limitations of quantitative research study

How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

Charla Viera, MS

See our "Privacy Policy"

Ensure your structure and ideas are consistent and clearly communicated

Pair your Premium Editing with our add-on service Presubmission Review for an overall assessment of your manuscript.

Limitations and Weaknesses of Quantitative Research

• Post author: Edeh Samuel Chukwuemeka ACMC
• Post published: August 16, 2021
• Post category: Scholarly Articles

Limitations and Weaknesses of Quantitative Research: Research entails the collection of materials for  academic or other purposes. It is a process of gathering information and data to solve or existing problem or prevent future problems. Research works can be done via two methods. Qualitative research or quantitative research.

Qualitative research involves the carrying out of research by gathering non-numerical data. For example, gathering of video evidence, texts or messages for analysis. On the other hand, quantitative research is the process where by numerical data are collected and analyzed. It is effectively used to find patterns and averages as well as generalising a finding or result to a wider population. Quantitative research is mostly used in natural and social sciences such as biology, psychology, economics, among others.

Quantitative research could be carried out using any four methods of researching which are descriptive research, correlational research, experimental research or survey research. In descriptive, one seeks to know the ‘what’ of a thing rather than the ‘why’ of such thing. It tries to describe the various components of an information.

Correlational research involves the research between two variables to ascertain the relationship between the variables. It understudies the impact of one variable on the other. On the other hand, an experimental research is one that uses scientific methods to establish the relationship between groups of variables. That is, it tries to establish a cause-effect relationship between the various variables under study.

Recommended: How to become a good researcher: 5 qualities you need to have.

Finally, the survey research which is most widely used involves the preparation of set questionnaires, interviews and polls to which answers are provided by a segment of the target population and then, conclusions are drawn from such answers given. Survey research studies the relationship between various variables in a given research.

One of the major benefit of  quantitative research method is that it makes one arrive at a well considered conclusion since samples are collected from those who are directly affected by the research. The data collected are majorly converted to a numerical form which aids in statistical analysis. Also, quantitative research is more convenient for projects with scientific and social science inclinations.

Also see: Advantages and Disadvantages of quantitative and qualitative Research

Weaknesses of Quantitative Research

Notwithstanding the benefits of quantitative research, the research method has its own weaknesses and limitations. This is because the method is not applicable and convenient in all cases of research. Thus, using a quantitative research method in a research where qualitative research method should be used will not produce the needed result.

To this end, some of the weaknesses and limitations of quantitative research are highlighted below.

1. It Requires a Large Number of Respondents: In the course of carrying out a quantitative research, recourse has to be made to a large number of respondents. This is because you are sampling a section of a population to get their views, which views will be seen as that of the general population. In doing this, a huge number of respondents have to be consulted so as to get a fair view or percentage of the target population.

For example, if one wishes to carry out a quantitative research in Nigeria as to her acceptance of a policy of the government, one will need to consult wider. This is because Nigeria has a population of over 200 million people and the opinions of a few thousands cannot pass out as that of 200 million people. In the light of this, more respondents will be required to be interviewed so as to enable one get a fair view of the population.

Large number of respondents is thus, one of the weaknesses or limitations of quantitative research as a small sampling of a section of the target population might not be of much help to the research.

2. It is time consuming: Unlike qualitative research which has to do with analysis of already prepared data, quantitative research demands that you source for and collate the data yourself while converting such data collected into a numerical form for proper analysis. This process is time consuming. Again, the task of sending out questionnaires to respondents and waiting for answers to such questionnaires might be time consuming as most respondents will reply late or may not even reply at all.

Great patience is therefore needed in carrying out a quantitative research. It is therefore not always a good method of research in cases of urgencies as the time to get responses might take too long.

Also see: Major characteristics of customary laws

3. It requires huge resources: Quantitative research requires huge investment of time, money and energy. It is time consuming just as it also involve huge financial commitments.

In carrying out quantitative research, one needs to get your questions prepared, sent out and also followed up to ensure that such is answered. Also, some respondents might demand to be paid before giving their inputs to such a research. An example is the trending online surveys in which the target respondents are paid for every survey they carry out for a researcher.

4. Difficulty in Analyzing the Data Collected: Data are collected from respondents and then converted into statistics. This usually poses as a limitation to a researcher who is not an expert in statistics. Analysis of collected data is also demanding and time consuming. A researcher needs to make such information collected into numerical data and correlate them with the larger population. Where this is not properly done, it means that the outcome might be false or misleading.

Also, due to the fact that a researcher might not have control over the environment he is researching in, as any such environment is susceptible to change at any point in time, the outcome of his research might be inconsistent.

Recommended: Problems and criticism of democracy

5. Outcomes of quantitative research is usually limited: In quantitative research, the outcomes are usually limited. This is because the outcome is usually based on what the researcher wants. This limited outcome is due to the structured pattern of the questionnaires. Questionnaires usually have close ended questions which gives a respondent little or no opportunity of explanations. Thus, the answers provided are limited to the questions asked and nothing more.

6. Data outcomes are usually generalised : As noted earlier, quantitative research is usually conducted on a section of a target population and not on the whole population. The outcome of this research is then generalised as the view of the entire population. What this portends is that the views of  few respondents in that research is seen as that of the general populace. Such views from them might be biased or insincere, yet they are seen as that of the entire population.

In the light of this, the fallacy of hasty generalisation is prone to be committed in a quantitative research. Generalisation of the views of a section of the population might not be the best as their views may be biased.

Recommended: Advantages and Disadvantages of Capitalism

In conclusion, quantitative research is a veritable means of conducting research especially in the fields of natural sciences and social sciences. This is because it mostly has a one on one interaction between the researcher and the various respondents as it majorly studies behavior. This advantage notwithstanding, the research method has its own  limitations and weaknesses. These limitations and weaknesses often times affect the quality of a research which is done using the quantitative method of research.

Edeh Samuel Chukwuemeka, ACMC, is a lawyer and a certified mediator/conciliator in Nigeria. He is also a developer with knowledge in various programming languages. Samuel is determined to leverage his skills in technology, SEO, and legal practice to revolutionize the legal profession worldwide by creating web and mobile applications that simplify legal research. Sam is also passionate about educating and providing valuable information to people.

13 Pros and Cons of Quantitative Research Methods

Quantitative research utilizes mathematical, statistical, and computational tools to derive results. This structure creates a conclusiveness to the purposes being studied as it quantifies problems to understand how prevalent they are.

It is through this process that the research creates a projectable result which applies to the larger general population.

Instead of providing a subjective overview like qualitative research offers, quantitative research identifies structured cause-and-effect relationships. Once the problem is identified by those involved in the study, the factors associated with the issue become possible to identify as well. Experiments and surveys are the primary tools of this research method to create specific results, even when independent or interdependent factors are present.

These are the quantitative research pros and cons to consider.

List of the Pros of Quantitative Research

1. Data collection occurs rapidly with quantitative research. Because the data points of quantitative research involve surveys, experiments, and real-time gathering, there are few delays in the collection of materials to examine. That means the information under study can be analyzed very quickly when compared to other research methods. The need to separate systems or identify variables is not as prevalent with this option either.

2. The samples of quantitative research are randomized. Quantitative research uses a randomized process to collect information, preventing bias from entering into the data. This randomness creates an additional advantage in the fact that the information supplied through this research can then be statistically applied to the rest of the population group which is under study. Although there is the possibility that some demographics could be left out despite randomization to create errors when the research is applied to all, the results of this research type make it possible to glean relevant data in a fraction of the time that other methods require.

3. It offers reliable and repeatable information. Quantitative research validates itself by offering consistent results when the same data points are examined under randomized conditions. Although you may receive different percentages or slight variances in other results, repetitive information creates the foundation for certainty in future planning processes. Businesses can tailor their messages or programs based on these results to meet specific needs in their community. The statistics become a reliable resource which offer confidence to the decision-making process.

4. You can generalize your findings with quantitative research. The issue with other research types is that there is no generalization effect possible with the data points they gather. Quantitative information may offer an overview instead of specificity when looking at target groups, but that also makes it possible to identify core subjects, needs, or wants. Every finding developed through this method can go beyond the participant group to the overall demographic being looked at with this work. That makes it possible to identify trouble areas before difficulties have a chance to start.

5. The research is anonymous. Researchers often use quantitative data when looking at sensitive topics because of the anonymity involved. People are not required to identify themselves with specificity in the data collected. Even if surveys or interviews are distributed to each individual, their personal information does not make it to the form. This setup reduces the risk of false results because some research participants are ashamed or disturbed about the subject discussions which involve them.

6. You can perform the research remotely. Quantitative research does not require the participants to report to a specific location to collect the data. You can speak with individuals on the phone, conduct surveys online, or use other remote methods that allow for information to move from one party to the other. Although the number of questions you ask or their difficulty can influence how many people choose to participate, the only real cost factor to the participants involves their time. That can make this option a lot cheaper than other methods.

7. Information from a larger sample is used with quantitative research. Qualitative research must use small sample sizes because it requires in-depth data points to be collected by the researchers. This creates a time-consuming resource, reducing the number of people involved. The structure of quantitative research allows for broader studies to take place, which enables better accuracy when attempting to create generalizations about the subject matter involved. There are fewer variables which can skew the results too because you’re dealing with close-ended information instead of open-ended questions.

List of the Cons of Quantitative Research

1. You cannot follow-up on any answers in quantitative research. Quantitative research offers an important limit: you cannot go back to participants after they’ve filled out a survey if there are more questions to ask. There is a limited chance to probe the answers offered in the research, which creates fewer data points to examine when compared to other methods. There is still the advantage of anonymity, but if a survey offers inconclusive or questionable results, there is no way to verify the validity of the data. If enough participants turn in similar answers, it could skew the data in a way that does not apply to the general population.

2. The characteristics of the participants may not apply to the general population. There is always a risk that the research collected using the quantitative method may not apply to the general population. It is easy to draw false correlations because the information seems to come from random sources. Despite the efforts to prevent bias, the characteristics of any randomized sample are not guaranteed to apply to everyone. That means the only certainty offered using this method is that the data applies to those who choose to participate.

3. You cannot determine if answers are true or not. Researchers using the quantitative method must operate on the assumption that all the answers provided to them through surveys, testing, and experimentation are based on a foundation of truth. There are no face-to-face contacts with this method, which means interviewers or researchers are unable to gauge the truthfulness or authenticity of each result.

A 2011 study published by Psychology Today looked at how often people lie in their daily lives. Participants were asked to talk about the number of lies they told in the past 24 hours. 40% of the sample group reported telling a lie, with the median being 1.65 lies told per day. Over 22% of the lies were told by just 1% of the sample. What would happen if the random sampling came from this 1% group?

4. There is a cost factor to consider with quantitative research. All research involves cost. There’s no getting around this fact. When looking at the price of experiments and research within the quantitative method, a single result mist cost more than $100,000. Even conducting a focus group is costly, with just four groups of government or business participants requiring up to $60,000 for the work to be done. Most of the cost involves the target audiences you want to survey, what the objects happen to be, and if you can do the work online or over the phone.

5. You do not gain access to specific feedback details. Let’s say that you wanted to conduct quantitative research on a new toothpaste that you want to take to the market. This method allows you to explore a specific hypothesis (i.e., this toothpaste does a better job of cleaning teeth than this other product). You can use the statistics to create generalizations (i.e., 70% of people say this toothpaste cleans better, which means that is your potential customer base). What you don’t receive are specific feedback details that can help you refine the product. If no one likes the toothpaste because it tastes like how a skunk smells, that 70% who say it cleans better still won’t purchase the product.

6. It creates the potential for an unnatural environment. When carrying out quantitative research, the efforts are sometimes carried out in environments which are unnatural to the group. When this disadvantage occurs, the results will often differ when compared to what would be discovered with real-world examples. That means researchers can still manipulate the results, even with randomized participants, because of the work within an environment which is conducive to the answers which they want to receive through this method.

These quantitative research pros and cons take a look at the value of the information collected vs. its authenticity and cost to collect. It is cheaper than other research methods, but with its limitations, this option is not always the best choice to make when looking for specific data points before making a critical decision.

10 Advantages & Disadvantages of Quantitative Research

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis.

Quantitative Research

When researchers look at gathering data, there are two types of testing methods they can use: quantitative research, or qualitative research. Quantitative research looks to capture real, measurable data in the form of numbers and figures; whereas qualitative research is concerned with recording opinion data, customer characteristics, and other non-numerical information.

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis. An integral component of quantitative research - and truly, all research - is the careful and considered analysis of the resulting data points.

There are several key advantages and disadvantages to conducting quantitative research that should be considered when deciding which type of testing best fits the occasion.

5 Advantages of Quantitative Research

• Quantitative research is concerned with facts & verifiable information.

Quantitative research is primarily designed to capture numerical data - often for the purpose of studying a fact or phenomenon in their population. This kind of research activity is very helpful for producing data points when looking at a particular group - like a customer demographic. All of this helps us to better identify the key roots of certain customer behaviors. 

Businesses who research their customers intimately often outperform their competitors. Knowing the reasons why a customer makes a particular purchasing decision makes it easier for companies to address issues in their audiences. Data analysis of this kind can be used for a wide range of applications, even outside the world of commerce. 

• Quantitative research can be done anonymously. 

Unlike qualitative research questions - which often ask participants to divulge personal and sometimes sensitive information - quantitative research does not require participants to be named or identified. As long as those conducting the testing are able to independently verify that the participants fit the necessary profile for the test, then more identifying information is unnecessary. 

• Quantitative research processes don't need to be directly observed.

Whereas qualitative research demands close attention be paid to the process of data collection, quantitative research data can be collected passively. Surveys, polls, and other forms of asynchronous data collection generate data points over a defined period of time, freeing up researchers to focus on more important activities. 

• Quantitative research is faster than other methods.

Quantitative research can capture vast amounts of data far quicker than other research activities. The ability to work in real-time allows analysts to immediately begin incorporating new insights and changes into their work - dramatically reducing the turn-around time of their projects. Less delays and a larger sample size ensures you will have a far easier go of managing your data collection process.

• Quantitative research is verifiable and can be used to duplicate results.

The careful and exact way in which quantitative tests must be designed enables other researchers to duplicate the methodology. In order to verify the integrity of any experimental conclusion, others must be able to replicate the study on their own. Independently verifying data is how the scientific community creates precedent and establishes trust in their findings.

5 Disadvantages of Quantitative Research

• Limited to numbers and figures.

Quantitative research is an incredibly precise tool in the way that it only gathers cold hard figures. This double edged sword leaves the quantitative method unable to deal with questions that require specific feedback, and often lacks a human element. For questions like, “What sorts of emotions does our advertisement evoke in our test audiences?” or “Why do customers prefer our product over the competing brand?”, using the quantitative research method will not derive a meaningful answer.

• Testing models are more difficult to create.

Creating a quantitative research model requires careful attention to be paid to your design. From the hypothesis to the testing methods and the analysis that comes after, there are several moving parts that must be brought into alignment in order for your test to succeed. Even one unintentional error can invalidate your results, and send your team back to the drawing board to start all over again.

• Tests can be intentionally manipulative.  

Bad actors looking to push an agenda can sometimes create qualitative tests that are faulty, and designed to support a particular end result. Apolitical facts and figures can be turned political when given a limited context. You can imagine an example in which a politician devises a poll with answers that are designed to give him a favorable outcome - no matter what respondents pick.

• Results are open to subjective interpretation.

Whether due to researchers' bias or simple accident, research data can be manipulated in order to give a subjective result. When numbers are not given their full context, or were gathered in an incorrect or misleading way, the results that follow can not be correctly interpreted. Bias, opinion, and simple mistakes all work to inhibit the experimental process - and must be taken into account when designing your tests. 

• More expensive than other forms of testing. 

Quantitative research often seeks to gather large quantities of data points. While this is beneficial for the purposes of testing, the research does not come free. The grander the scope of your test and the more thorough you are in it’s methodology, the more likely it is that you will be spending a sizable portion of your marketing expenses on research alone. Polling and surveying, while affordable means of gathering quantitative data, can not always generate the kind of quality results a research project necessitates. 

Key Takeaways 

Numerical data is a vital component of almost any research project. Quantitative data can provide meaningful insight into qualitative concerns. Focusing on the facts and figures enables researchers to duplicate tests later on, and create their own data sets.

To streamline your quantitative research process:

Have a plan. Tackling your research project with a clear and focused strategy will allow you to better address any errors or hiccups that might otherwise inhibit your testing. 

Define your audience. Create a clear picture of your target audience before you design your test. Understanding who you want to test beforehand gives you the ability to choose which methodology is going to be the right fit for them. 

Test, test, and test again. Verifying your results through repeated and thorough testing builds confidence in your decision making. It’s not only smart research practice - it’s good business.

About Author

Send Your First Survey Today!

Set up and begin receiving results within minutes. Sign up for free, no contract required.

Helpfull is the easiest way to get feedback from thousands of people in minutes. Our online focus group platform provides a pool of qualified panelists to give you their real detailed opinions helping you make better, more informed decisions.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Korean Med Sci
• v.37(16); 2022 Apr 25

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.

Advantages and Disadvantages of Quantitative Research

• Market Research
• Sustainable Businesses
• Supply Chain Management
• Operations & Technology
• Business Law & Taxes
• Business Insurance
• Business Finance
• Becoming an Owner
• University of Washington
• San Jose State University
• University of California, San Diego

Quantitative research is the process of gathering observable data to answer a research question using statistical , computational, or mathematical techniques. It is often seen as more accurate or valuable than qualitative research, which focuses on gathering non-numerical data.

Qualitative research looks at opinions, concepts, characteristics, and descriptions. Quantitative research looks at measurable, numerical relationships. As with any other process, it's important to recognize the advantages and disadvantages of quantitative research.

How Can Businesses Use Quantitative Research?

Research benefits small businesses by helping you make informed decisions. Conducting market research should be a regular part of any business plan, allowing you to grow efficiently and make good use of your available resources.

Businesses can use research to:

• Learn more about customer opinions and buying patterns .
• Test new products and services before launching them.
• Make decisions about product packaging, branding, and other visual elements.
• Understand patterns in your market or industry.
• Analyze the behavior of your competitors.
• Identify the best use of your marketing resources.
• Compare how successful different promotions will be before scaling up.
• Decide on where new locations or stores should be.

When deciding what type of research will benefit your business, it is important to consider the advantages and disadvantages of quantitative research.

Advantages of Quantitative Research

The use of statistical analysis and hard numbers found in quantitative research has distinct advantages in the research process.

• Can be tested and checked. Quantitative research requires careful experimental design and the ability for anyone to replicate both the test and the results. This makes the data you gather more reliable and less open to argument.
• Straightforward analysis. When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.
• Prestige. Research that involves complex statistics and data analysis is considered valuable and impressive because many people don't understand the mathematics involved. Quantitative research is associated with technical advancements like computer modeling, stock selection, portfolio evaluation, and other data-based business decisions. The association of prestige and value with quantitative research can reflect well on your small business.

Disadvantages of Quantitative Research

However, the focus on numbers found in quantitative research can also be limiting, leading to several disadvantages.

• False focus on numbers. Quantitative research can be limited in its pursuit of concrete, statistical relationships, which can lead to researchers overlooking broader themes and relationships. By focusing solely on numbers, you run the risk of missing surprising or big-picture information that can benefit your business.
• Difficulty setting up a research model. When you conduct quantitative research, you need to carefully develop a hypothesis and set up a model for collecting and analyzing data. Any errors in your set up, bias on the part of the researcher, or mistakes in execution can invalidate all your results. Even coming up with a hypothesis can be subjective, especially if you have a specific question that you already know you want to prove or disprove.
• Can be misleading. Many people assume that because quantitative research is based on statistics it is more credible or scientific than observational, qualitative research. However, both kinds of research can be subjective and misleading. The opinions and biases of a researcher are just as likely to impact quantitative approaches to information gathering. In fact, the impact of this bias occurs earlier in the process of quantitative research than it does in qualitative research.

Tips for Conducting Quantitative Research

If you decide to conduct quantitative research for your small business,

• Work with a professional. Professional market researchers and data analysts are trained in how to conduct survey research and run statistical models. To ensure that your research is well-designed and your results are accurate, work with a professional. If you can't afford to hire researchers for the length of the project, look for someone who can help just with set-up or analysis.
• Have a clear research question. To save time and resources, have a clear idea of what question you want answered before you begin researching. You can find areas that need research by looking at your marketing plan and identifying where you struggle to make an informed decision.
• Don't be afraid to change your model. Research is a process, and needing to change direction or start over doesn't mean you have failed or done something wrong. Often, successful research will raise new questions. Keep track of those new questions so that you can continue answering them as you move forward.
• Combine quantitative and qualitative research. Successfully running a small business relies on understanding people, and the behavior of your customers and competitors cannot be reduced to numbers. As you conduct quantitative research, try to collect qualitative data as well. This can take the form of open-ended questions on surveys, panel discussions, or even just keeping track of opinions or concerns that customers share. By combining the two types of research, you'll end up with the best possible picture of how your business can grow and succeed within its market.
• Deductive and Inductive Market Research
• Qualitative or Quantitative? How to Choose a Method for Your Survey
• Qualitative and Quantitative Research Methods
• Establishing Trustworthiness in Qualitative Research
• How to Conduct Market Research Surveys
• Market Research 101: Develop the Research Plan
• What Is a Market Research Focus Group?
• Quantitative and Qualitative Segmentation Research
• Surveys Research: Confidence Intervals and Levels
• How to Analyze Interview Data and Survey Responses
• Learn About Using Cross Tabs in Quantitative Research
• How to Measure Brand Equity
• Market Research 101: Data Analysis
• Learn About Syndicated Market Research
• What Is Market Research?
• How Market Researchers Choose Which Movies You See

• Privacy Policy

Home » Limitations in Research – Types, Examples and Writing Guide

Limitations in Research – Types, Examples and Writing Guide

Table of Contents

Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

• The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
• The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
• The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

• The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
• The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
• The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

• The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
• The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
• The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

• Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
• Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
• Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
• Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
• Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

• To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
• To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
• To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
• To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
• To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

• Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
• Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
• Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
• Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

• Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
• Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
• Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
• Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
• Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Thesis Statement – Examples, Writing Guide

Purpose of Research – Objectives and Applications

Dissertation – Format, Example and Template

Problem Statement – Writing Guide, Examples and...

Research Findings – Types Examples and Writing...

Research Summary – Structure, Examples and...

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

Prevent plagiarism. Run a free check.

Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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
• 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 .

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is Quantitative Research? | Definition, Uses & Methods. Scribbr. Retrieved September 4, 2024, from https://www.scribbr.com/methodology/quantitative-research/

Is this article helpful?

Pritha Bhandari

Other students also liked, descriptive statistics | definitions, types, examples, inferential statistics | an easy introduction & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

• 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

• << Previous: Qualitative Methods
• Next: Insiderness >>
• Last Updated: Sep 4, 2024 9:40 AM
• URL: https://libguides.usc.edu/writingguide

The Limitations of Quantitative Research on Product Innovation

Meticulous user research is your key to success. But is your research methodology telling the full story?

Evidence of quantitative data is highly prevalent in our lives. Personalized algorithms on YouTube and Netflix churn out one stellar recommendation after another, keeping us hooked.

It’s important to remember though that the biggest (and best) companies invest in quantitative and qualitative research . Each methodology uncovers different insights about users. 

A recent clamor for data scientists indicates that industries lean heavily on quantitative research. But can an over-reliance on this method stifle product innovation?

Rewind to before the pandemic hit, circa 2020. Educ8, a fictional tech company, was developing an online classroom application. The app aimed to eliminate the need for teachers and pupils to be in the same room. Suddenly, COVID-19 became rampant across the globe, forcing everyone to stay home. Although the app was incomplete, Educ8 sensed an opportunity to accelerate the rollout of their product.

What are the Pitfalls of Quantitative Research ?

The fictional Educ8 decided to conduct user research to refine its product before launch. The research team proceeded with quantitative methods. Steeped in statistics, it’s often perceived as more “ scientific and reliable .” They rolled out a survey to various students, schools and colleges in their home state. It included a link to a beta version of the app, accompanied by a survey where respondents had to rate various aspects of the app on a scale of 1 to 5 (easily quantifiable).

The survey revealed that over 70% of respondents accessed the app through their personal computers. Educ8 used this as the basis for further development of their app — they catered the experience more toward desktop users. They used an unrepresentative sample to make inferences about its users. A colossal mistake — instead of their app being accessible to all, Educ8 neglected to make the app user-friendly on mobiles. They forgot about children in developing nations, who would access the app from their parents’ smartphones.

Scientific, but not reliable. Any errors in the experimental setup (like Educ8, above) or researcher biases can render the results of a study invalid.

Quantitative research aims to support or refute a hypothesis with mathematical logic. Researchers at Educ8 theorized that pupils would score similar on tests using their platform versus in-class sessions (pre-pandemic). Students were given a test after using the app to see how well they retained taught material. Researchers were looking for statistical significance, the likelihood that the data represents the actual picture, or whether results are due to chance. For their study to be statistically significant , Educ8 needed (a) a large number of responses (greater than 30), and even then, (b) respondents may have provided incorrect or incomplete information or (c) the survey may have been biased. The research team at Educ8 could not wholeheartedly rely on the survey results, as responses likely did not represent the population as a whole. 

Researchers at Educ8 noticed that their beta app is rated decently well, but an in-class voting feature received a poor score, with an average of 1.2 out of 5. Quantitative data has helped them identify a problem. Now what? 

As their responses were limited to assigning a score, respondents couldn’t explain why they rated this feature so poorly. Quantitative data is descriptive , but we encounter difficulty interpreting the results meaningfully.

Without qualitative research data , product teams at Educ8 were in the dark about what to modify about this feature. Future errors in development can then arise from a lack of clarity. 

What are the Pitfalls of Mixed-Method Research ?

Consequently, Educ8 decided to employ mixed-method research — a complementary approach that seemingly provides better results than one procedure in isolation. It is an extension of quant and qual research rather than choosing just one. Researchers at Educ8 refined the survey by including open-ended questions in addition to purely numerical ones. 

It is vital to consider what insights you gain by employing mixed-method research. Sale et al identify the two as different paradigms, each studying differing phenomena. Quantitative researchers look at the truth objectively and view it as separate from the observer, whereas qualitative researchers look at reality through peoples’ experiences, a constantly changing reality.

“If we increase the price of Educ8 by 20%, what will be the impact on customer retention?” is a clear quantitative question.

“What aspects of the in-classroom environment do students and teachers alike need replicated in the application?” is a more open-ended, qualitative question with no definitive answer. The authors in the study linked above argue that mixing the two diminishes the value of both methods.

Driscoll et al found that when rich qualitative data is enumerated or coded into quantitative data or variables, it suffers a loss in depth and flexibility, rendering it “one-dimensional and immutable.” Imagine trying to quantify the nerves students feel when they give a presentation to their peers. 

Several barriers exist to the widespread adoption of the mixed-method research approach:

• Data integration & time required – making the two types of data compatible ( coding ) while answering the original research question is challenging. Researchers need a lot of time to process and make sense of both data types. Teams at Educ8 had to pore over qualitative responses to understand what elements of a regular classroom are missing from their app.
• Data access & rigor – researchers need to consider what data they have access to and the reliability and validity of their data throughout the process. A respondent gave the voting feature a score of 4 (out of 5). However, they were highly critical in the free response question. How does the researcher reconcile the two?
• Researcher skills, knowledge & methodology – researchers may not be highly proficient in both methodologies, i.e. their skills may be better suited to one. Researchers construct qualitative studies. Thus, coding qualitative data can be highly subjective and vary from one person to another. 
• Budgetary constraints – all companies have finite resources (people, time and money) and must decide how to allocate these.

What does this all mean for a company like Educ8? The researchers would need to decide what question is most urgent. They would then choose the research tactic that best answers that specific question. In this hypothetical world, the voting feature was rated unfavorably after the first survey. Researchers at Educ8 sent a follow-up, open-ended questionnaire to understand better understand the students’ experience.

How to Unlock Valuable Insights from Qualitative Research

Educ8 researchers found themselves at a stage where they wanted to understand more about their users and their interaction with their app. Quantitative research tends to be explanatory , a snapshot of a phenomenon. For example, the survey revealed that 90% of respondents had a largely favorable experience using the Educ8 app. Or that a majority of them rated the voting system poorly. 

Most exploratory research is qualitative . Qualitative research aims to discover and unearth user problems, investigate why they face difficulty, and understand how to remedy the situation. Testing is typically carried out with 6 to 8 users in small focus groups to enable a rich understanding of user experience and pain points. The qualitative study reveals why Educ8’s voting feature is confusing and tough to navigate – students are overwhelmed by the number of steps required to vote. This insight equipped the product team with the knowledge to improve app functionality. 

Another benefit of using qualitative research? It can provide near-instantaneous results, saving time in the process. Qualitative studies do not need statistical significance – you only need a few responses to understand a user problem. 

Imagine a small town evaluating road safety at a four-way intersection. Only a few instances of road accidents identify that it is a problem. They resolve to install stop signs in all directions so that travelers proceed cautiously. Problem solved. Similarly, Educ8 need not interview 30 people to identify issues with their online voting feature – if 5 out of 8 people find it confusing to navigate, chances are, this extends to the wider population too. 

Faster insights enable Educ8 to refine its voting system and roll out enhancements to make students’ and teachers’ lives easier. This can be the difference between retaining customers or losing them to a competitor such as Google Classroom.

User Research Should Drive Your Product Roadmap

Meticulous user research is the key behind all successful products. And the best research roadmaps combine quantitative and qualitative data to give you a clear landscape of what your customers really need.

Companies with an over-reliance on quantitative research can miss underlying reasons for user problems and behavior. Researchers focus on testing theories rather than generating new ones or getting to the bottom of user issues. Purely quantitative methods lack the detail that explains the numbers.

Ideally, Educ8 would have conducted copious amounts of quantitative and qualitative research, ensuring that their app was fully equipped to hit the market. However, the requirement dramatically changed with the landscape — suddenly, it went from being a studying supplement to being the only method available to students.

Moreover, companies do not have infinite time and resources to conduct research. Knowing the research questions you need answers to, what stage of product development you’re at, and your resource limitations helps you decide what methodology to use. 

All forms of research reveal more about customers, so you need to be clear about your objectives when you begin. Successful studies continually generate new questions — which means the research wheel never stops turning!

Want to learn more about the bright future of qualitative research? Watch Why Qualitative Research is the Linchpin of Better AI to learn the principles behind building digital experiences that transform our lives for the better.

Photo by Joshua Sortino on Unsplash

Categories:

• Leading the Way
• Marvin Customers
• Marvin Events
• Talk to Your Users
• Product Leadership
• Qualitative Research Platform
• User Research

Related Articles

• (Top 8) Best Qualitative Data Analysis Software

Where AI Innovation & Qualitative Research Collide: An Expert’s Take

From User Insights to Action: How AI Makes Qualitative Research Powerful

• Krish Arora
• Latest posts

Krish Arora leverages his experience as a finance professional to turn data into insights. A passionate writer with a strong appreciation for language, Krish crafts compelling stories with numbers and words to elevate the practice of user research.

• 6 Transcription Software For Qualitative Research
• Your Complete Guide to Product Management Customer Feedback
• (6 Top Picks) Survey Analysis Software - Complete Guide

withemes on instagram

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Diving Deeper into Limitations and Delimitations

If you are working on a thesis, dissertation, or other formal research project, chances are your advisor or committee will ask you to address the delimitations of your study. When faced with this request, many students respond with a puzzled look and then go on to address what are actually the study’s limitations.

In a previous article , we covered what goes into the limitations, delimitations, and assumptions sections of your thesis or dissertation. Here, we will dive a bit deeper into the differences between limitations and delimitations and provide some helpful tips for addressing them in your research project—whether you are working on a quantitative or qualitative study.

Acknowledging Weaknesses vs. Defining Boundaries

These concepts are easy to get confused because both limitations and delimitations restrict (or limit) the questions you’ll be able to answer with your study, most notably in terms of generalizability.

However, the biggest difference between limitations and delimitations is the degree of control you have over them—that is, how much they are based in conscious, intentional choices you made in designing your study.

Limitations occur in all types of research and are, for the most part, outside the researcher’s control (given practical constraints, such as time, funding, and access to populations of interest). They are threats to the study’s internal or external validity.

Limitations may include things such as participant drop-out, a sample that isn’t entirely representative of the desired population, violations to the assumptions of parametric analysis (e.g., normality, homogeneity of variance), the limits of self-report, or the absence of reliability and validity data for some of your survey measures.

Limitations can get in the way of your being able to answer certain questions or draw certain types of inferences from your findings. Therefore, it’s important to acknowledge them upfront and make note of how they restrict the conclusions you’ll be able to draw from your study. Frequently, limitations can get in the way of our ability to generalize our findings to the larger populations or to draw causal conclusions, so be sure to consider these issues when you’re thinking about the potential limitations of your study.

Delimitations are also factors that can restrict the questions you can answer or the inferences you can draw from your findings. However, they are based on intentional choices you make a priori (i.e., as you’re designing the study) about where you’re going to draw the boundaries of your project. In other words, they define the project’s scope.

Like limitations, delimitations are a part of every research project, and this is not a bad thing. In fact, it’s very important! You can’t study everything at once. If you try to do so, your project is bound to get huge and unwieldy, and it will become a lot more difficult to interpret your results or come to meaningful conclusions with so many moving parts. You have to draw the line somewhere, and the delimitations are where you choose to draw these lines.

One of the clearest examples of a delimitation that applies to almost every research project is participant exclusion criteria. In conducting either a quantitative or a qualitative study, you will have to define your population of interest. Defining this population of interest means that you will need to articulate the boundaries of that population (i.e., who is not included). Those boundaries are delimitations.

For example, if you’re interested in understanding the experiences of elementary school teachers who have been implementing a new curriculum into their classrooms, you probably won’t be interviewing or sending a survey to any of the following people: non-teachers, high-school teachers, college professors, principals, parents of elementary school children, or the children themselves. Furthermore, you probably won’t be talking to elementary school teachers who have not yet had the experience of implementing the curriculum in question. You would probably only choose to gather data from elementary school teachers who have had this experience because that is who you’re interested in for the purposes of your study. Perhaps you’ll narrow your focus even more to elementary school teachers in a particular school district who have been teaching for a particular length of time. The possibilities can go on. These are choices you will need to make, both for practical reasons (i.e., the population you have access to) and for the questions you are trying to answer.

Of course, for this particular example, this does not mean that it wouldn’t be interesting to also know what principals think about the new curriculum. Or parents. Or elementary school children. It just means that, for the purposes of your project and your research questions, you’re interested in the experience of the teachers, so you’re excluding anyone who does not meet those criteria. Having delimitations to your population of interest also means that you won’t be able to answer any questions about the experiences of those other populations; this is ok because those populations are outside of the scope of your project . As interesting as their experiences might be, you can save these questions for another study. That is the part of the beauty of research: there will always be more studies to do, more questions to ask. You don’t have to (and can’t) do it all in one project.

Continuing with the previous example, for instance, let’s suppose that the problem you are most interested in addressing is the fact that we know relatively little about elementary school teachers’ experiences of implementing a new curriculum. Perhaps you believe that knowing more about teachers’ experiences could inform their training or help administrators know more about how to support their teachers. If the identified problem is our lack of knowledge about teachers’ experiences, and your research questions focus on better understanding these experiences, that means that you are choosing not to focus on other problems or questions, even those that may seem closely related. For instance, you are not asking how effective the new curriculum is in improving student test scores or graduation rates. You might think that would be a very interesting question, but it will have to wait for another study. In narrowing the focus of your research questions, you limit your ability to answer other questions, and again, that’s ok. These other questions may be interesting and important, but, again, they are beyond the scope of your project .

Common Examples of Limitations

While each study will have its own unique set of limitations, some limitations are more common in quantitative research, and others are more common in qualitative research.

In quantitative research, common limitations include the following:

– Participant dropout

– Small sample size, low power

– Non-representative sample

– Violations of statistical assumptions

– Non-experimental design, lack of manipulation of variables, lack of controls

– Potential confounding variables

– Measures with low (or unknown) reliability or validity

– Limits of an instrument to measure the construct of interest

– Data collection methods (e.g., self-report)

– Anything else that might limit the study’s internal or external validity

In qualitative research, common limitations include the following:

– Lack of generalizability of findings (not the goal of qualitative research, but still worth mentioning as a limitation)

– Inability to draw causal conclusions (again, not the goal of qualitative research, but still worth mentioning)

– Researcher bias/subjectivity (especially if there is only one coder)

– Limitations in participants’ ability/willingness to share or describe their experiences

– Any factors that might limit the rigor of data collection or analysis procedures

Common Examples of Delimitations

As noted above, the two most common sources of delimitations in both quantitative and qualitative research include the following:

– Inclusion/exclusion criteria (or how you define your population of interest)

– Research questions or problems you’ve chosen to examine

Several other common sources of delimitations include the following:

– Theoretical framework or perspective adopted

– Methodological framework or paradigm chosen (e.g., quantitative, qualitative, or mixed-methods)

– In quantitative research, the variables you’ve chosen to measure or manipulate (as opposed to others)

Whether you’re conducting a quantitative or qualitative study, you will (hopefully!) have chosen your research design because it is well suited to the questions you’re hoping to answer. Because these questions define the boundaries or scope of your project and thus point to its delimitations, your research design itself will also be related to these delimitations.

Questions to Ask Yourself

As you are considering the limitations and delimitations of your project, it can be helpful to ask yourself a few different questions.

Questions to help point out your study’s limitations :

1. If I had an unlimited budget, unlimited amounts of time, access to all possible populations, and the ability to manipulate as many variables as I wanted, how would I design my study differently to be better able to answer the questions I want to answer? (The ways in which your study falls short of this will point to its limitations.)

2. Are there design issues that get in the way of my being able to draw causal conclusions?

3. Are there sampling issues that get in the way of my being able to generalize my findings?

4. Are there issues related to the measures I’m using or the methods I’m using to collect data? Do I have concerns about participants telling the truth or being able to provide accurate responses to my questions?

5. Are there any other factors that might limit my study’s internal or external validity?

Questions that help point out your study’s delimitations :

1. What are my exclusion criteria? Who did I not include in my study, and why did I make this choice?

2. What questions did I choose not to address in my study? (Of course, the possibilities are endless here, but consider related questions that you chose not to address.)

3. In what ways did I narrow the scope of my study in order to hone in on a particular issue or question?

4. What other methodologies did I not use that might have allowed me to answer slightly different questions about the same topic?

How to Write About Limitations and Delimitations

Remember, having limitations and delimitations is not a bad thing. They’re present in even the most rigorous research. The important thing is to be aware of them and to acknowledge how they may impact your findings or the conclusions you can draw.

In fact, writing about them and acknowledging them gives you an opportunity to demonstrate that you can think critically about these aspects of your study and how they impact your findings, even if they were out of your control.

Keep in mind that your study’s limitations will likely point to important directions for future research. Therefore, when you’re getting ready to write about your recommendations for future research in your discussion, remember to refer back to your limitations section!

As you write about your delimitations in particular, remember that they are not weaknesses, and you don’t have to apologize for them. Good, strong research projects have clear boundaries. Also, keep in mind that you are the researcher and you can choose whatever delimitations you want for your study. You’re in control of the delimitations. You just have to be prepared—both in your discussion section and in your dissertation defense itself—to justify the choices you make and acknowledge how these choices impact your findings.

Browse More on PhDStudent

Everything You Need to Know About References and Citations: Part 1

When you conduct your research, it is important to record the details of all the information you find to provide accurate references, …

How to Write a Proposal: For a Master’s Thesis or Dissertation

Note: Many thanks to fellow PhDStudent blogger Ryan Krone for his contributions and insight to this post. Your thesis/dissertation proposal provides an …

How to Find Free Money for Graduate School Part 2

Getting into graduate school is already a challenge on its own, and funding the program once admitted is even harder. Graduate studies …

How to Find Free Money for Graduate School

You’ve finally earned your Bachelor’s degree and have made it into graduate school. Whether you already have massive student loans from undergrad …

Part 3 of How to Pick Your Defense Committee

  What strategies can a doctoral student employ to maneuver the trials and tribulations of a dissertation committee? In Part 2, we …

Best Dissertation Proofreading and Editing Tips to Make Your Work Spotless

You’ve heard this statement many times before: “the dissertation is the most important project you’ve ever worked on.” That may sound like …

Part 2 of How to Pick Your Defense Committee

Choosing a committee can be a daunting task for a doctoral student.  We’ve already covered two strategies that can help you through this …

Part 1 of How to Pick Your Defense Committee

So you’re ready to pick your committee members; there are a few things to keep in mind first—after all, it is a …

Intro To Series on How to Pick Your Defense Committee

Choosing the right defense committee can potentially be the difference between a smooth transition of receiving your doctoral degree or dodging bullets …

On Babies and Dissertations: Part 3

I recently had the experience of expecting my first baby a month before I graduated. Throughout the process, I accidentally learned several …

On Babies and Dissertations: Part 2

On Babies and Dissertations: Part 1

I recently had the experience of expecting my first baby a month before I graduated and accidentally learned tips on graduating on time with a …

Click here to cancel reply.

You must be logged in to post a comment.

Copyright © 2024 PhDStudent.com. All rights reserved. Designed by Divergent Web Solutions, LLC .

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Quantitative Research Methods: Maximizing Benefits, Addressing Limitations, and Advancing Methodological Frontiers

2024, ISRG Journal of Multidisciplinary Studies (ISRGJMS)

This research paper offers a thorough examination of the benefits and drawbacks of applying quantitative methods to research in a range of academic fields. The precision, objectivity, and capacity to extrapolate results to larger populations are all provided by quantitative methods. They do, however, also bring with them certain difficulties, such as the need to ensure the validity and reliability of data collection processes and to capture subtle qualitative aspects of phenomena. Through a critical analysis of the advantages and disadvantages of quantitative methods, researchers are better equipped to choose their research approaches. Researchers are advised to carefully evaluate research questions, validation methods, and ethical guidelines. To further the field of research methodology, future research directions should concentrate on interdisciplinary collaboration, creative methodologies, and integration of quantitative and qualitative approaches.

Related Papers

Fernando Almeida

Scientific research adopts qualitative and quantitative methodologies in the modeling and analysis of numerous phenomena. The qualitative methodology intends to understand a complex reality and the meaning of actions in a given context. On the other hand, the quantitative methodology seeks to obtain accurate and reliable measurements that allow a statistical analysis. Both methodologies offer a set of methods, potentialities and limitations that must be explored and known by researchers. This paper concisely maps a total of seven qualitative methods and five quantitative methods. A comparative analysis of the most relevant and adopted methods is done to understand the main strengths and limitations of them. Additionally, the work developed intends to be a fundamental reference for the accomplishment of a research study, in which the researcher intends to adopt a qualitative or quantitative methodology. Through the analysis of the advantages and disadvantages of each method, it becomes possible to formulate a more accurate, informed and complete choice.

iosrjournals.org

Gregoire Nleme

In this paper, as a practitioner, I describe quantitative and qualitative inquiries. I explain some of their differences and similarities. I emphasize most important differences that researchers should consider when selecting one of the methods or when running a mixed study. I list differences in worldviews, research design, research processes, reliability assurance, and validity assurance. I propose a process flowchart for each type of inquiry. The purpose of the essay is to give researchers and primarily doctorate students a review of differences and similarities between the two methods of inquiry.

Forum Qualitative Sozialforschung Forum Qualitative Social Research

Margrit Schreier

Journal of Biosocial Science

Daniel Sellen

Kerrin Myres

Qualitative & Multi-Method Research

Hein Goemans

In this brief essay I will elaborate on some of the points I raised at last year&#39;s APSA panel on multi-method work. As always, I emphasize the essential complementarity of different methods. I first briefly discuss why qualitative research and formal models have much to offer each other and why scholars in each methodological tradition can gain much from a better understanding of the other tradition. I then shift to a focus on the overlooked link between qualitative and quantitative research, to argue for a reconsideration of the requirements of the inputs of quantitative research: What constitutes good data? I close with a proposal that calls for collaboration between qualitative and quantitative researchers to set new standards for the collection and use of large-N datasets.

Canadian journal of psychiatry. Revue canadienne de psychiatrie

David Streiner

Qualitative research consists of methods that allow for a more in-depth understanding of phenomena and encompasses techniques such as focus groups, in-depth interviews, and participant observation. The guidelines that pertain to sampling and analysis are different from those which govern quantitative techniques, but they can be applied just as rigorously to ensure the validity of the results. This article introduces these methods and criteria and illustrates how qualitative and quantitative methods can be combined in order to improve what is learned from each.

Pattamawan Jimarkon

Much of the literature on research in applied linguistics views quantitative and qualitative research as distinct entities embodying contrasting philosophies. In this paper, however, we present an example of how preliminary quantitative analyses of data can inform a qualitative discourse analysis study. Data from an online discussion forum concerning the Thai political crisis of 2010 were initially analysed quantitatively to identify keywords, word clusters, length of postings and user ratings of postings for the contributions from the opposing political factions. Each posting was also rated for level of antagonism and credibility of argumentation. These quantitative data provide an overview of the discussion forum and the patterns of discussion within it which was then used as a framework to guide the main qualitative analysis ensuring that the key revealed meanings and functions were covered in the analysis and reducing potential bias in data analysis and presentation. Introduction Mixed methods research has a long history in disciplines that attempt to explain behavior and social phenomena (Dörnyei, 2007). The practice includes a mix or qualitative and quantitative methods, a mix of quantitative methods or a mix of qualitative methods. The type of mixed methods approach that is most popular and is increasingly employed is the first, the mix of the two, which are often based on different research paradigms. Single method research is normally criticised by their opposition as inferior and insufficient. In a pure quantitative study, with the focus on theory or hypothesis testing, the researcher may not be sensitive on contextual details. Moreover, it requires a large amount of data to be able to give an effective ground. A qualitative method, on the other hand, is prone to high subjectivity of the researcher and is unlikely to be generalisable. It can only deal with a small size of data, which makes decision making of the overview and conclusive deduction an ordeal. Four models of mixed methods design are proposed including concurrent design, explanatory sequential design, exploratory sequential design and embedded sequential/concurrent designs (Creswell & Zhang, 2009). The first model, the concurrent design compares and contrasts the results between the two methods to present evidence. Second, the explanatory sequential design utilises the explanation of one set of results to support the other's. In an exploratory sequential design, one method's results are followed by the other's to strengthen the claims made, in the name of generalisability, for instance. In the last design, embedded sequential/concurrent design, a small database is made part of the big database and is used to experiment or enhance the major findings. Typically, a mixed method research deals with different types of data but it may also mean applying different methods to investigate the same data. Two dimensions of advancements that mixed methods data analyses (MMDA) have to offer can be considered as 1) the design virtue and b) research expertise. The research virtue obtained from mixed methods may refer to the strategies that are used to display trustworthiness of the research such as triangulation, complementarity, development, initiation and expansion (Green et al, 1989). In greater details, four major advantages of MMDA have been put forward (Dörnyei, 2007). First, oversimplification, decontexualisation and reduction of the quantitative analysis can be disputed by in depth meaningful qualitative analysis, while content-specificity and unrepresentativeness can be overcome by the generalisable quantitative analysis. Second, for multi-level analyses, MMDA can add more meaning by converging numbers into words and vice versa. Third, triangulation through multi-methods analyses means increasing validity of the study of the results. Fourth, a study with mixed methods analyses tends to attract more attention from the

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Tracie Seidelman

Zelo Getachew

manos savvakis

Melanie DiLoreto

Constantinos Phellas

Dr Muhibul Haq

Tshidi M Wyllie

Tshidi M Wyllie,Ph.D.

Health Education Quarterly

David Buchanan

Katrin Niglas

Health Education …

Kenneth McLeroy

M.Lib.I.Sc. Project, Panjab University, under guidance of Dr. Shiv Kumar

SUBHAJIT PANDA

International Journal of Research

Enas Abuhamda , Islam Asim Ismail

UNICAF University - Zambia

Ivan Steenkamp

Seng Li Kareng

Munyaradzi Moyo

Ziabur Rahman

Cognizance journal

sajeeb shrestha

Journal of Evidence Based Medicine and Healthcare

Anshika Srivastava

Karin Johansson Blight, PhD

IRJET Journal

Victor Willson

Quantitative Research Methods and Means of Analysis

Dino Bozonelos

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

The strengths and weaknesses of quantitative and qualitative research: what method for nursing?

Affiliation.

• 1 Department of Professional Development, Wealden College of Health and Social Studies, East Surrey Hospital, Redhill, Surrey, England.
• PMID: 7822608
• DOI: 10.1046/j.1365-2648.1994.20040716.x

The overall purpose of research for any profession is to discover the truth of the discipline. This paper examines the controversy over the methods by which truth is obtained, by examining the differences and similarities between quantitative and qualitative research. The historically negative bias against qualitative research is discussed, as well as the strengths and weaknesses of both approaches, with issues highlighted by reference to nursing research. Consideration is given to issues of sampling; the relationship between the researcher and subject; methodologies and collated data; validity; reliability, and ethical dilemmas. The author identifies that neither approach is superior to the other; qualitative research appears invaluable for the exploration of subjective experiences of patients and nurses, and quantitative methods facilitate the discovery of quantifiable information. Combining the strengths of both approaches in triangulation, if time and money permit, is also proposed as a valuable means of discovering the truth about nursing. It is argued that if nursing scholars limit themselves to one method of enquiry, restrictions will be placed on the development of nursing knowledge.

PubMed Disclaimer

Similar articles

• Reliability and validity in research. Roberts P, Priest H. Roberts P, et al. Nurs Stand. 2006 Jul 12-18;20(44):41-5. doi: 10.7748/ns2006.07.20.44.41.c6560. Nurs Stand. 2006. PMID: 16872117 Review.
• The value of combining qualitative and quantitative approaches in nursing research by means of method triangulation. Foss C, Ellefsen B. Foss C, et al. J Adv Nurs. 2002 Oct;40(2):242-8. doi: 10.1046/j.1365-2648.2002.02366.x. J Adv Nurs. 2002. PMID: 12366654
• Case study: design? Method? Or comprehensive strategy? Jones C, Lyons C. Jones C, et al. Nurse Res. 2004;11(3):70-6. doi: 10.7748/nr2004.04.11.3.70.c6206. Nurse Res. 2004. PMID: 15065485 Review.
• Triangulation as a method for contemporary nursing research. Halcomb E, Andrew S. Halcomb E, et al. Nurse Res. 2005;13(2):71-82. doi: 10.7748/nr.13.2.71.s8. Nurse Res. 2005. PMID: 16416981 Review.
• Mixed methods research for the novice researcher. Giddings LS, Grant BM. Giddings LS, et al. Contemp Nurse. 2006 Oct;23(1):3-11. doi: 10.5172/conu.2006.23.1.3. Contemp Nurse. 2006. PMID: 17083315 Review.
• Development of a Culturally Adapted Dietary Intervention to Reduce Alzheimer's Disease Risk among Older Black Adults. Shaw AR, Key MN, Fikru S, Lofton S, Sullivan DK, Berkley-Patton J, Glover CM, Burns JM, Vidoni ED. Shaw AR, et al. Int J Environ Res Public Health. 2023 Sep 2;20(17):6705. doi: 10.3390/ijerph20176705. Int J Environ Res Public Health. 2023. PMID: 37681845 Free PMC article.
• Driving forces of GPs' migration in Europe: an exploratory qualitative study. Velgan M, Vanderheyde T, Kalda R, Michels N. Velgan M, et al. BJGP Open. 2023 Jun 27;7(2):BJGPO.2022.0132. doi: 10.3399/BJGPO.2022.0132. Print 2023 Jun. BJGP Open. 2023. PMID: 36717117 Free PMC article.
• The influence of patriarchy on Nepali-speaking Bhutanese women's diabetes self-management. Sharma A, Stuckey H, Mendez-Miller M, Cuffee Y, Juris AJ, McCall-Hosenfeld JS. Sharma A, et al. PLoS One. 2022 Sep 14;17(9):e0268559. doi: 10.1371/journal.pone.0268559. eCollection 2022. PLoS One. 2022. PMID: 36103470 Free PMC article.
• User Perceptions of Different Vital Signs Monitor Modalities During High-Fidelity Simulation: Semiquantitative Analysis. Akbas S, Said S, Roche TR, Nöthiger CB, Spahn DR, Tscholl DW, Bergauer L. Akbas S, et al. JMIR Hum Factors. 2022 Mar 18;9(1):e34677. doi: 10.2196/34677. JMIR Hum Factors. 2022. PMID: 35119375 Free PMC article.
• Numbers and narratives: how qualitative methods can strengthen the science of paediatric antimicrobial stewardship. Woods-Hill CZ, Xie A, Lin J, Wolfe HA, Plattner AS, Malone S, Chiotos K, Szymczak JE. Woods-Hill CZ, et al. JAC Antimicrob Resist. 2022 Jan 22;4(1):dlab195. doi: 10.1093/jacamr/dlab195. eCollection 2022 Mar. JAC Antimicrob Resist. 2022. PMID: 35098126 Free PMC article. Review.

Publication types

• Search in MeSH

LinkOut - more resources

Full text sources.

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

André Queirós Higher Polytechnic Institute of Gaya, V. N. Gaia, Portugal

Daniel Faria Higher Polytechnic Institute of Gaya, V. N. Gaia, Portugal

Fernando Almeida Faculty of Engineering of Oporto University, INESC TEC, Porto, Portugal

.................................................

..................................................

Education Journals

European Journal Of Physical Education and Sport Science

European Journal of Foreign Language Teaching

European Journal of English Language Teaching

European Journal of Special Education Research

European Journal of Alternative Education Studies

European Journal of Open Education and E-learning Studies

Public Health Journals

European Journal of Public Health Studies

European Journal of Fitness, Nutrition and Sport Medicine Studies

European Journal of Physiotherapy and Rehabilitation Studies

Social Sciences Journals

European Journal of Social Sciences Studies

European Journal of Economic and Financial Research

European Journal of Management and Marketing Studies

European Journal of Human Resource Management Studies

European Journal of Political Science Studies

Literature, Language and Linguistics Journals

European Journal of Literature, Language and Linguistics Studies

European Journal of Literary Studies

European Journal of Applied Linguistics Studies

European Journal of Multilingualism and Translation Studies

...................................................

• Other Journals
• ##Editorial Board##
• ##Indexing and Abstracting##
• ##Author's guidelines##
• ##Covered Research Areas##
• ##Announcements##
• ##Related Journals##
• ##Manuscript Submission##

STRENGTHS AND LIMITATIONS OF QUALITATIVE AND QUANTITATIVE RESEARCH METHODS

Scientific research adopts qualitative and quantitative methodologies in the modeling and analysis of numerous phenomena. The qualitative methodology intends to understand a complex reality and the meaning of actions in a given context. On the other hand, the quantitative methodology seeks to obtain accurate and reliable measurements that allow a statistical analysis. Both methodologies offer a set of methods, potentialities and limitations that must be explored and known by researchers. This paper concisely maps a total of seven qualitative methods and five quantitative methods. A comparative analysis of the most relevant and adopted methods is done to understand the main strengths and limitations of them. Additionally, the work developed intends to be a fundamental reference for the accomplishment of a research study, in which the researcher intends to adopt a qualitative or quantitative methodology. Through the analysis of the advantages and disadvantages of each method, it becomes possible to formulate a more accurate, informed and complete choice.

Article visualizations:

Acocella, I. (2012). The focus group in social research: advantages and disadvantages. Quality & Quantity, 46(4), 1125-1136.

Almeida, F., & Monteiro, J. (2017). Approaches and principles for UX web experiences. International Journal of Information Technology and Web Engineering, 12(2), 49-65.

Alshenqeeti, H. (2014). Interviewing as a data collection method: a critical review. English Linguistics Research, 3(1), 39-45.

Atieno, O. (2009). An analysis of the strengths and limitation of qualitative and quantitative research paradigms. Problems of Education in the 21st Century, 13, 13-18.

Baxter, P., & Jack, S. (2008). Qualitative case study methodology: study design and implementation for novice researchers. The Qualitative Report, 13(4), 544-559.

Blackstone, A. (2012). Principles of sociological inquiry: qualitative and quantitative methods. Retrieved from https://2012books.lardbucket.org/books/sociological-inquiry-principles-qualitative-and-quantitative-methods/index.html

Borrego, M., Douglas, E., & Amelink, C. (2009). Quantitative, qualitative, and mixed research methods in engineering education. Journal of Engineering Education, 98(1), 53-66.

Castellan, C. (2010). Quantitative and qualitative research: a view for clarity. International Journal of Education, 2(2), 1-14.

Charmaz, K. (2006). Constructing grounded theory: a practical guide through qualitative analysis. London, United Kingdom: Sage.

Choy, L. (2014). The strengths and weaknesses of research methodology: comparison and complimentary between qualitative and quantitative approaches. IOSR Journal of Humanities and Social Science, 19(4), 99-104.

Coughlan, M., Cronin, P, & Ryan, F. (2007). Step-by-step guide to critiquing research. Part 1: quantitative research. British Journal of Nursing, 16(11), 658-663.

Crescentini, A. & Mainardi, G. (2009). Qualitative research articles: guidelines, suggestions and needs. Journal of Workplace Learning, 21(5), 431-439.

Creswell, J. (2013). Research design: qualitative, quantitative, and mixed methods approaches. London, United Kingdom: Sage.

Creswell, J. & Poth, C. (2017). Qualitative inquiry and research design: choosing among five approaches. London, United Kingdom: Sage.

Davies, M. (2011). Concept mapping, mind mapping and argument mapping: what are the differences and do they matter? Higher Education, 62(3), 279-301.

Etikan, I., Musa, S., & Alkassim, R. (2016). Comparison convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4.

Flanagan, T. (2013). The scientific method and why it matters. C2C Journal, 7(1), 4-6.

Felix, E. (2015). The implications of parametric and non-parametric statistics in data analysis in marketing research. International Journal of Humanities and Social Science, 5(6), 74-83.

Hoy, W. & Adams, C. (2015). Quantitative research in education. London, United Kingdom: Sage.

Jamshed, S. (2014). Qualitative research method - interviewing and observation. Journal of Basic and Clinical Pharmacy, 5(4), 87-88.

Kelley, K., Clark, B., Brown, V., & Sitzia, J. (2003). Good practice in the conduct and reporting of survey research. International Journal for Quality in Health Care, 15(3), 261-266.

Kothari, C. (2013). Research methodology: methods and techniques. New Delhi, India: New Age International.

Maher, J., Markey, J., & Ebert-May, D. (2013). The other half of the story: effect size analysis in quantitative research. CBE Life Sciences Education, 12(3), 345-351.

Martin, W., & Bridgmon, K. (2012). Quantitative and statistical research methods: from hypothesis to results. New Jersey, USA: Jossey-Bass.

Maxwell, J. (2013). Qualitative research design: an interactive approach. London, United Kingdom: Sage.

Merriam, S. & Tisdell, E. (2015). Qualitative research: a guide to design and implementation. New Jersey, USA: Jossey-Bass.

Mori, H. & Nakayama, T. (2013). Academic impact of qualitative studies in healthcare: bibliometric analysis. Plos One, 8(3), 1-7.

Moriarty, J. (2011). Qualitative methods overview. London, UK: NIHR School for Social Care Research.

Noble, H., & Smith, J. (2015). Issues of validity and reliability in qualitative research. Evidence-Based Nursing, 18(2), 34-5.

Nurani, L. (2008). Critical review of ethnographic approach. Jurnal Sosioteknologi, 7(14), 441-447.

Oppong, S. (2013). The problem of sampling in qualitative research. Asian Journal of Management Sciences and Education, 2(2), 202-210.

Polit, D., & Beck C. (2006). Essentials of nursing research: methods, appraisal and utilization. Philadelphia, USA: Lippincott Willaims and Wilkins.

Ponelis, S. (2015). Using interpretive qualitative case studies for exploratory research in doctoral studies: a case of information systems research in small and medium enterprises. International Journal of Doctoral Studies, 10, 535-550.

Rahman, S. (2017). The advantages and disadvantages of using qualitative and quantitative approaches and methods in language "testing and assessment" research: a literature review. Journal of Education and Learning, 6(1), 102-112.

Roshan, B. & Deeptee, P. (2009). Justifications for qualitative research in organisations: a step forward. The Journal of Online Education, 1, 1-7.

Schneider, J. (2013). Caveats for using statistical significance tests in research assessment. Journal of Informetrics, 7(1), 50-62.

Starman, A. (2013). The case study as a type of qualitative research. Journal of Contemporary Educational Studies, 1, 28-43.

Williams, C. (2007). Research methods. Journal of Business & Economic Research, 5(3), 65-72.

• There are currently no refbacks.

Copyright © 2015-2023. European Journal of Education Studies (ISSN 2501 - 1111) is a registered trademark of Open Access Publishing Group . All rights reserved.

This journal is a serial publication uniquely identified by an International Standard Serial Number ( ISSN ) serial number certificate issued by Romanian National Library ( Biblioteca Nationala a Romaniei ). All the research works are uniquely identified by a CrossRef DOI digital object identifier supplied by indexing and repository platforms. All authors who send their manuscripts to this journal and whose articles are published on this journal retain full copyright of their articles. All the research works published on this journal are meeting the  Open Access Publishing  requirements and can be freely accessed, shared, modified, distributed and used in educational, commercial and non-commercial purposes under a  Creative Commons Attribution 4.0 International License (CC BY 4.0) .

Methods for Quantitative Research in Psychology

• Conducting Research

Psychological Research

August 2023

This seven-hour course provides a comprehensive exploration of research methodologies, beginning with the foundational steps of the scientific method. Students will learn about hypotheses, experimental design, data collection, and the analysis of results. Emphasis is placed on defining variables accurately, distinguishing between independent, dependent, and controlled variables, and understanding their roles in research.

The course delves into major research designs, including experimental, correlational, and observational studies. Students will compare and contrast these designs, evaluating their strengths and weaknesses in various contexts. This comparison extends to the types of research questions scientists pose, highlighting how different designs are suited to different inquiries.

A critical component of the course is developing the ability to judge the quality of sources for literature reviews. Students will learn criteria for evaluating the credibility, relevance, and reliability of sources, ensuring that their understanding of the research literature is built on a solid foundation.

Reliability and validity are key concepts addressed in the course. Students will explore what it means for an observation to be reliable, focusing on consistency and repeatability. They will also compare and contrast different forms of validity, such as internal, external, construct, and criterion validity, and how these apply to various research designs.

The course concepts are thoroughly couched in examples drawn from the psychological research literature. By the end of the course, students will be equipped with the skills to design robust research studies, critically evaluate sources, and understand the nuances of reliability and validity in scientific research. This knowledge will be essential for conducting high-quality research and contributing to the scientific community.

Learning objectives

• Describe the steps of the scientific method.
• Specify how variables are defined.
• Compare and contrast the major research designs.
• Explain how to judge the quality of a source for a literature review.
• Compare and contrast the kinds of research questions scientists ask.
• Explain what it means for an observation to be reliable.
• Compare and contrast forms of validity as they apply to the major research designs.

This program does not offer CE credit.

More in this series

Introduces applying statistical methods effectively in psychology or related fields for undergraduates, high school students, and professionals.

August 2023 On Demand Training

Introduces the importance of ethical practice in scientific research for undergraduates, high school students, and professionals.

A quantitative study of the benefits of the third-order over the second-order harmonic radar

• Abad Lima, Rita J.
• Li, Changzhi

Nonlinear radar technology has been employed in various applications for a few decades, primarily focusing on second-order harmonic radar systems. This study delves into the potential benefits and drawbacks of utilizing higher-order harmonic radar systems. We present a comparative analysis between second- and third-order harmonic systems using Cadence AWR Visual System Simulator (VSS) followed by experimental validation. In the experiments, target emulation is achieved through nonlinear tags. In contrast, cluttered environments near the targets are simulated using corner reflectors, with the tag and the corner reflector moving at different frequencies facilitated by Zaber linear actuators. Our findings reveal both the advantages and limitations associated with higher-order systems, offering valuable insights into an underexplored area of research within the domain of harmonic radar technology. This contribution addresses the existing gap in the literature pertaining to higher-order harmonic radars by providing comparative analyses using both measurement and simulation data.