comparison may introduce errors due to different social norms and cultural issues regarding early marriage in various parts of the country. Moreover, the perception of early marriage among this group of women might differ slightly from that of women in other regions and rural areas due to factors such as their relocation, economic stability, community engagement, and relatively higher decision-making autonomy. This indicates a need for qualitative interviews to be conducted in different regions of the country. However, we were unable to do so due to a lack of funding. It is worth noting that although participants included in our qualitative survey were from only one district, a significant portion of them reported their origins as being from different parts of the country, including rural areas, rather than their present location. They had moved to this district for employment, given that ready-made garment and small-scale industries are predominantly located in this area. Moreover, we conducted qualitative interviews in 2023–2024, while the quantitative data we analysed was collected in 2017–2018. Comparing data from different time points may introduce some distortion in the reported associations and conclusions. However, we could not address this issue further as the quantitative survey data we analysed is the most recent available in Bangladesh.

Our findings revealed that approximately 55% of women who married at an early age believed that their marriage took place at the right time. Among early-married women, those who were employed at the time of their marriage were more likely to perceive their marriage as timely, whereas those who were pursuing studies at the time of their marriage were more inclined to view their marriage as occurring too early and should have been delayed. Multiple factors emerged as influential in shaping the perception of earlier-aged married women regarding the timing of their marriage, including the desire to escape poverty, concerns related to safety and security, and the influence of intimate relationships and societal norms. These findings highlight that a significant proportion of early-married women believe their marriage occurred at the right time, indicating a gap in policies and programs designed to raise awareness about the risks of early marriage and early childbearing. It is crucial for policies and programs to prioritize comprehensive education for all girls and those around them, including parents, to ensure they are informed about the appropriate age for marriage and the potential consequences of early marriage. Additionally, those involved in decision-making and upholding social norms around early marriage should receive extensive counselling on its adverse effects. This focus should particularly target working girls, who may be more vulnerable to early marriage.

Data availability

“The data that support the findings of this study are available from The DHS Program, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the corresponding author upon reasonable request and with permission of The DHS Program. To proceed, researchers are required to submit a research proposal via the website (https://dhsprogram.com/data/available-datasets.cfm). Subsequently, the designated individual will review the proposal and approve access for data download. We are unable to share the qualitative interview data due to restrictions imposed by the ethical review committee”.

Abbreviations

Low- and middle-income countries

Demographic Health Survey

Bangladesh Demographic Health Survey

adjusted odds ratio

Confidence interval

Sustainable development goal

National Institute of Population Research and Training

Primary sampling unit

Subramanee, S. D. et al. Child marriage in South Asia: a systematic review. Int. J. Environ. Res. Public Health 19 (22), 15138 (2022).

PubMed   PubMed Central   Google Scholar  

UNICEF. UN General Assembly Resolutions on Child Marriage . https://www.unicef.org/protection/child-marriage (2022).

National Institute of Population Research and Training (NIPORT). Bangladesh Demographic and Health Survey 2017–2018 (2020).

Bangladesh Bureau of Statistics (BBS) & UNICEF Bangladesh Progotir Pathey, Bangladesh Multiple Indicator Cluster Survey 2019, Survey Findings Report (2019).

Marphatia, A. A., Ambale, G. S. & Reid, A. M. Women’s marriage age matters for public health: a review of the broader health and social implications in South Asia. Front. Public Health 5 , 269 (2017).

UNICEF. Ending Child Marriage: A Profile of Progress in Ethiopia (UNICEF, 2018).

Scolaro, E. et al. Child marriage legislation in the Asia-Pacific region. Rev. Faith Int. Affairs 13 (3), 23–31 (2015).

Google Scholar  

Sekine, K. & Hodgkin, M. E. Effect of child marriage on girls’ school dropout in Nepal: analysis of data from the multiple Indicator Cluster Survey 2014. PLoS ONE 12 (7), e0180176 (2017).

Islam, M. M. Child marriage, marital disruption, and marriage thereafter: evidence from a national survey. BMC Women’s Health 485 , 22485 (2022).

Naved, R. T. et al. Impact of Tipping Point Initiative, a Social Norms Intervention, in Addressing Child Marriage and Other Adolescent Health and Behavioral Outcomes in a Northern District of Bangladesh (2022).

Dadras, O., Khampaya, T. & Nakayama, T. Child marriage, reproductive outcomes, and service utilization among young Afghan women: findings from a nationally Representative Survey in Afghanistan. Stud. Fam. Plann. 53 (3), 417–431 (2022).

PubMed   Google Scholar  

Fan, S. & Koski, A. The health consequences of child marriage: a systematic review of the evidence. BMC Public Health 22 (1), 1–17 (2022).

Hossain, M. M. et al. Child marriage and its association with morbidity and mortality of under-5 years old children in Bangladesh. PLoS ONE 17 (2), e0262927 (2022).

CAS   PubMed   PubMed Central   Google Scholar  

Khanam, S. J. & Khan, M. N. Effects of parental migration on early childhood development of left-behind children in Bangladesh: evidence from a nationally representative survey. PLoS ONE 18 (11), e0287828 (2023).

Julitta Onabanjo, M. M. M. F. Urgent Action Needed to Meet SDG to End Child Marriage by 2030 (UNICEF, 2022).

Kamal, S. M. et al. Child marriage in Bangladesh: trends and determinants. J. Biosoc. Sci. 47 (1), 120–139 (2015).

MathSciNet   PubMed   Google Scholar  

MacQuarrie, K., Juan, C. & Fish, T. D. Trends, Inequalities, and Contextual Determinants of Child Marriage in Asia (ICF, 2019).

Billah, M. A. et al. Spatial Pattern and Influential Factors for Early Marriage: Evidence from Bangladesh Demographic Health Survey Data 2017–2018 (2023).

Modak, P. Determinants of girl-child marriage in high prevalence states in India. J. Int. Women’s Stud. 20 (7), 374–394 (2019).

Boertien, D. & Härkönen, J. Why does women’s education stabilize marriages? The role of marital attraction and barriers to divorce. Demogr. Res. 38 , 1241–1276 (2018).

Mourtada, R., Schlecht, J. & DeJong, J. A qualitative study exploring child marriage practices among Syrian conflict-affected populations in Lebanon. Confl. Health 11 (1), 53–65 (2017).

Biswas, R. K., Khan, J. R. & Kabir, E. Trend of child marriage in Bangladesh: a reflection on significant socioeconomic factors. Child Youth Serv. Rev. 104 , 104382 (2019).

Saleh, A. M. et al. Exploring Iraqi people’s perception about early marriage: a qualitative study. BMC Women’s Health 22 (1), 393 (2022).

UNICEF. Child marriage knowledge, attitudes, and perceptions among affected communities in Albania. UNICEF 1 , 1–101 (2018).

Seta, R. Child marriage and its impact on health: a study of perceptions and attitudes in Nepal. J. Glob. Health Rep. 7 , e2023073 (2023).

Mardi, A. et al. Perceptions of teenage women about marriage in adolescence in an Iranian setting: a qualitative study. Electron. Phys. 10 (2), 6292 (2018).

Maxwell, J. A. Qualitative Research Design: An Interactive Approach (Sage, 2012).

UNICEF. Child Marriage, Aolescent Pregnancy and School Dropout in South Asia . https://www.unicef.org/rosa/reports/child-marriage-adolescent-pregnancy-and-school-dropout-south-asia (2022).

Barros, A. J. & Hirakata, V. N. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med. Res. Methodol. 3 (1), 1–13 (2003).

Swazan, I. S. & Das, D. Bangladesh’s emergence as a ready-made garment export leader: an examination of the competitive advantages of the garment industry. Int. J. Glob. Bus. Competitive. 17 (2), 162–174 (2022).

Clarke, V. & Braun, V. Thematic analysis. J. Posit. Psychol. 12 (3), 297–298 (2017).

Dhakal, K. NVivo J. Med. Libr. Assoc. 110 (2), 270 (2022).

Ferdous, D. S., Saha, P. & Yeasmin, F. Preventing Child, Early and Forced Marriage in Bangladesh: Understanding Socio-economic Drivers and Legislative Gaps (2019).

Bajracharya, A., Psaki, S. & Sadiq, M. Child Marriage, Adolescent Pregnancy and School Dropout in South Asia (Report by the Population Council for the United Nations Children’s Fund Regional Office for South Asia, 2019).

Jayachandran, S. Social norms as a barrier to women’s employment in developing countries. IMF Econ. Rev. 69 (3), 576–595 (2021).

MathSciNet   Google Scholar  

Shahen, M. A. Gender-based violence in Bangladesh: a critical analysis. Int. J. Qual. Res. 1 (2), 127–139 (2021).

Bhowmik, J., Biswas, R. K. & Hossain, S. Child marriage and adolescent motherhood: a nationwide vulnerability for women in Bangladesh. Int. J. Environ. Res. Public Health 18 (8), 4030 (2021).

Daily & Star From Early Marriage to Risky Pregnancy: We Must Break the Cycle (Dhaka, 2022).

Nagata, H. Female workers’ skills, wages, and householding in Bangladesh’s readymade garment industry: the case of a Japanese multinational company. J. Interdiscp. Econ. 32 (1), 47–74 (2020).

Ahinkorah, B. O. et al. Girl child marriage and its association with maternal healthcare services utilization in sub-saharan Africa. BMC Health Serv. Res. 22 (1), 777 (2022).

Abera, M. et al. Early marriage and women’s empowerment: the case of child-brides in Amhara National Regional State, Ethiopia. BMC Int. Health Hum. Rights 20 (1), 1–16 (2020).

Li, C., Cheng, W. & Shi, H. Early Marriage and Maternal Health care Utilisation: Evidence from sub-Saharan Africa 101054 (Economics & Human Biology, 2021).

Sekhri, S. & Debnath, S. Intergenerational consequences of early age marriages of girls: Effect on children’s human capital. J. Dev. Stud. 50 (12), 1670–1686 (2014).

Otoo-Oyortey, N. & Pobi, S. Early marriage and poverty: exploring links and key policy issues. Gend. Dev. 11 (2), 42–51 (2003).

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Acknowledgements

We are thankful to MEASURE DHS for the data support and also grateful to icddr, b where the data for this study was analysed. We are also acknowledged the Governments of Bangladesh, Canada, Sweden and the UK for providing core/unrestricted support for to run icddr, b. The authors also acknowledge the support of Health System and Population Studies Division of icddr, b and Department of Population Science of Jatiya Kabi Kazi Nazrul Islam University, where this study was designed and conducted.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Nossal Institute for Global Health, Melbourne School of Population and Global health, The University of Melbourne, Melbourne, 3010, Australia

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Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), 68 Shaheed Tajuddin Ahmed Sarani, Mohakhali, Dhaka, 1212, Bangladesh

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Health System and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), 68 Shaheed Tajuddin Ahmed Sarani, Mohakhali, Dhaka, 1212, Bangladesh

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“Khan MN designed the study. Khan MN, Khanam SJ and Khan MMA collected qualitative data. Khan MN and Billah MA analysed quantitative data while all authors analysed qualitative data. Khan MN, Khan MMA, Billah MA and Khanam SJ wrote the first draft of this manuscript. Akter S critically reviewed and edited the previous versions of this manuscript. All authors approved this final version of the manuscript”.

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Correspondence to Md. Nuruzzaman Khan .

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Ethical approval

The quantitative data analysed in this study was extracted from the survey which was approved by the institutional review board of ICF macro (Inner City Fund) and the National Research Ethics Committee of the Bangladesh Medical Research Council. Informed consent was obtained from all participants. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. No separate ethical approval was required to conduct this study. We obtained permission to access this survey and conduct this research. All methods were performed in accordance with the relevant guidelines and regulations. Ethical approval for conducting the qualitative survey was obtained from the Institutional Review Board of the University of Rajshahi (123/430/IAMEBBC/IBSc), ensuring compliance with ethical guidelines and protocols. Informed consent was obtained from all participants who were 18 years of age or older. For participants who were under 18 years of age, their legal guardian, such as their husband or father, provided informed consent on their behalf. This process ensured that all participants had a clear understanding of the survey’s purpose, procedures, and potential risks, and voluntarily agreed to participate. So that this applies to illiterate respondents as well.

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Khan, M.N., Khanam, S.J., Khan, M.M.A. et al. Exploring the impact of perceived early marriage on women’s education and employment in Bangladesh through a mixed-methods study. Sci Rep 14 , 21683 (2024). https://doi.org/10.1038/s41598-024-73137-w

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Comparison of multi-parallel quantitative real-time PCRs targeting different DNA regions and detecting soil-transmitted helminths in stool

• Marina Papaiakovou 1 , 2 , 3 ,
• Rubén O. Cimino 4 , 5 ,
• Nils Pilotte 1 , 6 ,
• Julia Dunn 7 ,
• D. Timothy J. Littlewood 2 ,
• Steven A. Williams 1 ,
• Alejandro J. Krolewiecki 4 , 5 &
• Rojelio Mejia 4 , 8  

Parasites & Vectors volume  17 , Article number:  390 ( 2024 ) Cite this article

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Metrics details

Soil-transmitted helminths infect an estimated 18% of the world’s population, causing a significant health burden. Microscopy has been the primary tool for diagnosing eggs from fecal samples, but its sensitivity drops in low-prevalence settings. Quantitative real-time polymerase chain reaction (qPCR) is slowly increasing in research and clinical settings. However, there is still no consensus on preferred qPCR targets.

We aimed to compare soil-transmitted helminth (STH) DNA detection methods by testing naïve stool samples spiked with known quantities of STH eggs and larvae. DNA extracts from spiked samples were tested using independent quantitative realtime PCR (qPCR) assays targeting ribosomal or putative non-protein coding satellite sequences.

For Trichuris trichiura, there was a strong correlation between egg/larvae counts and qPCR results using either qPCR method (0.86 and 0.87, respectively). Strong correlations also existed for A. lumbricoides (0.60 and 0.63, respectively), but weaker correlations were found for Ancylostoma duodenale (0.41 for both assays) and Strongyloides stercoralis (0.48 and 0.65, respectively). No correlation for Necator americanus was observed when testing with either qPCR assay. Both assays had fair-to-moderate agreement across targets when using field-collected stool samples (0.28–0.45, for all STHs), except for S. stercoralis (0.12) with slight agreement.

Conclusions

There is a strong correlation between qPCR results and egg/larvae counts. Our study confirms that qPCR is an effective diagnostic tool, even with low-intensity infections, regardless of the DNA-based diagnostic marker used. However, the moderate agreement between the two different qPCR assays when testing field samples highlights the need to understand the role of these targets in the genome so that the parasite burden can be quantified more accurately and consistently by qPCR.

Graphical abstract

Soil-transmitted helminths (STHs) ( Ascaris lumbricoides , Trichuris trichiura , Necator americanus , Ancylostoma duodenale ) and Strongyloides stercoralis infect more than 1.4 billion people worldwide [ 1 ], resulting in years of disability and extensive morbidity. The most widely used techniques for diagnosing STH infections are microscopy-based, despite the repeated demonstration of their shortcomings [ 2 ]. Microscopy is still recommended by the World Health Organization (WHO) for use in epidemiological interventions and monitoring progress in deworming programs [ 3 ]. Advances in molecular testing have spurred interest in developing new tools for monitoring STH infections, such as assays utilizing real-time polymerase chain reaction (qPCR) [ 4 , 5 , 6 ]. There is a need for more sensitive tools to complement WHO efforts to monitor the elimination of STHs [ 7 ]. Intervention success and impact assessment are highly reliant upon sensitive and accurate diagnostic tools for STH detection. However, attempts to evaluate molecular methods remain incomplete, including assessing one or more microscopic techniques against molecular methods [ 8 , 9 , 10 ]. Different real-time PCR assays targeting various DNA regions [ribosomal internal transcribed spacer sequences (ITS), ribosomal subunit sequences, or mitochondrial genes] have been developed to detect STHs [ 11 ]. Assays targeting mitochondrial and ribosomal sequences leverage their relatively high copy numbers, providing moderate-to-high sensitivity for real-time PCR. Ribosomal assays have been validated in numerous studies and clinical settings for STH detection [ 6 , 8 , 12 , 13 ]. However, ribosomal targets tend to be conserved between species and present in lower copy numbers than other genome repeats. Given their conservation, they are frequently less specific than targets designed from other repeat types. Other nuclear tandemly arranged repeats can reach up to 37% of the parasite’s genome in certain species of STHs [ 14 ]. The development of new bioinformatics tools [ 15 ] and the assembly of improved genomes for the various STHs [ 16 ] has facilitated the exploration of the function of these target sequences and the genetic variation. Novel, optimized assays targeting highly repetitive elements in a parasite’s genome have the potential to reduce time and cost through high throughput automation [ 14 ]. Such targets have enhanced the sensitivity and specificity of qPCR assays, allowing for the differentiation of closely related species and facilitating target detection at copy numbers below those found within a single egg [ 5 , 17 ].

This study assesses the agreement when two different molecular assays utilize different target sequences and compares results across a panel of samples spiked with known quantities of STH eggs or larvae. Similar testing was also performed on a panel of field-collected samples to assess the transferability of results.

Spiked sample preparation

Known numbers of parasitic eggs (1, 2, 5, 10, 15, 20, 40 egg or larvae) were used to spike 10 mg samples of naïve stool at Baylor College of Medicine (BCM), followed by DNA extraction using the FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana, CA) and a high-speed homogenizer (FastPrep-24, MP Biomedicals). In total, 19 samples containing A. lumbricoides eggs were created, as were 20 containing T. trichiura eggs, 24 containing S. stercoralis larvae, and 10 containing hookworm eggs. Details on the number of replicates per egg/larvae quantity used for spiking can be found in Supplementary Table S1. Aliquots of the same DNA extracts were shipped from the BCM to the Natural History Museum (NHM) for testing.

qPCR testing

Two independent laboratories, BCM and NHM, tested aliquots of the same DNA extracts. The NHM assay was initially developed at Smith College in Northampton, MA, USA [ 5 ], and the BCM assay was initially developed at the National Institutes of Health (NIH) in Bethesda, MD, USA [ 6 ]. The assays used for testing target repetitive genomic elements, except for the A. lumbricoides assay, which targets the internal transcribed spacer 1 (ITS1) region. The assays used at BCM all target ribosomal genes (ITS1 for A. lumbricoides , ITS1 for T. trichiura , 18S for S. stercoralis , ITS2 for N. americanus , and ITS2 for A. duodenale ).

Field sample testing

A panel of 130 samples was collected as part of ongoing field studies in Orán, Argentina (approved by the bioethics committee of Colegio de Médicos de la Provincia de Salta and the IRBs of BCM; protocol number H-34926). All samples underwent direct smear stool microscopy and were frozen, without preservatives, until 50 mg of each sample was subjected to DNA extraction. Aliquots were sent to BCM and NHM for qPCR analysis using the above assays.

Statistical analysis

The correlation between target concentration (fg/µl or copies/µl, for the BCM assays and the NHM assays, respectively) and spiked egg numbers was assessed by the Kendall rank correlation test [ 18 ]. Correlations were visualized in R v.4.2.2; N. americanus was excluded from the graphs due to too few data points, but the correlation values are still presented in Table  1 . P -values < 0.05 were considered statistically significant. Comparisons between the two qPCRs were depicted using unweighted Cohen’s kappa agreement [ 19 ]. Fleiss kappa was calculated to evaluate the agreement between microscopy and the two qPCR tests (three raters), treating results as categorical values (presence/absence).

Concordance between qPCR and egg/larvae counts

The Kendall Tau-b values for the NHM and BCM assays were 0.86 and 0.87 for T. trichiura and 0.60 and 0.63 for A. lumbricoides , indicating strong concordance between DNA quantity measured using qPCR and egg numbers as determined by microscopy for both STHs. Using both assays, the Tau-b values for A. duodenale (0.41 for both, but not significant) and S. stercoralis (0.48 and 0.65, respectively) were less strong but still significant. With 0 and −0.816 Tau-b values for the NHM and BCM assays, respectively, results for N. americanus are probably due to ineffective extraction or insufficient eggs/larvae, making it difficult to draw any conclusions (Table  1 ). The graphs in Additional File 1 : Fig. S1. also show the linearity and correlation between the qPCR quantitative method and eggs or larvae spiked ( N. americanus graph not shown).

Two- and three-rater agreement on field samples

We calculated the overall percentage agreement (total number of agreed positives or negatives in a given sample set). Cohen’s kappa for agreement on a sample-by-sample case, for both sets of qPCR assays, treats data as categorical values (presence/absence) since the qPCR output interpretation (i.e., quantitation) still does not correspond directly to worm burden or worm intensity [ 9 , 20 ]. Fleiss kappa was calculated to show the greater discordance between microscopy and both qPCR assays; the results are presented in Table  2 . Between the two sets of qPCR assays, there was a moderate agreement for A. lumbricoides (kappa = 0.43) and fair agreement for N. americanus (kappa = 0.33), T. trichiura (kappa value = 0.366), and A. duodenale (kappa value = 0.28). However, both assays showed a slight agreement for S. stercoralis (kappa value = 0.121). As expected, Fleiss kappa showed weak agreement between microscopy (ranging from 0.06 to 0.22) and qPCR assays (Table  2 ).

We present a comparative study evaluating two independent qPCR assay platforms for four STH species and S. stercoralis using laboratory-spiked and field-collected samples. We showed concordance and moderate-to-strong correlation between the presence of helminth eggs or larvae and the amount of parasite DNA. A strong correlation between spiked eggs and qPCR output has been demonstrated previously in similar settings [ 21 ]. Greater discordance between both qPCR platforms and microscopy illustrates the superior sensitivity (true positives) and specificity (true negatives) of the molecular methods compared with coprological tests. This comparative study demonstrates the benefits of qPCR when STH prevalence and intensity are low in a population. Molecular assays can be the foundation for reliable diagnostic results, irrespective of the target used, and opportunities for technological transfer, even to resource-limited areas, are expanding [ 22 , 23 ]. We acknowledge that the number of spiked samples (eggs and larvae) used in this study was limited, requiring further scaling. This could partially explain the poor correlation observed for A. duodenale . The poor concordance between larval counts and qPCR for S. stercoralis could be explained by the low sensitivity of qPCR for Strongyloides [ 24 ]. We focused solely on spiking stool samples with small numbers of eggs/larvae, as the correlation between larger egg counts and qPCR in field-collected stool samples has already been demonstrated [ 7 , 9 , 25 ]. Another limitation was that an extraction control to check the efficacy of the DNA extraction method was not available at the time of this study. Suboptimal extraction of the samples spiked with N. americanus could explain the 0 and negative correlations for both N. americanus- targeting assays. Although DNA extraction products were sent to each institution, false negatives will impact both assays. One downside of using repeat-based assays is that the copy number of highly repetitive sequences can vary significantly between individual organisms and even within different stages of the same organism’s life cycle. However, larger monomer repeats might be more consistent within species [ 14 , 26 ]. Further and future genomic work will highlight structural and geographical differences in repeat-based diagnostics. Currently, the full function of these nuclear repeats is mainly unknown. As a result, we are unsure how random mutations affect these areas and whether this variability can lead to bias in the quantification of STHs. These mutations make it challenging to compare results between samples accurately. Another limitation was the lack of egg/larvae counts from stool microscopy. However, previous studies have shown that the amount of egg burden directly correlates with detecting parasite DNA with qPCR in field-collected stool samples [ 6 , 9 ].

In summary, we present further evidence that qPCR is a valid alternative to fecal microscopy for the detection of STH, and our study supports current attempts [ 7 ] toward replacing coprological tools to assess low-intensity STH infections using qPCR. Results highlight assay-specific strengths and weaknesses. This study represents the first comparison of two distinct qPCR platforms with unique molecular targets. The study involved samples spiked with known quantities of STH eggs or larvae. The findings provide a framework for better understanding what constitutes a reliable diagnostic target. Developing standardized, accurate, and quality-controlled measures is crucial to successfully testing for STHs in fecal samples using qPCR. Standardization can be achieved through an External Quality Assessment Scheme (EQAS) program [ 27 ], which ensures consistency and diagnostic precision.

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Jourdan PM, Lamberton PHL, Fenwick A, Addiss DG. Soil-transmitted helminth infections. Lancet. 2018;391:252–65.

Article   PubMed   Google Scholar  

Nikolay B, Brooker SJ, Pullan RL. Sensitivity of diagnostic tests for human soil-transmitted helminth infections: a meta-analysis in the absence of a true gold standard. Int J Parasitol. 2014;44:765–74.

Article   PubMed   PubMed Central   Google Scholar  

Montresor A, Porta N, Albonico M, Gabrielli AF, Jankovic D, Fitzpatrick C, Vercruysse J, Levecke B. Soil-transmitted helminthiasis: the relationship between prevalence and classes of intensity of infection. Trans R Soc Trop Med Hyg. 2015;109:262–7.

Autier B, Gangneux J-P, Robert-Gangneux F. Evaluation of the AllplexTM GI-Helminth(I) Assay, the first marketed multiplex PCR for helminth diagnosis. Parasite. 2021;28:33.

Pilotte N, Papaiakovou M, Grant JR, Bierwert LA, Llewellyn S, McCarthy JS, Williams SA. Improved PCR-based detection of soil transmitted helminth infections using a Next-Generation Sequencing approach to assay design. PLoS Negl Trop Dis. 2016;10:e0004578.

Mejia R, Vicuña Y, Broncano N, Sandoval C, Vaca M, Chico M, Cooper PJ, Nutman TB. A novel, multi-parallel, real-time polymerase chain reaction approach for eight gastrointestinal parasites provides improved diagnostic capabilities to resource-limited at-risk populations. Am J Trop Med Hyg. 2013;88:1041–7.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Papaiakovou M, Littlewood DTJ, Gasser RB, Anderson RM. How qPCR complements the WHO roadmap (2021–2030) for soil-transmitted helminths. Trends Parasitol. 2021;37:698–708.

Article   CAS   PubMed   Google Scholar  

Cimino RO, Jeun R, Juarez M, Cajal PS, Vargas P, Echazú A, Bryan PE, Nasser J, Krolewiecki A, Mejia R. Identification of human intestinal parasites affecting an asymptomatic peri-urban Argentinian population using multi-parallel quantitative real-time polymerase chain reaction. Parasit Vectors. 2015;8:380.

Dunn JC, Papaiakovou M, Han KT, Chooneea D, Bettis AA, Wyine NY, Lwin AMM, Maung NS, Misra R, Littlewood DTJ, Anderson RM. The increased sensitivity of qPCR in comparison to Kato-Katz is required for the accurate assessment of the prevalence of soil-transmitted helminth infection in settings that have received multiple rounds of mass drug administration. Parasit Vectors. 2020;13:324.

Levecke B, Cools P, Albonico M, Ame S, Angebault C, Ayana M, et al. Identifying thresholds for classifying moderate-to-heavy soil-transmitted helminth intensity infections for FECPAKG2, McMaster, Mini-FLOTAC and qPCR. PLoS Negl Trop Dis. 2020;14:e0008296.

O’Connell EM, Nutman TB. Molecular diagnostics for soil-transmitted helminths. Am J Trop Med Hyg. 2016;95:508–13.

Kaisar MMM, Brienen EAT, Djuardi Y, Sartono E, Yazdanbakhsh M, Verweij JJ, Supali T, van Lieshout L. Improved diagnosis of Trichuris trichiura by using a bead-beating procedure on ethanol preserved stool samples prior to DNA isolation and the performance of multiplex real-time PCR for intestinal parasites. Parasitology. 2017;144:965–74.

Verweij JJ. Application of PCR-based methods for diagnosis of intestinal parasitic infections in the clinical laboratory. Parasitology. 2014;141:1863–72.

Grant JR, Pilotte N, Williams SA. A case for using genomics and a bioinformatics pipeline to develop sensitive and species-specific PCR-based diagnostics for soil-transmitted helminths. Front Genet. 2019;10:883.

Novák P, Neumann P, Macas J. Global analysis of repetitive DNA from unassembled sequence reads using RepeatExplorer2. Nat Protoc. 2020;15:3745–76.

Doyle SR. Improving helminth genome resources in the post-genomic era. Trends Parasitol. 2022;38:831–40.

Pilotte N, Maasch JRMA, Easton AV, Dahlstrom E, Nutman TB, Williams SA. Targeting a highly repeated germline DNA sequence for improved real-time PCR-based detection of Ascaris infection in human stool. PLoS Negl Trop Dis. 2019;13:e0007593.

Ng SK, Holden L, Sun J. Identifying comorbidity patterns of health conditions via cluster analysis of pairwise concordance statistics. Stat Med. 2012;31:3393–405.

Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74.

Papaiakovou M, Gasser RB, Littlewood DTJ. Quantitative PCR-based diagnosis of soil-transmitted helminth infections: Faecal or Fickle? Trends Parasitol. 2019;35:491–500.

Zendejas-Heredia PA, Colella V, Hii SF, Traub RJ. Comparison of the egg recovery rates and limit of detection for soil-transmitted helminths using the Kato-Katz thick smear, faecal flotation and quantitative real-time PCR in human stool. PLoS Negl Trop Dis. 2021;15:e0009395.

Carter JY. External quality assessment in resource-limited countries. Biochem Med. 2017;27:97–109.

Article   Google Scholar  

Cantera JL, White HN, Forrest MS, Stringer OW, Belizario VY Jr, Storey HL, de Hostos EL, de los Santos T. Sensitive and semiquantitative detection of soil-transmitted helminth infection in stool using a recombinase polymerase amplification-based assay. PLoS Negl Trop Dis. 2021;15:e0009782.

Knopp S, Salim N, Schindler T, Karagiannis Voules DA, Rothen J, Lweno O, Mohammed AS, Singo R, Benninghoff M, Nsojo AA, Genton B, Daubenberger C. Diagnostic accuracy of Kato-Katz, FLOTAC, Baermann, and PCR methods for the detection of light-intensity hookworm and Strongyloides stercoralis infections in Tanzania. Am J Trop Med Hyg. 2014;90:535–45.

Easton AV, Oliveira RG, O’Connell EM, Kepha S, Mwandawiro CS, Njenga SM, Kihara JH, Mwatele C, Odiere MR, Brooker SJ, Webster JP, Anderson RM, Nutman TB. Multi-parallel qPCR provides increased sensitivity and diagnostic breadth for gastrointestinal parasites of humans: field-based inferences on the impact of mass deworming. Parasit Vectors. 2016;9:38.

López-Flores I, Garrido-Ramos MA. The repetitive DNA content of eukaryotic genomes. Genome Dyn. 2012;7:1–28.

Schutte AHJ, Koelewijn R, Ajjampur SSR, Levecke B, McCarthy JS, Mejia R, Williams SA, Verweij JJ, van Lieshout L, van Hellemond JJ. Detection of soil-transmitted helminths and Schistosoma spp. by nucleic acid amplification test: Results of the first 5 years of the only international external quality assessment scheme. PLoS Negl Trop Dis. 2024;18:e0012404.

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Acknowledgements

MP would like to thank Professor Cinzia Cantacessi and Dr. Stephen R Doyle for their continuous support. MP, AJK and RM would like to thank Nicolas R Caro, Marisa Juarez and Paola A. Vargas, for their help with egg collection and logistical support in Orán, Argentina.

This work received financial support from the Coalition for Operational Research on Neglected Tropical Diseases (COR-NTD), which is funded by The Task Force for Global Health primarily by the Bill & Melinda Gates Foundation (OPP1190754), by UK aid from the UK Government, and by the United States Agency for International Development through its Neglected Tropical Diseases Program. M.P. was partly funded by an NHM SIF award (2018) for laboratory work. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. M.P. is a grateful recipient of a Harding Distinguished Postgraduate Scholarship from the University of Cambridge and Cambridge Commonwealth Trust.

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M.P., N.P., S.A.W., R.J., A.K., and R.O. conceived the concept of the pilot study. M.P. conducted the analysis and drafted the original draft of the manuscript. N.P., J.C.D., D.T.J.L., R.O.C., A.K., R.J., and S.A.W. provided valuable feedback on the analysis and draft. All authors approved the final manuscript.

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M.P. was a member of the World Health Organization Diagnostics Technical Advisory Group (WHO DTAG) for Neglected Tropical Diseases (NTDs) between March 2021 and December 2023. The WHO played no role in producing the contents of this short report. All other authors declare no conflict of interest.

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Papaiakovou, M., Cimino, R.O., Pilotte, N. et al. Comparison of multi-parallel quantitative real-time PCRs targeting different DNA regions and detecting soil-transmitted helminths in stool. Parasites Vectors 17 , 390 (2024). https://doi.org/10.1186/s13071-024-06464-6

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