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Quantitative Vs. Qualitative Data and Laboratory Testing

How to Calculate Significance

Quantitative data is numerical data, whereas qualitative data has no numbers attached to it. The gender of respondents in a study, dividing light bulbs into categories like "very bright," "somewhat bright" and "dim," or the type of pizza a customer prefers are all examples of qualitative data. If you say, by contrast, that 51 percent of the plants tested grew to 10 inches or more, while 33 percent grew to 5 inches or less, you are looking at quantitative data.

Distinction

Qualitative data is by definition non-numerical, but qualitative data can sometimes be assembled to provide quantitative data. For example, if customers in a survey describe how they feel about a food item they purchased, the questionnaire would only provide qualitative data. If the individual questionnaire results were compiled to determine how many or what percentage of customers prefer pepperoni to anchovies, however, the survey would now have provided some quantitative data as well.

Some lab tests provide qualitative results and others quantitative. A procedure called a Western blot, for example, typically provides only qualitative data -- whether or not a particular protein was present, but not how much of it was present. Another common test called the enzyme-linked immunosorbent assay (ELISA) can be performed using approaches that will provide either qualitative or quantitative results. The typical pregnancy test is qualitative; it tests for the presence of human chorionic gonadotrophin (HCG) in the patient's urine, but does not quantify the amount present.

Advantages of Qualitative Data

Sometimes qualitative data is preferable. If you are doing a pregnancy test, for example, you know that if high levels of HCG are present, it means you're almost undoubtedly pregnant. You're not really trying to find out precisely what the level of HCG is -- you want a yes-or-no answer, not a numerical answer that will be more difficult for you to interpret. Likewise, if you are testing to determine whether blood samples from a patient are HIV-positive, the patient and her physician want a yes-or-no answer and not a numerical one.

Advantages of Quantitative Data

In other experiments or lab tests, quantitative data is preferable. If biochemists are working on determining the isoelectric point of an enzyme (the pH at which it has no net charge), they want a quantitative, numerical answer. Likewise, if you were to test positive for HIV and your doctor ordered a viral load test, a test that gives the amount of virus present per given unit of body fluid, she would be trying to obtain quantitative data for use in planning your treatment.

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• University of South Alabama: Qualitative Data Analysis
• "Biochemical Techniques, Laboratory Manual"; Aaron Coleman, et al.; 2010
• "Biology"; Neil A. Campbell, et al.; 2008

About the Author

Based in San Diego, John Brennan has been writing about science and the environment since 2006. His articles have appeared in "Plenty," "San Diego Reader," "Santa Barbara Independent" and "East Bay Monthly." Brennan holds a Bachelor of Science in biology from the University of California, San Diego.

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

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Qualitative vs. Quantitative Methods

There are two types of research methods out there- Qualitative and Quantitative. Qualitative is used to describe methods which draw on data collection techniques such as interviews and observations. Quantitative research describes methods that gather a range of numeric data. The purpose is to generate knowledge and create understanding about the world.

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Sourced from from Maricopa College Qualitative vs. Qualitative Research

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Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

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

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

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

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

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analysing quantitative data

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

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

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

Analysing qualitative data

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

Some common approaches to analysing qualitative data include:

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

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

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

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

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

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

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

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

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Quantitative vs Qualitative Data: What’s the Difference?

If you’re considering a career in data—or in any kind of research field, like psychology—you’ll need to get to grips with two types of data: Quantitative and qualitative .

Quantitative data is anything that can be counted or measured ; it refers to numerical data. Qualitative data is descriptive , referring to things that can be observed but not measured—such as colors or emotions.

In this post, we’ll define both quantitative and qualitative data in more detail. We’ll then explore all the key ways in which they differ—from how they are collected and analyzed, to the advantages and disadvantages of each. We’ll also include useful examples throughout.

By the end, you’ll have a clear understanding of the difference between qualitative and quantitative data, and a good idea of when to use which. Want to skip ahead to a specific section? Just use this clickable menu:

• Quantitative vs qualitative data: What are they, and what’s the difference between them?
• What are the different types of quantitative and qualitative data?
• How are quantitative and qualitative data collected?
• Quantitative vs qualitative data: Methods of analysis
• What are the advantages and disadvantages of quantitative vs qualitative data?
• When should I use qualitative or quantitative data?
• Quantitative vs. qualitative data: FAQ
• Key takeaways 

Without further ado, let’s jump in.

1. What is the difference between quantitative and qualitative data?

When it comes to conducting research and data analysis, you’ll work with two types of data: quantitative and qualitative. Each requires different collection and analysis methods, so it’s important to understand the difference between the two.

What is quantitative data?

Quantitative data refers to any information that can be quantified. If it can be counted or measured, and given a numerical value, it’s quantitative data. Quantitative data can tell you “how many,” “how much,” or “how often”—for example, how many people attended last week’s webinar? How much revenue did the company make in 2019? How often does a certain customer group use online banking?

To analyze and make sense of quantitative data, you’ll conduct statistical analyses.

Learn more: What is quantitative data? A complete introduction

What is qualitative data?

Unlike quantitative data, qualitative data cannot be measured or counted. It’s descriptive, expressed in terms of language rather than numerical values.

Researchers will often turn to qualitative data to answer “Why?” or “How?” questions. For example, if your quantitative data tells you that a certain website visitor abandoned their shopping cart three times in one week, you’d probably want to investigate why—and this might involve collecting some form of qualitative data from the user. Perhaps you want to know how a user feels about a particular product; again, qualitative data can provide such insights. In this case, you’re not just looking at numbers; you’re asking the user to tell you, using language, why they did something or how they feel.

Qualitative data also refers to the words or labels used to describe certain characteristics or traits—for example, describing the sky as blue or labeling a particular ice cream flavor as vanilla.

What are the main differences between quantitative and qualitative data?

The main differences between quantitative and qualitative data lie in what they tell us , how they are collected , and how they are analyzed. Let’s summarize the key differences before exploring each aspect in more detail:

• Quantitative data is countable or measurable, relating to numbers. Qualitative data is descriptive, relating to language.
• Quantitative data tells us how many, how much, or how often (e.g. “20 people signed up to our email newsletter last week”). Qualitative data can help us to understand the “why” or “how” behind certain behaviors, or it can simply describe a certain attribute—for example, “The postbox is red” or “I signed up to the email newsletter because I’m really interested in hearing about local events.”
• Quantitative data is fixed and “universal,” while qualitative data is subjective and dynamic. For example, if something weighs 20 kilograms, that can be considered an objective fact. However, two people may have very different qualitative accounts of how they experience a particular event.
• Quantitative data is gathered by measuring and counting. Qualitative data is collected by interviewing and observing.
• Quantitative data is analyzed using statistical analysis, while qualitative data is analyzed by grouping it in terms of meaningful categories or themes.

The difference between quantitative and qualitative data: An example

To illustrate the difference between quantitative and qualitative data, let’s use an example. Imagine you want to describe your best friend. What kind of data might you gather or use to paint a vivid picture?

First, you might describe their physical attributes, such as their height, their hair style and color, what size feet they have, and how much they weigh. Then you might describe some of their most prominent personality traits. On top of that, you could describe how many siblings and pets they have, where they live, and how often they go swimming (their favorite hobby).

All of that data will fall into either the quantitative or qualitative categories, as follows:

Quantitative data:

• My best friend is 5 feet and 7 inches tall
• They have size 6 feet
• They weigh 63 kilograms
• My best friend has one older sibling and two younger siblings
• They have two cats
• My best friend lives twenty miles away from me
• They go swimming four times a week

Qualitative data:

• My best friend has curly brown hair
• They have green eyes
• My best friend is funny, loud, and a good listener
• They can also be quite impatient and impulsive at times
• My best friend drives a red car
• They have a very friendly face and a contagious laugh

Of course, when working as a researcher or data analyst, you’ll be handling much more complex data than the examples we’ve given. However, our “best friend” example has hopefully made it easier for you to distinguish between quantitative and qualitative data.

2. Different types of quantitative and qualitative data

When considering the difference between quantitative and qualitative data, it helps to explore some types and examples of each. Let’s do that now, starting with quantitative data.

Types of quantitative data (with examples)

Quantitative data is either discrete or continuous :

• Discrete quantitative data takes on fixed numerical values and cannot be broken down further. An example of discrete data is when you count something, such as the number of people in a room. If you count 32 people, this is fixed and finite.
• Continuous quantitative data can be placed on a continuum and infinitely broken down into smaller units. It can take any value; for example, a piece of string can be 20.4cm in length, or the room temperature can be 30.8 degrees.

What are some real-world examples of quantitative data?

Some everyday examples of quantitative data include:

• Measurements such as height, length, and weight
• Counts, such as the number of website visitors, sales, or email sign-ups
• Calculations, such as revenue
• Projections, such as predicted sales or projected revenue increase expressed as a percentage
• Quantification of qualitative data—for example, asking customers to rate their satisfaction on a scale of 1-5 and then coming up with an overall customer satisfaction score

Types of qualitative data (with examples)

Qualitative data may be classified as nominal or ordinal :

• Nominal data is used to label or categorize certain variables without giving them any type of quantitative value. For example, if you were collecting data about your target audience, you might want to know where they live. Are they based in the UK, the USA, Asia, or Australia? Each of these geographical classifications count as nominal data. Another simple example could be the use of labels like “blue,” “brown,” and “green” to describe eye color.
• Ordinal data is when the categories used to classify your qualitative data fall into a natural order or hierarchy. For example, if you wanted to explore customer satisfaction, you might ask each customer to select whether their experience with your product was “poor,” “satisfactory,” “good,” or “outstanding.” It’s clear that “outstanding” is better than “poor,” but there’s no way of measuring or quantifying the “distance” between the two categories.

Nominal and ordinal data tends to come up within the context of conducting questionnaires and surveys. However, qualitative data is not just limited to labels and categories; it also includes unstructured data such as what people say in an interview, what they write in a product review, or what they post on social media.

What are some real-world examples of qualitative data?

Some examples of qualitative data include:

• Interview transcripts or audio recordings
• The text included in an email or social media post
• Product reviews and customer testimonials
• Observations and descriptions; e.g. “I noticed that the teacher was wearing a red jumper.”
• Labels and categories used in surveys and questionnaires, e.g. selecting whether you are satisfied, dissatisfied, or indifferent to a particular product or service.

3. How are quantitative and qualitative data collected?

One of the key differences between quantitative and qualitative data is in how they are collected or generated.

How is quantitative data generated?

Quantitative data is generated by measuring or counting certain entities, or by performing calculations. Some common quantitative data collection methods include:

• Surveys and questionnaires: This is an especially useful method for gathering large quantities of data. If you wanted to gather quantitative data on employee satisfaction, you might send out a survey asking them to rate various aspects of the organization on a scale of 1-10.
• Analytics tools: Data analysts and data scientists use specialist tools to gather quantitative data from various sources. For example, Google Analytics gathers data in real-time, allowing you to see, at a glance, all the most important metrics for your website—such as traffic, number of page views, and average session length.
• Environmental sensors: A sensor is a device which detects changes in the surrounding environment and sends this information to another electronic device, usually a computer. This information is converted into numbers, providing a continuous stream of quantitative data.
• Manipulation of pre-existing quantitative data: Researchers and analysts will also generate new quantitative data by performing statistical analyses or calculations on existing data. For example, if you have a spreadsheet containing data on the number of sales and expenditures in USD, you could generate new quantitative data by calculating the overall profit margin.

How is qualitative data generated?

Qualitative data is gathered through interviews, surveys, and observations. Let’s take a look at these methods in more detail:

• Interviews are a great way to learn how people feel about any given topic—be it their opinions on a new product or their experience using a particular service. Conducting interviews will eventually provide you with interview transcripts which can then be analyzed.
• Surveys and questionnaires are also used to gather qualitative data. If you wanted to collect demographic data about your target audience, you might ask them to complete a survey where they either select their answers from a number of different options, or write their responses as freeform text.
• Observations: You don’t necessarily have to actively engage with people in order to gather qualitative data. Analysts will also look at “naturally occurring” qualitative data, such as the feedback left in product reviews or what people say in their social media posts.

4. Quantitative vs qualitative data: methods of analysis

Another major difference between quantitative and qualitative data lies in how they are analyzed. Quantitative data is suitable for statistical analysis and mathematical calculations, while qualitative data is usually analyzed by grouping it into meaningful categories or themes.

Quantitative data analysis

How you analyze your quantitative data depends on the kind of data you’ve gathered and the insights you want to uncover. Statistical analysis can be used to identify trends in the data, to establish if there’s any kind of relationship between a set of variables (e.g. does social media spend correlate with sales), to calculate probability in order to accurately predict future outcomes, to understand how the data is distributed—and much, much more.

Some of the most popular methods used by data analysts include:

• Regression analysis
• Monte Carlo simulation
• Factor analysis
• Cohort analysis
• Cluster analysis
• Time series analysis

You’ll find a detailed explanation of these methods in our guide to the most useful data analysis techniques .

Qualitative data analysis

With qualitative data analysis, the focus is on making sense of unstructured data (such as large bodies of text). Given that qualitative data cannot be measured objectively, it is open to subjective interpretation and therefore requires a different approach to analysis.

The main method of analysis used with qualitative data is a technique known as thematic analysis. Essentially, the data is coded in order to identify recurring keywords or topics, and then, based on these codes, grouped into meaningful themes.

Another type of analysis is sentiment analysis , which seeks to classify and interpret the emotions conveyed within textual data. This allows businesses to gauge how customers feel about various aspects of the brand, product, or service, and how common these sentiments are across the entire customer base.

Traditionally, qualitative data analysis has had something of a bad reputation for being extremely time-consuming. However, nowadays the process can be largely automated, and there are plenty of tools and software out there to help you make sense of your qualitative data. To learn more about qualitative analysis and what you can do with it, check out this round-up of the most useful qualitative analysis tools on the market .

5. What are the advantages and disadvantages of quantitative vs qualitative data?

Each type of data comes with advantages and disadvantages, and it’s important to bear these in mind when conducting any kind of research or sourcing data for analysis. We’ll outline the main advantages and disadvantages of each now.

What are the advantages and disadvantages of quantitative data?

A big advantage of quantitative data is that it’s relatively quick and easy to collect, meaning you can work with large samples. At the same time, quantitative data is objective; it’s less susceptible to bias than qualitative data, which makes it easier to draw reliable and generalizable conclusions.

The main disadvantage of quantitative data is that it can lack depth and context. The numbers don’t always tell you the full story; for example, you might see that you lost 70% of your newsletter subscribers in one week, but without further investigation, you won’t know why.

What are the advantages and disadvantages of qualitative data?

Where quantitative data falls short, qualitative data shines. The biggest advantage of qualitative data is that it offers rich, in-depth insights and allows you to explore the context surrounding a given topic. Through qualitative data, you can really gauge how people feel and why they take certain actions—crucial if you’re running any kind of organization and want to understand how your target audience operates.

However, qualitative data can be harder and more time-consuming to collect, so you may find yourself working with smaller samples. Because of its subjective nature, qualitative data is also open to interpretation, so it’s important to be aware of bias when conducting qualitative analysis.

6. When should I use qualitative or quantitative data?

Put simply, whether you use qualitative or quantitative data (or a combination of both!) depends on the data analytics project you’re undertaking. Here, we’ll discuss which projects are better suited to which data.

Generally, you can use the following criteria to determine whether to go with qualitative data, quantitative data, or a mixed methods approach to collecting data for your project.

• Do you want to understand something, such as a concept, experience, or opinions? Use qualitative data.
• Do you want to confirm or test something, such as a theory or hypothesis? Use quantitative data.
• Are you taking on research? You may benefit from a mixed methods approach to data collection.

You may find that more often than not, both types of data are used in projects, in order to gain a clear overall image—integrating both the numbers side and human side of things.

6. Quantitative vs. qualitative data: FAQ

What are the main differences between qualitative and quantitative research.

Qualitative research is primarily exploratory and uses non-numerical data to understand underlying reasons, opinions, and motivations. Quantitative research, on the other hand, is numerical and seeks to measure variables and relationships through statistical analysis. Additionally, qualitative research tends to be subjective and less structured, while quantitative research is objective and more structured.

What are examples of qualitative and quantitative data?

Examples of qualitative data include open-ended survey responses, interview transcripts, and observational notes. Examples of quantitative data include numerical survey responses, test scores, and website traffic data. Qualitative data is typically subjective and descriptive, while quantitative data is objective and numerical.

7. Key takeaways

Throughout this post, we’ve defined quantitative and qualitative data and explained how they differ. What it really boils down to, in very simple terms, is that quantitative data is countable or measurable, relating to numbers, while qualitative data is descriptive, relating to language.

Understanding the difference between quantitative and qualitative data is one of the very first steps towards becoming a data expert. If you’re considering a career in data, you’ll find links to some useful articles at the end of this post. Had enough theory and want some action? Check out our list of free data analytics courses for beginners , or cut to the chase and simply sign up for a free, five-day introductory data analytics short course .

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qualitative and quantitative header

Learning Objective

Differentiate between qualitative and quantitative approaches.

Hong is a physical therapist who teaches injury assessment classes at the University of Utah. With the recent change to online for the remainder of the semester, Hong is interested in the impact on students’ skills acquisition for injury assessment. He wants to utilize both quantitative and qualitative approaches—he plans to compare previous student test scores to current student test scores. He also plans to interview current students about their experiences practicing injury assessment skills virtually. What specific study design methods will Hong use?

Making sense of the evidence

hen conducting a literature search and reviewing research articles, it is important to have a general understanding of the types of research and data you anticipate from different types of studies.

In this article, we review two broad categories of study methods, quantitative and qualitative, and discuss some of their subtypes, or designs, and the type of data that they generate.

Quantitative vs. qualitative approaches

Quantitative is measurable. It is often associated with a more traditional scientific method of gathering data in an organized, objective manner so that findings can be generalized to other persons or populations. Quantitative designs are based on probabilities or likelihood—it utilizes ‘p’ values, power analysis, and other scientific methods to ensure the rigor and reproducibility of the results to other populations. Quantitative designs can be experimental, quasi-experimental, descriptive, or correlational.

Qualitative is usually more subjective , although like quantitative research, it also uses a systematic approach. Qualitative research is generally preferred when the clinical question centers around life experiences or meaning. Qualitative research explores the complexity, depth, and richness of a particular situation from the perspective of the informants—referring to the person or persons providing the information. This may be the patient, the patient’s caregivers, the patient’s family members, etc. The information may also come from the investigator’s or researcher’s observations. At the heart of qualitative research is the belief that reality is based on perceptions and can be different for each person, often changing over time.

Study design differences

Quantitative design methods

Quantitative designs typically fall into four categories: experimental, quasi-experimental, descriptive, or correlational. Let’s talk about these different types. But before we begin, we need to briefly review the difference between independent and dependent variables.

The independent variable is the variable that is being manipulated, or the one that varies. It is sometimes called the ‘predictor’ or ‘treatment’ variable.

The dependent variable is the outcome (or response) variable. Changes in the dependent variables are presumed to be caused or influenced by the independent variable.

Experimental

In experimental designs, there are often treatment groups and control groups. This study design looks for cause and effect (if A, then B), so it requires having control over at least one of the independent, or treatment variables. Experimental design administers the treatment to some of the subjects (called the ‘experimental group’) and not to others (called the ‘control group’). Subjects are randomly assigned—meaning that they would have an equal chance of being assigned to the control group or the experimental group. This is the strongest design for testing cause and effect relationships because randomization reduces bias. In fact, most researchers believe that a randomized controlled trail is the only kind of research study where we can infer cause (if A, then B). The difficulty with a randomized controlled trial is that the results may not be generalizable in all circumstances with all patient populations, so as with any research study, you need to consider the application of the findings to your patients in your setting. 

Quasi-experimental

Quasi-Experimental studies also seek to identify a cause and effect (causal) relationship, although they are less powerful than experimental designs. This is because they lack one or more characteristics of a true experiment. For instance, they may not include random assignment or they may not have a control group. As is often the case in the ‘real world’, clinical care variables often cannot be controlled due to ethical, practical, or fiscal concerns. So, the quasi experimental approach is utilized when a randomized controlled trial is not possible. For example, if it was found that the new treatment stopped disease progression, it would no longer be ethical to withhold it from others by establishing a control group.

Descriptive

Descriptive studies give us an accurate account of the characteristics of a particular situation or group. They are often used to determine how often something occurs, the likelihood of something occurring, or to provide a way to categorize information. For example, let’s say we wanted to look at the visiting policy in the ICU and describe how implementing an open-visiting policy affected nurse satisfaction. We could use a research tool, such as a Likert scale (5 = very satisfied and 1 = very dissatisfied), to help us gain an understanding of how satisfied nurses are as a group with this policy.

Correlational

Correlational research involves the study of the relationship between two or more variables. The primary purpose is to explain the nature of the relationship, not to determine the cause and effect. For example, if you wanted to examine whether first-time moms who have an elective induction are more likely to have a cesarean birth than first-time moms who go into labor naturally, the independent variables would be ‘elective induction’ and ‘go into labor naturally’ (because they are the variables that ‘vary’) and the outcome variable is ‘cesarean section.’ Even if you find a strong relationship between elective inductions and an increased likelihood of cesarean birth, you cannot state that elective inductions ‘cause’ cesarean births because we have no control over the variables. We can only report an increased likelihood.   

Qualitative design methods

Qualitative methods delve deeply into experiences, social processes, and subcultures. Qualitative study generally falls under three types of designs: phenomenology, ethnography and grounded theory.

Phenomenology

In this approach, we want to understand and describe the lived experience or meaning of persons with a particular condition or situation. For example, phenomenological questions might ask “What is it like for an adolescent to have a younger sibling with a terminal illness?” or “What is the lived experience of caring for an older house-bound dependent parent?”

Ethnography

Ethnographic studies focus on the culture of a group of people. The assumption behind ethnographies is that groups of individuals evolve into a kind of ‘culture’ that guides the way members of that culture or group view the world. In this kind of study, the research focuses on participant observation, where the researcher becomes an active participant in that culture to understand its experiences. For example, nursing could be considered a professional culture, and the unit of a hospital can be viewed as a subculture. One example specific to nursing culture was a study done in 2006 by Deitrick and colleagues . They used ethnographic methods to examine problems related to answering patient call lights on one medical surgical inpatient unit. The single nursing unit was the ‘culture’ under study.

Grounded theory

Grounded theory research begins with a general research problem, selects persons most likely to clarify the initial understanding of the question, and uses a variety of techniques (interviewing, observation, document review to name a few) to discover and develop a theory. For example, one nurse researcher used a grounded theory approach to explain how African American women from different socioeconomic backgrounds make decisions about mammography screening. Because African American women historically have fewer mammograms (and therefore lower survival rates for later stage detection), understanding their decision-making process may help the provider support more effective health promotion efforts. 

Being able to identify the differences between qualitative and quantitative research and becoming familiar with the subtypes of each can make a literature search a little less daunting.

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This article originally appeared July 2, 2020. It was updated to reflect current practice on March 21, 2021.

Barbara Wilson

Mary-jean (gigi) austria, tallie casucci.

Performing a rapid critical appraisal helps evaluate a study for its worth by ensuring validity, meaningful data, and significance to the patient. Contributors Barb Wilson, Mary Jean Austria, and Tallie Casucci share a checklist of questions to complete a rapid critical appraisal efficiently and effectively.

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Medical students Rachel Tsolinas and Sam Wilkinson, along with SOM professor Kathryn Moore, share a practical tool all health care professionals can use to broaden our understanding of how culture influences decisions and events.

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Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

Quantitative Data 101: What is quantitative data?

Take a deeper dive into what quantitative data is, how it works, how to analyze it, collect it, use it, and more.

Learn more about quantitative data →

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

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.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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30 Differences Between Qualitative and Quantitative Test Results

• April 21, 2024

Q1. What are the drawbacks of using qualitative test results?

Q2. how can i increase the qualitative test results’ reliability, q3. what role does dependability play in the outcomes of quantitative tests, q4. which kinds of qualitative testing are most frequently used, q5. how does user feedback fit into the qualitative testing process, q6. what distinguishes test findings from one another, qualitative and quantitative.

Test findings that are qualitative and quantitative are two different categories of data that offer different kinds of information.

Test results that are classified as qualitative are descriptive in character as opposed to quantitative or numerical. Rather than being stated in terms of precise numbers, these results are frequently described in terms of traits, features, or attributes. In many disciplines, such as science, research, and product development, qualitative testing is a technique used to get data about a subject’s characteristics or attributes without depending solely on numerical measures.

Qualitative test results, for instance, could characterize a substance’s color, texture, odor, or other sensory attributes in a scientific investigation. Qualitative data in social science research may be analyzed through observations or interview replies that shed light on attitudes, actions, or experiences.

The measurements or findings from a testing procedure that uses numerical data are referred to as quantitative test results. These results may be statistically analyzed because they are expressed in terms of quantities. Scientific research, engineering, banking, and many other domains where accurate measurements and numerical data are essential frequently use quantitative testing.

Quantitative test findings, for instance, can include measurements of temperature, weight, length, or concentration in a scientific experiment. Metrics like reaction time, error rates, or the quantity of transactions handled in a given amount of time are examples of quantitative results in software testing. Quantitative results in educational testing may be exam or assessment scores.

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

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About Qualitative Data

Qualitative data are data representing information and concepts that are not represented by numbers. They are often gathered from interviews and focus groups, personal diaries and lab notebooks, maps, photographs, and other printed materials or observations. Qualitative data are distinguished from  quantitative data , which focus primarily on data that can be represented with numbers. 

Qualitative data can be analyzed in multiple ways. One common method is data coding, which refers to the process of transforming the raw collected data into a set of meaningful categories that describe essential concepts of the data. Qualitative data and methods may be used more frequently in humanities or social science research and may be collected in descriptive studies.

(From the Data Glossary , National Center for Data Services, National Library of Medicine)

Methods Texts

Below are some methods texts recommended by qualitative workshop leaders from the UC Berkeley Library and the D-Lab: 

Workshops and Training

• Managing qualitative data 101 Tips on managing qualitative materials from your qualitative research librarian.
• D-Lab workshops Free online workshops on quant and qualitative skills, including coding and using qualitative analysis software.
• Institute for the Study of Societal Issues (ISSI) Training Ethnographic methods workshop from a campus institute.
• Qualitative Methods classes Filter to upcoming semesters and look for qualitative methods classes; the Graduate School of Education and School of Public Health offer extensive methods training.

Qualitative Data Analysis Software

Unfortunately, Berkeley does not yet have a sitewide license for any qualitative analysis software.

If you are a student, you can find affordable student licenses with a web search.

If you are a faculty member, instructor, lecturer, or visiting scholar without grant funding, unfortunately software is quite expensive.

You can find reviews of many qualitative software packages at this University of Surrey link:

• Choosing an Appropriate CAQDAS package .

You can also check out the websites of several major options below: 

• Taguette Taguette has fewer features than other qualitative analysis software, but is free and open-source.
• Atlas.ti Atlas.ti is a major qualitative analysis software, and has affordable licenses for students.
• MaxQDA MaxQDA is a major qualitative analysis software, with affordable student licenses. The D-Lab often teaches workshops on this software.
• NVIVO NVIVO is an established QDA software, with affordable student licenses.
• Dedoose Dedoose supports qualitaive and mixed methods research, using an online interface. Students pay $11 per month.

Resources for Qualitative Data Management

• Managing and Sharing Qualitative Data 101 This page from Berkeley's research data management website offers several things to consider.
• Tutorials on Ethnographic Data Management This curricula includes eight presentations and accompanying exercises for you to think through your qualitative data project--or coach others to do the same.
• Support Your Data: Evaluation Rubric Download the evaluation rubric on this page to assess where you are with qualitative data management, and consider areas to explore next.
• The Qualitative Data Repository (QDR) QDR is one of the top US-based repositories focused on the challenges of managing, storing, and sharing qualitative research materials.
• Research Data @ Berkeley Email Research Data for a consultation about how to set up your qualitative data management plan; they can help you locate other resources on campus.

Mixed Methods Research

Interpretations related to mixed (sometimes called merged) methods vary; be wary of jargon!  Gery Ryan, of the Kaiser Permanente School of Medicine, gives these definitions, while arguing that we should be thinking of the purposes of the research rather than the methodological labels:

Mixed methods research : “Combines elements of qualitative and quantitative research approaches (e. g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration.”

Multimethod research : “Either solely combine multiple qualitative approaches or solely combine multiple quantitative approaches.”

Data triangulation : “Uses multiple sources of data or multiple approaches to analyzing data to enhance the credibility of a research study.”

(From " Mixed Methods Research Designs and Data Triangulation " by Gery Ryan, Kaiser Permanente School of Medicine)

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• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

• Research Process

Choosing the Right Research Methodology: A Guide for Researchers

• 3 minute read

Table of Contents

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

Understanding different research methods:

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

Qualitative vs quantitative research:

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

Qualitative research methodology:

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

Quantitative research methodology:

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

Analysing qualitative vs quantitative data:

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

When to use qualitative vs quantitative research:

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

Conclusion:

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

Why is data validation important in research?

When Data Speak, Listen: Importance of Data Collection and Analysis Methods

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Data Module #1: What is Research Data?

• Defining Research Data
• Qualitative vs. Quantitative
• Types of Research Data
• Data and Statistics
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Qualitative vs. Quantitative Data

Research data can be placed into two broad categories:  quantitative  or  qualitative.  .

Quantitative  data are used when a researcher is trying to quantify a problem, or address the "what" or "how many" aspects of a research question. It is data that can either be counted or compared on a numeric scale. For example, it could be the number of first year students at Macalester, or the ratings on a scale of 1-4 of the quality of food served at Cafe Mac. This data are usually gathered using instruments, such as a questionnaire which includes a ratings scale or a thermometer to collect weather data. Statistical analysis software, such as SPSS, is often used to analyze quantitative data.

Qualitative  data describes qualities or characteristics. It is collected using questionnaires, interviews, or observation, and frequently appears in narrative form. For example, it could be notes taken during a focus group on the quality of the food at Cafe Mac, or responses from an open-ended questionnaire. Qualitative data may be difficult to precisely measure and analyze. The data may be in the form of descriptive words that can be examined for patterns or meaning, sometimes through the use of coding. Coding allows the researcher to categorize qualitative data to identify themes that correspond with the research questions and to perform quantitative analysis.

Should I Use Qualitative or Quantitative Data for My Research?

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

• Quantitative vs Qualitative Observation: 15 Key Differences

• Data Collection

When carrying out experimental research, researchers can adopt either qualitative or quantitative methods of data observation depending on the sample size, research variables, and the hypothesis. Observation is an important aspect of systematic investigation because it sets the pace for any research. 

Qualitative and quantitative observation methods can be used interdependently with a variety of research tools in order to facilitate data collection and analysis. However, it is easy for these methods of observation to be mixed up hence, the need for researchers to understand the key differences between qualitative and quantitative observation.  

What is Quantitative Observation?

A quantitative observation is an objective method of data analysis that measures research variables using numerical and statistical parameters. This method of observation views research variables in terms of quantity hence; it is usually associated with values that can be counted such as age, weight, volume, and scale. 

A quantitative observation is also referred to as standardized observation because it measures research variables using definite parameters and results in definite research outcomes. It is usually carried out with a large data sample size because the larger the research sample; the more accurate the research findings would be. 

Surveys, questionnaires, and polls are common methods of carrying out quantitative observation and you can use online data-gathering platforms like Formplus to create and administer quantitative observation surveys. As a result of its dependence on numerical data , quantitative observation is commonly used for scientific research.  

Characteristics of Quantitative Observation Method

• It is definite

Unlike other methods of data analysis, the quantitative analysis yields definite results that can be quantified. Hence, adopting this data analysis design would help you arrive at more accurate research outcomes. 

• Fixed Findings

The research outcomes arrived at via quantitative observation are typically constant and not subject to sporadic changes. For example, the freezing point of water is 0°C and remains constant as long as other research variables are constant. 

• Research Sample Size

For a quantitative observation to be effective, the data sample must be large enough. This provides researchers with enough information for arriving at objective findings. 

• Reduced Research Biases

The data gathered using quantitative observation is usually accurate since it is subject to a few research biases . 

 What is Qualitative Observation? 

Qualitative observation is a research method that makes use of subjective parameters for data gathering. It utilizes processes like inductive reasoning, naturalism, and empathetic neutrality in order to equate quality similarities and differences among research variables. 

Usually, qualitative observation is more time-consuming, extensive and personal, and it uses the 5 sensory organs while examining research variables. This is because the focus of qualitative observation is the characteristics of the research subjects rather than numerical value or quantity. 

Characteristics of Qualitative Observation

• In qualitative observation, there is no right, wrong or definite answer. In this method, the researcher is keen on gathering varying answers because the more dynamic the data sample is, the better the research outcome. 
• Qualitative observation pays attention to how the context of research influences information, outcomes, and findings. 
• It is subjective in nature. 
• Qualitative observation considers every research process differently regardless of any similarities with previous studies. 

Here are 15 Differences between Quantitative and Qualitative Observation 

• Definitions 

Qualitative observation is a research method that examines the characteristics of research variables while quantitative observation is a research design that quantifies variables in terms of statistical and numerical value. Simply put, quantitative observation is an objective method of data gathering while qualitative observation is a subjective method of data gathering. 

For example, when a researcher pays equates research variables in terms of their quality, then this is qualitative observation. However, when a researcher measures the number of variables using fixed numerical or statistical parameters, then this is quantitative observation. 

Examples of quantitative observation include age, weight, height, length, population, size and other numerical values while examples of qualitative observation are color, smell, taste, touch or feeling, typology, and shapes. 

Generally, quantitative observation deals with data that can be counted while qualitative observation deals with data that can be described in terms of the 5 sensory organs. 

Consider the examples below:

• I have 2 brothers and 3 sisters.
• The t-shirts are colored blue, black and red.

The data sample in example 1 denotes quantitative observation while the data sample in example 2 denotes qualitative observation. 

Qualitative observation is mainly used in research that is concerned with the differentiating qualities of research variables while quantitative observation is mainly used in research processes that require data quantification. In some situations, a researcher may need to combine quantitative and qualitative observations in order to arrive at more objective findings. 

If a researcher needs to categorize his or her data sample based on statistical parameters, then quantitative observation would be utilized. However, if a researcher needs to categorize his or her data sample based on qualitative differences, then qualitative observation would be adopted. 

• Advantages of Qualitative Observation over Quantitative Observation  

Qualitative observation results in more in-depth and descriptive research outcomes, unlike quantitative observation. In qualitative observation, the researcher pays attention to the nature of the research variables in order to discover the true characteristics and behaviors of these variables in their natural environments. 

On the other hand, quantitative research only focuses on the numerical values of research variables without taking the nature of these variables into consideration. Hence, it is more suitable for research processes that examine quantifiable data .  

• Disadvantages of Qualitative Observation  

Because of its focus on the in-depth description of research variables, qualitative observation is time-consuming, capital intensive and also requires a high level of expertise. Hence, this method of observation may not be suitable for systematic investigations that are set within a short time frame and are subject to limited resources. 

On the other hand, quantitative research requires a shorter time frame and results in more definite research outcomes. Since its data sample can be quantified using fixed numerical parameters, quantitative observation yields more accurate results than qualitative observation and it is suitable for statistical investigations. 

• Methodology

Qualitative observation gathers data samples using complete observer, observer as a participant, participant as an observer and complete participant methods while quantitative observation collects data samples using surveys, questionnaires, and polls. For instance, you can use Formplus to create and share an online survey with your research groups part of quantitative observation. 

Qualitative observation methods typically entail the researcher recording the research variables in their natural environment. To do this, the observer may need to become a part of the research group, interact with the research group or co-exist with the research group in order to effectively describe its habits.  

• Characteristics

Numerical evaluation and bias-free research findings are the major characteristics of quantitative observation while inductive analysis and naturalism are common features of qualitative observation. Quantitative observation defines research data based in terms of quantity hence, it utilizes statistical parameters for measurements. 

Qualitative observation, on the other hand, uses inductive analysis and naturalism to describe the nature of research variables. Naturalism entails observing research variables as they interact in their natural environment while inductive analysis involves generating hypotheses based on interactions with the research group. 

• Data Sample Size

Qualitative observation is usually conducted on a small data sample size while quantitative observation is carried out on a large data sample size. Quantitative observation depends on the quantity of the research variables in order to arrive at objective findings since the data is quantified as the actual. 

In the case of qualitative observation, the research variables represent the emotions of a larger data sample. Qualitative observation works with a small data sample size because it is more extensive and personal, and the outcomes are the result of extended observation of the research group. 

• Uses in Research

As a research design, qualitative observation is used to gather information for policy formulation, developing new concepts and creating new products while quantitative observation is mostly used in scientific research since it generates numerically observed outcomes that can be measured. 

For instance, if an organization wants to gather information relating to market needs for a product launch, it may have to adopt qualitative observation methods. However, if the same organization needs to gather information on the number of consumers that use its product, it may have to utilize quantitative observation methods. 

• Objectivity

A quantitative observation is objective while qualitative observation is subjective. Quantitative observation methods depend on fixed numerical parameters in order to categorize data samples while qualitative observation depends on subjective parameters for data gathering and data analysis. 

Quantitative observation methods depend on fixed numerical parameters in order to categorize data samples while qualitative observation depends on subjective parameters for data gathering and data analysis – Click to Tweet

In qualitative observation, the researcher does not work with any fixed parameters in generating research outcomes rather, s/he collects and describes a variety of information related to the research variables. Quantitative observation, on the other hand, examines the data samples in line with definite numerical values.  

Quantitative observation methods make use of statistical parameters while qualitative observation makes use of subjective parameters. In this sense, carrying out quantitative observation means quantifying your data using certain numerical values such as age, weight, population, depth, amount and other units of measurement. 

On the other hand, qualitative observation does not quantify data hence, it is not suitable for statistical evaluation. Instead, it focuses on describing the nature of the research variables by examining how they interact with their natural environment; therefore, it is not a common method of observation in scientific research. 

Qualitative observation is more suitable for sociological investigations while quantitative observation is more suitable for scientific research. Qualitative observation methods such as naturalism involve examining research groups in their natural environment in order to arrive at objective conclusions about their behaviors and characteristics. 

Quantitative observation utilizes data gathering methods such as surveys and polls in order to quantify and categorize the research data. This research approach aligns with the scientific method of inquiry in which the research data sample is examined using measurable processes in order to arrive at definite results. 

Qualitative observation is more susceptible to biased outcomes , unlike quantitative observation. Qualitative observation methods are fluid and do not have any definite parameters for data description hence, the data gathering process is largely subject to the discretion of the researcher. 

Quantitative observation produces bias-free outcomes because this method of investigation adopts definite and objective approaches to the examination of research variables. However, these outcomes have a margin of error which is the level of error in results arrived at from analyzing random sampling surveys. 

• Variability 

Qualitative observation has a high degree of variability, unlike quantitative observation. Variability in research refers to the lack of consistency in research parameters or the lack of a fixed or definite research methodology as is obtainable in qualitative observation.

Qualitative observation methods do not have fixed parameters for the examination of sample data instead, these methods are modified based on the discretion of the researcher to suit the sample and research environment. On the other hand, quantitative observation examines data samples based on definite numerical values. 

Quantitative observation employs deductive analysis while qualitative observation employs inductive analysis. In a deductive analysis, the researcher develops a research theory, builds hypotheses from this theory and tests the hypotheses by collecting and analyzing data samples using quantitative observation methods. 

On the other hand, in inductive analysis, the researcher first gathers data samples through the observation of the research variables in their natural environment. After doing this, he or she proceeds to analyze the data samples in order to identify patterns and develop a theory that explains these patterns. 

Similarities between Quantitative and Qualitative Observation

Despite their different approaches to data gathering and analysis, there are a number of similarities between quantitative and qualitative observation methods. Here are a number of them: 

• Participants 

Both qualitative observation and quantitative observation depend on data samples gathered from research participants in order to generate objective findings. However, while qualitative observation draws data samples from actual interaction with the participants, quantitative research may utilize different indirect methods for data collection from participants. 

• Tools for Research Analysis

Qualitative and quantitative observations are both potent tools for systematic investigation. While the former is used for research analysis aimed at describing the nature of the variables, the latter is used to quantify variables based on numerical values. 

Qualitative and quantitative observation methods can be used interdependently in research. For example, in gathering feedback about a product, an organization may need to collect information about the product’s market share before proceeding with consumer satisfaction inquires. 

Both quantitative and qualitative observation methods are aimed at data collection. In other words, quantitative and qualitative observation helps the researcher to gather the information that would later be analyzed in order to come up with research findings. 

How to Use Formplus for Quantitative Observation

You can use Formplus to create and administer online surveys as part of the methods of quantitative observation. Formplus allows you to create a dynamic survey form in minutes and you can easily share your form link with friends and family. 

Here’s a step-by-step guide on how to use Formplus for quantitative observation: 

Sign in on Formplus

In the Formplus builder, you can easily create your survey form by dragging and dropping preferred fields into your form. To access the Formplus builder, you will need to create an account on Formplus . 

Once you do this, sign in to your account and click on “Create Form ” to begin. 

Edit Form Title

• Click on the field provided to input your form title, for example, “Qualitative Observation”. 

Edit Form  

• Click on the edit button to edit the form.
• Add Fields: Drag and drop preferred form fields into your form in the Formplus builder inputs column. There are several field input options for survey forms in the Formplus builder. 
• Edit fields
• Click on “Save”
• Preview form. 

Customize Form

Formplus allows you to add unique features to your survey form. You can personalize your form using various customization options in the builder. Here, you can add background images, your organization’s logo, and other features. You can also change the display theme of your form. 

Now, save your survey form and share the link with respondents. You can also track all form responses in the analytics dashboard. 

Conclusion  

Qualitative observation and quantitative observation are 2 of the most common data collection and data processing methods used in research . Both methods are primarily defined by specific characteristics in terms of their research design, data sample size and other features already mentioned in this write-up. 

Unlike quantitative observation that arrives at research outcomes through deductive reasoning, qualitative observation applies inductive reasoning for data analysis. In this sense, the researcher develops a theory to explain the patterns he has observed from his research sample after an extended inquiry period. 

In terms of similarities, both qualitative and quantitative observation methods depend on participants and groups in order to gather research variables. As an online data-gathering platform, Formplus can help you to develop and easily administer online surveys as part of the methods of quantitative observation. 

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Lesson 5 of 12 By Avijeet Biswal

Table of Contents

While working on a research project, we often wonder whether our project is qualitative or quantitative in its approach. Although their objectives and applications overlap in many ways, there are significant differences between them. In this article, we’ll learn about Qualitative vs. Quantitative Research.

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What is Qualitative Research?

In qualitative research, different types of non-numerical data is gathered and evaluated to better understand ideas, views, or experiences (such as video, text, or audio). In-depth details about a situation can be discovered or ideas for fresh study concepts can be sparked through it. Quantitative research, which includes gathering and analyzing numerical data for statistical analysis, is the antithesis of qualitative research. The humanities and social sciences frequently employ qualitative research in sociology, anthropology, education, history, health sciences, etc.

Qualitative Data Analysis

Given that you have invested time and money in gathering your data, analysis of it is essential. You don't want to end up in the dark after making so much effort. Thus, it is a necessary step. There are no predetermined guidelines for assessing this material; the first step is comprehending its two basic methods.

Deductive Strategy

The deductive method entails examining qualitative data following a specified framework. The questions might serve as a roadmap for researchers as they analyze the data. When a researcher has a good sense of the expected replies he or she will obtain from the sample population, they can utilize this quick and simple method.

Inductive Method

Contrarily, the inductive method does not rely on preconceived guidelines or a predefined framework. It is a more extensive and time-consuming method of qualitative data analysis. Researchers frequently employ an inductive technique when they have little or no knowledge about the investigated phenomena.

Key Features Of Qualitative Research

• Content evaluation. Verbal or behavioral data must be categorized to classify, summarize, and tabulate.
• Analyzing narratives Utilizing the context of each case and the varied experiences of each respondent, this strategy entails reformulating the narrative that respondents have provided. In other words, narrative analysis is the researcher's reinterpretation of the original qualitative data.
• Analysis of discourse. A technique for analyzing all kinds of written material, including naturally occurring speech.
• Framework examination. This more sophisticated approach includes a number of steps, including familiarization, choosing a thematic framework, coding, charting, mapping, and interpretation.
• Solid theory. This approach to analyzing qualitative data begins with developing a hypothesis by examining a single example. 

Limitations of Qualitative Research

• The individual talents of the researcher are a major determinant of the research's quality, and the researcher's biases and quirks might have a greater impact.
• Rigor is more challenging to uphold, gauge, and prove.
• Analysis and interpretation take a lot of time because of the volume of data.
• Within the scientific community, it is occasionally not as well understood and accepted as quantitative research.
• The respondents' replies may be impacted by the researcher's presence, which is frequently unavoidable in qualitative research.
• Problems with confidentiality and anonymity might arise when disclosing findings
• Visually describing findings might be more time-consuming and complex.

Advantages Of Qualitative Research

1. understand the attitudes.

Consumer behavior is frequently malleable. Businesses may be left wondering what will happen to them if something happens unexpectedly. Qualitative research methods offer a plausible explanation for why a person's attitude could change.

2. It Generates Content

Even for a seasoned marketer, developing new methods to convey outdated material may be challenging. The qualitative research methodology enables the collection of real thoughts from certain socioeconomic demographics.

3. It Reduces Costs

Comparatively speaking to other research techniques, qualitative research employs a smaller sample size. This is a result of the fact that each participant is asked for more data. Less expensive research is associated with smaller sample sizes. This method of study not only saves money but it also has the potential to yield quicker findings. This is one of the greatest research methods now accessible if data is required rapidly for a crucial decision.

4. Offer Insights Unique To A Certain Sector

The two key elements for retaining customers are relationships and engagement. To communicate with their core demographics in a way that is as accurate and authentic as possible, modern organizations may employ qualitative research to uncover fresh insights that help advance these two essential elements.

5. Enables Creativity To Act As A Catalyst

Facts are frequently preferred above views in research. Instead of innovation, it wants observations. Unlike standard research, qualitative research follows a distinct path. Using this format, respondents won't seek to answer questions in a way that would suit the researcher, which tends to introduce bias into the collected data.

6. Ongoing, Open-Ended Process

Many people have a conditioned, skimpy response that they develop out of habit. Researchers can go further into these behaviors to uncover the real facts that a subject might offer by using the qualitative research technique. It has access to the emotional information that influences how we make decisions.

7. Takes Into Account Human Experience

Facts are crucial. Statistics can reveal patterns. The human experience, however, cannot be disregarded. Two people will each perceive the identical incident differently due to their unique human experiences. The intricacy of this material may be included in the findings drawn from the gathered study by conducting qualitative research.

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What is Quantitative Research?

The process of gathering and interpreting numerical data is known as quantitative research. In addition to identifying trends and averaging data, hypotheses can be formulated, causality can be examined, and findings can be extrapolated to greater populations. A comparative study, which gathers and examines non-numerical data, is known as quantitative research (e.g., text, video, or audio). The scientific and social sciences, including biology, chemistry, psychology, economics, sociology, and marketing, frequently employ quantitative research.

Key Features of Quantitative Research

The goal of descriptive research is to describe the current situation of a chosen variable. The purpose of these studies is to offer systematic data regarding phenomena. The researcher typically does not start with a hypothesis but is more likely to do so after gathering evidence. The hypothesis is tested through the analysis and synthesis of the data.

Using statistical data, correlational research aims to quantify the strength of a link between two or more variables. Relationships between and among various facts are looked for and understood in this design style. While this kind of study will spot trends and patterns in data, it does not go as far as to show the reasons behind the observed patterns.

The goal of causal-comparative/quasi-experimental research is to identify the causal links between the variables. Although there are some significant variations, these designs are extremely comparable to actual studies. The effects of an independent variable on the dependent variable are measured, but the investigator does not change the independent variable. The researcher must take advantage of naturally occurring or pre-existing groupings rather than create them randomly.

The scientific method, also known as real experimentation, is used in experimental research to determine the cause-and-effect link between the many study-related factors. The actual experiment is frequently viewed as a laboratory study, although this is not necessarily the case; the lab environment has no bearing on it.

Limitations of Quantitative Research

The fact that quantitative research techniques only provide a surface-level understanding of a phenomenon and ignore test-takers and testers' experiences as well as what they mean by certain terms is one of its limitations.

Advantages of Quantitative Research

1. can be examined and tested.

To do quantitative research, thorough experimental planning and the capacity for universal test and result replication are essential. As a result, the information you collect is more trustworthy and less subject to debate.

2. Clearly Stated Analysis 

The findings you get from collecting quantitative data can help you decide which statistical tests to run. As a result, your data interpretation and presentation of your findings will be simple and less vulnerable to mistakes and subjectivity.

3. Prestige

Many individuals don't comprehend the mathematics needed in such research; thus, it is valued and remarkable when it requires extensive statistics and data analysis. Technical innovations like computer modeling, stock picking, portfolio evaluation, and other data-driven business choices are connected to quantitative research.

Qualitative vs Quantitative Research - A Comparison

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When Do You Use Qualitative and Quantitative Research?

To understand qualitative research, let’s take the following example.

Qualitative Research Examples

Suppose a bookstore owner is looking for ways to improve their sales and customer outreach. An online community of readers who were the bookstore's loyal customers were interviewed, and related questions were asked, and they answered the questions. In the end, it was found that most of the books in the stores were for adults, and there were not sufficient books for children or teenagers.

By conducting this qualitative research, the bookstore owner realized what the shortcomings were and what were the feelings of the readers. Through this research, the bookstore owner can now keep books for different age groups and improve his sales and customer outreach. 

Qualitative Research Examples

Let's consider another example to understand quantitative research. Suppose any organization likes to conduct a customer satisfaction (CSAT) survey. For that, a customer satisfaction survey template can be implemented. Through this survey, a company can collect quantitative data and metrics on the goodwill of the brand or the company in the mind of the customer based on several parameters such as product quality, pricing, and customer experience. This data can be gathered by asking a net promoter score (NPS) question, and matrix table questions that provide data in the form of numbers that can be analyzed and worked upon using various analytics tools.

Now, let’s talk about Qualitative vs. Quantitative Research based on how data is collected for these research methods. 

Data Collection

Now, let’s talk about Qualitative vs. Quantitative Research based on the kind of research approaches they adopt.

Research Approach

For any research, sample data is important to derive meaningful information. Let’s understand Qualitative vs. Quantitative Research based on research samples.

Research Samples

With that, let’s now get an idea about the role of the researcher in qualitative and quantitative research.

Role of the Researcher

Now, let’s learn about Qualitative vs. Quantitative Research based on the scientific methods that are used in these techniques.

Scientific Method

Analyzing Data

Final report.

You may prefer to use only one type of research within a study, but the data generated from the research might not provide the desired results. To implement an unbiased research project that will provide accurate and meaningful insights, it is advised to consider both qualitative and quantitative research methods to get the right results. After reading this article, you would have learned the major differences between qualitative and quantitative research. 

If you want to learn more about different research techniques or how they impact your data and data analysis, then check out our extensive course on Data Analytics . Get in-depth with your analysis and jumpstart your career as a Data Analyst.

Do you have any questions related to Qualitative vs Quantitative Research? If so, then please put it in the comments section of this article. Our team will help you solve your queries at the earliest. 

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About the Author

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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Empirical & Non-Empirical Research

• Quantitative vs. Qualitative
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What's the Difference Between Qualitative and Quantitative?

Distinguishing quantitative & qualitative methods, word clues to identify methods.

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What’s the Difference Between Qualitative and Quantitative Methods?

From: Suter, W. N. (2012). Qualitative Data, Analysis, and Design. In  Introduction to educational research: A critical thinking approach . SAGE Publications, Inc., www.galileo.usg.edu/redirect?inst=pie1&url=https://dx.doi.org/10.4135/9781483384443

The words in this table can be used to evaluate whether an article tends more toward the quantitative or qualitative domain. Well-written article abstracts will contain words like these to succinctly characterize the article's content.

Adapted from: McMillan, J. H. (2012).  Educational research: Fundamentals for the consumer  (6th ed.). Boston, MA: Pearson.

Search SAGE Research Methods for resources about qualitative methods

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The External Validity of Laboratory Experiments: Qualitative Rather Than Quantitative Effects

• Judd B. Kessler , L. Vesterlund
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On the generalizability of experimental results in economics: with a response to commentors, on doing relevant and rigorous experiments: review and recommendations.

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The case for laboratory experiments in behavioural public policy

Individual cheating in the lab: a new measure and external validity, on the external validity of construction bidding experiment, on the external validity of laboratory tax compliance experiments, the paternalistic turn in behavioral law and economics: a critique, subject pool effects in price competition games: students versus professionals, subject pools and deception in agricultural and resource economics experiments, 48 references, what do laboratory experiments tell us about the real world, what do laboratory experiments measuring social preferences reveal about the real world.

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Artificiality: The tension between internal and external validity in economic experiments

Lab experiments are a major source of knowledge in the social sciences, nber working paper series the behavioralist meets the market: measuring social preferences and reputation effects in actual transactions, nber working paper series field experiments in economics: the past, the present, and the future, on the scope of experiments in economics: comments on siakantaris, viewpoint: on the generalizability of lab behaviour to the field, fundraising through competition: evidence from the lab, laboratory experiments: professionals versus students, related papers.

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18 The External Validity of Laboratory Experiments: The Misleading Emphasis on Quantitative Effects

• Published: February 2015
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This chapter comments on the papers of Levitt and List and of Camerer. It explains why for most laboratory studies it is only relevant whether the qualitative or directional results of the study are externally valid. It argues that laboratory studies are conducted to identify general principles of behavior and therefore promise to generalize. It then examines whether laboratory experiments live up to this promise. It discusses the extent to which qualitative results persist outside of the lab and how we should respond when they do not. The chapter concludes by arguing that the lab and field methodologies are highly complementary and that both provide important insights to the understanding of economics.

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Science Education (General Chemistry)

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Applications and Summary

Common lab glassware and uses.

Source: Laboratory of Dr. Neal Abrams — SUNY College of Environmental Science and Forestry

Glassware is a regular appearance in the professional chemistry laboratory, because it has a relatively low cost, extreme durability, and specific levels of precision. While some labware is being supplemented with plastic or even everyday kitchen materials, glass is still the standard material by which laboratory work is done. While there are few rules about glassware, there are some best practices for use that set the groundwork for good techniques in the lab.

Glass is ubiquitous in the chemistry laboratory, but not all glass is the same. Standard consumer-grade glass is known as "soda-lime" or "float" glass. It is good for many applications, but cracks under rapid heating and cooling applications due to expansion/contraction. Borosilicate glass is used to solve this problem in the lab. Made with an introduction of small amounts of boron, borosilicate glass has a very low coefficient of expansion, which prevents internal stresses. The most common trade name for borosilicate glass is Pyrex, the same type of glass used in some kitchen bakeware.

While borosilicate glass is thermally robust, the impurities found in borosilicate and standard glass lead to a limited temperature range and optical quality. Fused silica, or quartz, is used in situations where glass needs to be heated above 450 °C or to be transparent to UV light. Fused silica is chemically-pure silicon dioxide with no impurities and a very high melting point above 1,600 °C. The easiest way to tell the difference between borosilicate glass and fused silica in the lab is to look down the long axis of a piece of glassware. A greenish color is indicative of borosilicate impurities, whereas fused silica is optically clear and colorless.

Standard laboratory glassware, like beakers and flasks, has a limited accuracy of measuring volume, typically ±5%. Volumetric glassware, however, is considered very accurate. This accuracy is known to the user through a few different pieces of information on the glassware. For one, an etched line or volume marking is typically located on volumetric glassware to indicate a volume. The next piece of information is the temperature at which the glassware is accurate, typically 20 °C. This is important because the density (and volume) of a liquid are dependent on temperature. Thirdly, the notations "TD" or "TC" are used to indicate "to deliver" or "to contain", respectively. When a piece of glass is marked as "TD", it is calibrated to accurately deliver the stated volume, whereas glassware with the "TC" marking only contains a specified volume, but it may not transfer to another vessel accurately.

Glassware can be sealed using a variety of stoppers, typically rubber, cork, or glass. Rubber and cork stoppers fit into standard glass necks, though cork is being phased out, and newer stoppers made of neoprene are taking over. Stoppers are conical in shape and fit like a wedge into the glassware. Stoppers can have anywhere from 0 – 3 holes, allowing for connections to tubing or inserting thermometers and stirrers. A variation of the stopper is the septum, which can be used to seal glassware and allows for easy access with a syringe needle. The downside of most flexible stoppers is that they break down over time, though newer Teflon stoppers are more robust but lack the physical flexibility. Ground glass stoppers are used to seal flasks that have ground glass fittings. While the seal is very good, glass-to-glass connections are known to seize, so joint grease (vacuum, Krytox, etc.) is often used to prevent this. Rubber stoppers are sized by number, ranging from 000 – 10, whereas glass stoppers are sized by the diameter and length of the sealing section. For example, a stopper marked as 24/40 is 24 mm in diameter at its widest part and 40 mm long on the tapered edge, which would fit into a flask with a 24/40 opening.

Connections between pieces of glassware are made using a variety of ground glass joints including a standard taper, ball-and-socket, and O-ring. The standard taper is the most common fitting. Glass joints are sized to fit into one another and a variety of size adapters are available. Like all other glass joints, grease is required to prevent seizing. While the joint may be sealed, it is not a mechanically strong connection and can fall apart. To prevent glass pieces from separating, connector clips are used, which are sometimes referred to as Keck clips. These clips are color-coded for the size of the joint. Alternatives to connector clips include springs and wire.

Clamping and supporting glassware is a vital part of a successful experiment. While some pieces of glassware, like beakers and Erlenmeyer flasks, have flat bottoms that can sit flat on a hotplate, other pieces of glassware, like round-bottom flasks, need to be supported using clamps. Even with flat-bottom glassware, it can be far too easy for something like a vacuum filtration flask to fall over. Metal clamps are connected to the neck of a piece of glassware using either a three-finger or a standard clamp. The other end of the clamp is then attached to a ring stand (or retort stand). Other clamps exist for special purposes, like chain-style for large pieces or water-bath clamps for thermometers. The lab jack uses a scissoring action to raise or lower a piece of glassware. This is very convenient for large or heavy items and, when used in conjunction with a cork ring, can also be used to move round-bottom flasks.

Just like in the kitchen, soap and water are typically used to clean glassware in the lab. When that fails, organic solvents, like acetone, are sometimes employed to remove sticky and insoluble organic deposits. Even then, some compounds adhere to glassware so well that they are impossible to remove without some form of chemical etching. In the case of organic carbon-containing deposits, glassware can be soaked in a base bath composed of an alcohol (ethanol) and a strong base (sodium hydroxide). This bath etches thin molecular layers of glass from the vessel, taking the stubborn deposits with it. It is very important to never place volumetric glassware in a base bath, which could lead to etching and a change in volume. When a metal has plated or infused into a piece of glassware, an acid bath made with a dilute strong acid, like hydrochloric, is used. The amphoteric nature of glass and the general oxidation of metal in acid lead to its cleaning power. Regardless of the bath type, 24–48 h is required for effective deposit removal.

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1. Glassware for Qualitative Uses

• The beaker is one of the most common pieces of glassware in the laboratory. It is a simple cylindrical container used to hold solids and liquids with sizes ranging from very small (10 mL) to very large (4,000 mL). It has a lip for ease of pouring and decanting liquids. The graduations are approximate, but very useful when exact volumes are not needed.
• Flasks are designed so the contents can be swirled without spilling. They are also easily fitted with stoppers and often have the stopper size written directly on the flask.
• The most common of all flasks, the Erlenmeyer flask has a flat bottom with approximate graduations. The flat bottom allows the Erlenmeyer flask to be directly heated and used in simple reflux (boiling) and condensation procedures.
• The Florence flask is a hybrid between the round bottom and the Erlenmeyer flask and ranges from a few hundred milliliters to a few liters in size. Florence flasks can have either a flat bottom or a round bottom, so applications vary from direct heating to using a heating mantle. It does not have a ground glass joint, so a stopper is used to seal the container. The rounded shape is better for applications that involve boiling.
• Test tubes are relatively small cylindrical vessels used to store, heat, and mix chemicals. While the test tube comes in specific sizes, it's typically used in qualitative observational procedures.
• The watch glass is used when a high surface area is needed for a small volume of liquid. This is common for crystallizing and evaporating, as well as other qualitative procedures. Watch glasses can also be used as covers for beakers, but not flasks.
• The crystallization dish is a hybrid between a watch glass and the Petri dish (common in biological procedures). It has a low height-to-width ratio, which means the sides are very low compared to the width of the vessel. This allows for high surface areas for evaporation, but the crystallization dish is more commonly used as a short-term container for liquids in a variety of bath processes (water, acid, or oil).

2. Glassware for Measuring

• The graduated cylinder is used to measure a semi-precise volume of liquid. While it is not as precise as volumetric glassware, it is much more accurate and precise than a beaker or flask (to within 1%). Volumes are measured to the bottom of the meniscus for aqueous solutions and the top of the meniscus for non-aqueous hydrophobic solutions. Graduated cylinders are general-use pieces of "TD" glassware, where the delivery volume is important. Higher levels of accuracy require volumetric glassware.
• Used for making standardized (high precision) solutions, where precision is known to four significant figures.
• Volumetric flasks are a mainstay when preparing any standardized solution. Since volumes are not necessarily additive, the volumetric flask is used to make solutions of precise volumes. The etched mark on the neck of the glassware signifies the volume to high precision at the specified temperature. A solution is prepared by adding enough solvent to dissolve the solute, then the solute is added and dissolved. The solution is then diluted to the mark using the solvent. The solution is mixed throughout the dilution process and sometimes requires being placed in an ice bath in the case of exothermic dissolution (typically strong acids or bases). Volumetric flasks range in size from 1 mL to 4,000 mL and larger.
• Volumetric pipettes are known for high precision, like volumetric flasks, but are used to dispense liquids, typically in the preparation of solutions in a volumetric flask. The pipette also has an etched mark denoting a precise volume, and the solution is drawn into the pipette using a pipette bulb, never by mouth.
• Micropipettes are a specialized class of volumetric pipettes used for very small volumes from 1 µl to 1,000 µL. The micropipette uses plastic disposable tips, but these can be re-used under appropriate situations. Most micropipettes have an adjustable range of volumes using separate withdraw and dispense actions on the pipette body. The mechanism for adjusting, determining volume limits, and ejecting disposable tips varies by manufacturer.
• The burette is an analytical piece of glassware used to dispense variable (but precise) volumes of liquids. Commonly found in analytical chemistry, the burette is used in a variety of titration experiments.

3. Procedural Glassware

• Round-bottom flasks, or boiling flasks, are typically found in synthesis experiments, since the round shape allows for even heating and stirring. The neck typically has a female ground-glass joint and can be attached to condensers and other pieces of glassware. To prevent spills, the solution volume should not exceed 50% of the flask volume. Sizes range from 50 mL to 20,000 mL.
• While most common to the organic chemistry lab, the separatory funnel is used to separate liquids of different densities and solubilities. The bottom of the separatory funnel is very narrow and leads to a stopcock, allowing for precise separations of liquids, while the top is very wide for ease in shaking and mixing.
• The filter flask looks like an Erlenmeyer flask, but has a hose barb near the top to attach a vacuum hose. The flask typically has thicker walls than an Erlenmeyer due to the reduced pressure (vacuum) used with the flask. Vacuum (Büchner) funnels fit into the neck of the flask using a rubber collar or a 1-hole rubber stopper.
• Traditional funnels used for gravity filtration have a wide cone-shaped body, for adding and filtering solutions, and a long narrow stem, for delivery into a flask. Filter paper is folded into a cone shape, inserted into the funnel, and wetted with a solvent (typically water). The powder funnel has a wider stem designed for dispensing solids and viscous liquids. Filter paper is only used in conjunction with the filter funnel.
• The ceramic Büchner funnel fits into the filter (Büchner) flask using a rubber cone or 1-hole rubber stopper. The funnel is typically made of ceramic with pin-sized holes in the flat bottom. Filter paper is placed on top of the holes and wetted with solvent (water) to prevent solids from getting under the filter paper.
• A crucible is made of ceramic and holds small amounts of chemicals during heating at high temperatures. Depending on the specific type, the crucible can withstand temperatures above 1,000 °C and is used in conjunction with a Bunsen burner or furnace. Common uses include heating a hydrated solid to remove water or combusting a compound to determine organic content.
• While the mortar and pestle originated in chemistry (and alchemy) laboratories, it is more common in pharmacology, biology, and culinary applications. Made of ceramic or stone, materials are placed in the bowl-shaped mortar and ground and crushed using the pestle.

Glassware has long been a core component of the chemistry laboratory.

Glass’s longstanding popularity has remained high because it is relatively inert, highly durable, easily customizable, and inexpensive.

Because of these desirable traits, glass has been used to create a wide assortment of apparatuses. Being unfamiliar with this equipment could lead to confusion, misuse and disaster. Therefore, a solid understanding of glassware is necessary to ensure safety and success in the lab.

This video will explore many of the common pieces of glassware found in the laboratory.

Laboratory glassware is manufactured with different compositions, each possessing unique properties that are useful in different experimental conditions.

Equipment made from consumer-grade, or "soda-lime", glass is the least expensive, and is adequate for many applications. However, rapid temperature changes can cause this glass to crack.

Borosilicate glass, which exhibits little thermal expansion, is preferred in thermally stressful conditions. This glass is manufactured through the addition of small amounts of boron, and is often used in bakeware, such as Pyrex.

However, both borosilicate and standard glass contain impurities, resulting in reduced optical quality. Therefore, a glass composed of purely silicon and oxygen is utilized in situations that require the glass to be transparent to UV light. This is known as fused silica or fused quartz.

Now that you understand the different types of glass used in the laboratory, let’s look at common glassware, as well as related paraphernalia.

We will begin our survey with glassware used for qualitative analysis. Any measurements, or graduations, on this equipment are approximate, and they are best used for procedures that do not require high levels of accuracy. First, the beaker, one of the most common pieces of glassware, is available in a range of sizes. Beakers are often used to hold, mix, and heat reagents. Most have a small lip for pouring liquids.

Test tubes, which are relatively small cylindrical vessels, are also used to store, heat, and mix chemicals. Their design allows for multiple samples to be easily manipulated, stored, and observed at once.

Watch glasses are used when a large surface area is needed for a small volume of liquid. This is common for crystallizing and evaporating procedures. Watch glasses can also be used as covers for beakers.

The crystallization dish is similar to the watch glass, proving a large surface area for liquids. However, it is more commonly used as a container for bath processes. Lastly, the flask. Each type of flask is shaped for its purpose, but all are designed with wide bodies and narrow necks, allowing the contents to be mixed without spilling. They are also easily fitted with stoppers. The Erlenmeyer flask is the most common. The flat bottom allows it to be directly heated and used in simple boiling and condensation procedures.

Next, we will review glassware used for accurately measuring liquids. The graduated cylinder is used to measure semi-precise volumes, and deliver to another container. The surface of most liquids forms a concave meniscus in narrow glassware. Volume should be read at the bottom for accuracy. 

While the graduated cylinder is versatile, volumetric glassware is used when a higher level of accuracy is required. Volumetric glassware can be an order of magnitude more precise than a graduated cylinder. Each piece is marked with either "TD" or "TC". If the equipment is calibrated to transport the measured volume, it is marked "TD" for "To deliver". Conversely, other pieces of volumetric glassware are only calibrated to be accurate while holding the measured volume, and are marked "TC" for "To Contain".

The volumetric flask is used to make and contain solutions of precise volumes. This is done by first dissolving the solute, and then adding solvent to the graduation to dilute to the intended volume.

Unlike the apparatuses that are accurate only to contain, the volumetric pipette is used to deliver a specific volume with a high degree of accuracy. A bulb is used to draw the liquid, never by mouth.

The burette is used to deliver variable, but precise, volumes of liquid, controlled with the stopcock. It’s often used in titration experiments.

Next, our survey will cover glassware that has more specific procedural uses.

First, the round-bottom, or boiling flask, is designed to allow for even heating and stirring, to drive chemical reactions. To prevent spills, it should never be filled to more than 50% of its total volume.

While traditional funnels have a familiar shape, there can be variations depending on their intended use. For example, funnels used for gravity filtration are fitted with folded filter paper. Powder funnels have wider stems designed for dispensing solids and viscous liquids.

The separatory funnel is used in liquid-liquid extractions to separate immiscible liquids of different densities. It has a specialized shape, with a wide top for mixing, and a narrow bottom leading to a stopcock for the separation. The Büchner flask and funnel are used for vacuum filtration. The funnel is typically ceramic, with pin-sized holes in its flat bottom. It is fitted into the flask with a rubber collar to provide an airtight seal. The flask resembles an Erlenmeyer in shape, but has a barbed side arm for the vacuum hose.

In some chemical processes, laboratory glassware may need to be sealed, connected, or supported. Sealing glassware is typically done with a stopper. Rubber and neoprene are used in pieces with standard necks. They can be manufactured with holes to allow for the insertion of tubes, thermometers, or stirrers, while still providing an airtight seal.

Glass stoppers are used to seal equipment with ground glass fittings. These provide a strong seal, but the possibility of glass to glass seizing necessitates the use of joint grease. Joint grease must also be used when connecting two pieces of glassware together. However, because these joints are not mechanically strong, plastic connector clips are used to prevent them from separating.

When additional structural support is needed, glassware is often clamped in place. Clamps provide this support by connecting to a piece’s neck on one end, and a retort stand on the other. While some glassware should always be secured, clamping can also be used to ensure that components stay upright during a procedure.

Now that we've surveyed many of the pieces of glassware found in professional laboratories, we'll discuss some of their many uses.

Observation of naturally occurring, spontaneous reactions can be performed in the lab by replicating their original conditions. Glassware is vital to these investigations because of its inert and durable nature.

In the Miller-Urey experiment, the environment of early earth was simulated in a round-bottomed flask to investigate the abiotic synthesis of organic compounds. A large manifold of interlocking glassware helped to provide the necessary atmospheric gasses, which was then sparked, simulating lighting. The product was pipetted out of the flask to avoid contamination, and stored for further investigation.

When synthesizing organic molecules, it is often necessary to apply heat for long periods of time. In this example, a carbon-carbon cross-coupling reaction was performed using an apparatus made from three pieces of glassware. The apparatus - made from a round-bottomed flask, a reflux condenser, and an oil bubbler - allows for the solution to be boiled indefinitely, without losing volume or changing pressure.

You've just watched JoVE's introduction to Common Glass Laboratory Equipment and Their Uses. You should now be familiar with the glassware used for qualitative, measuring, and procedural applications.

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While there are few rules to how glassware must be used, each piece of glassware was designed for a general set of procedures. Unique situations create some flexibility on the application, and nearly all glassware can be further adapted and customized with the assistance of a professional glassblower.

Glass’s longstanding popularity has remained high because it is relatively inert, highly durable, easily customizable, and inexpensive.

Equipment made from consumer-grade, or "soda-lime", glass is the least expensive, and is adequate for many applications. However, rapid temperature changes can cause this glass to crack.

Now that you understand the different types of glass used in the laboratory, let’s look at common glassware, as well as related paraphernalia.

Next, we will review glassware used for accurately measuring liquids. The graduated cylinder is used to measure semi-precise volumes, and deliver to another container. The surface of most liquids forms a concave meniscus in narrow glassware. Volume should be read at the bottom for accuracy. 

While the graduated cylinder is versatile, volumetric glassware is used when a higher level of accuracy is required. Volumetric glassware can be an order of magnitude more precise than a graduated cylinder. Each piece is marked with either "TD" or "TC". If the equipment is calibrated to transport the measured volume, it is marked "TD" for "To deliver". Conversely, other pieces of volumetric glassware are only calibrated to be accurate while holding the measured volume, and are marked "TC" for "To Contain".

The burette is used to deliver variable, but precise, volumes of liquid, controlled with the stopcock. It’s often used in titration experiments.

First, the round-bottom, or boiling flask, is designed to allow for even heating and stirring, to drive chemical reactions. To prevent spills, it should never be filled to more than 50% of its total volume.

The separatory funnel is used in liquid-liquid extractions to separate immiscible liquids of different densities. It has a specialized shape, with a wide top for mixing, and a narrow bottom leading to a stopcock for the separation. The Büchner flask and funnel are used for vacuum filtration. The funnel is typically ceramic, with pin-sized holes in its flat bottom. It is fitted into the flask with a rubber collar to provide an airtight seal. The flask resembles an Erlenmeyer in shape, but has a barbed side arm for the vacuum hose.

In some chemical processes, laboratory glassware may need to be sealed, connected, or supported. Sealing glassware is typically done with a stopper. Rubber and neoprene are used in pieces with standard necks. They can be manufactured with holes to allow for the insertion of tubes, thermometers, or stirrers, while still providing an airtight seal.

When additional structural support is needed, glassware is often clamped in place. Clamps provide this support by connecting to a piece’s neck on one end, and a retort stand on the other. While some glassware should always be secured, clamping can also be used to ensure that components stay upright during a procedure.

Now that we've surveyed many of the pieces of glassware found in professional laboratories, we'll discuss some of their many uses.

You've just watched JoVE's introduction to Common Glass Laboratory Equipment and Their Uses. You should now be familiar with the glassware used for qualitative, measuring, and procedural applications.

JoVE Science Education Database. General Chemistry. Common Lab Glassware and Uses. JoVE, Cambridge, MA, (2024).

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Qualitative vs Quantitative Data in Surveys: What’s Better?

Deciding whether to collect customer data through stories (qualitative data) or numbers (quantitative data) can be tricky. 

Many researchers and marketers find themselves stuck between the detailed insights of qualitative data and the clear, measurable facts of quantitative data. 

It then creates a big debate about which method, qualitative vs quantitative, is better for surveys. 

Let’s talk about it!

What is qualitative data?

Qualitative data refers to non-numerical information gathered through methods like interviews, focus groups, and open-ended questions in surveys. 

➡️ GOAL : providing in-depth insights into human behavior, motivations, and attitudes. It gives a better understanding of the subject matter.

What is quantitative data?

Quantitative data consists of numerical information that can be measured and analyzed statistically. It is collected through methods such as surveys with closed-ended questions , experiments, and observations

➡️ GOAL: providing a broad overview of trends and patterns across a large sample.

When to use qualitative data in research?

Have you got doubts about that? Let’s clear them.

Exploring new phenomena

When conducting research in areas where little is known, qualitative data collection methods are invaluable. 

They let researchers gather qualitative insights through open-ended questions, focus groups, and interviews . It also provides a rich, detailed understanding of the subject matter. 

Useful in: qualitative studies exploring concepts, behaviors, or experiences in depth, offering a foundation for further quantitative research.

However, you won’t gather the data effectively without a robust tool for that. Surveylab provides many question types suitable for collecting both: qualitative and quantitative data. 

For example: open-ended questions, single choice, multiple choice, matrix, numeric / slider, NPS , and more.

Other Surveylab’s superpowers:

• multi-language surveys , 
• dedicated customer and domain support ,
• ideal for mobile surveys  
• integrations with other tools ( CRM, eShop, BI / DWH)

Check out pricing and other features .

Understanding contexts and complexities

Qualitative research shines when the goal is to understand the context behind behaviors, decisions, or perceptions. 

Through methods like thematic analysis and discourse analysis , qualitative researchers can interpret non-numerical data to uncover patterns and meanings that numerical data cannot reveal. 

Useful in: fields like sociology or anthropology, where the nuances of human interaction and culture are central.

Developing concepts and theories

In the early stages of research, particularly when developing theories or concepts, qualitative data provides the depth and flexibility needed to form hypotheses.  

Useful in: collecting data from focus groups or in-depth interviews to build theoretical frameworks that explain how and why certain phenomena occur.

Gaining user or customer insights

For businesses and designers, qualitative research offers a pathway to deep user or customer insights. 

Gathering qualitative data through user interviews, customer feedback, or focus groups helps understand users’ needs, preferences, and pain points.

Useful in: developing new products, services, or features that closely meet customer expectations and improve the overall user experience.

Evaluating social programs

Qualitative data is essential for evaluating the impact of social programs or interventions. 

Unlike quantitative data, qualitative feedback from participants provides insights into how and why a program succeeded or failed. 

This can include participants’ personal stories, experiences, and perceptions. 

Useful in: comprehensive view of the program’s effectiveness and areas for improvement.

When to use quantitative data in research

As it’s a different approach, you need to know when to use quantitative data.

Measuring variables quantitatively

Quantitative research is the go-to when the objective is to measure variables and analyze numerical data statistically. 

The approach is suited for studies that require quantitative data collection methods like surveys with closed-ended questions or experiments where numerical values can be assigned to outcomes. 

Useful in: fields like psychology or economics, where researchers seek to quantify behaviors, attitudes, or conditions.

Testing hypotheses or theories

When researchers want to test hypotheses or validate theories , quantitative research methods provide the rigor and structure needed. 

Through controlled experiments and statistical analyses , quantitative data allows for hypothesis testing. It enables researchers to draw conclusions based on empirical evidence. 

Useful in: establishing causal relationships and validating theoretical models.

Identifying trends and patterns

Using statistical analysis and descriptive statistics , researchers can analyze quantitative data to uncover significant trends, correlations, or differences within the data. 

Useful in: market research, epidemiology, and other fields where understanding broad patterns is essential.

Generalizing findings to a larger population

Quantitative research methods, particularly those involving a random sample , are designed to generalize findings from a sample to a larger population. 

Useful in: researching broad inferences about a group’s behaviors, attitudes, or characteristics, ensuring that the conclusions drawn are statistically significant and representative.

Comparing groups or conditions

Through quantitative analysis, researchers can use numerical data to conduct comparative studies, and employ statistical analyses to determine if significant differences exist between groups. 

Useful in: clinical trials, educational research, and any study where comparing outcomes is key.

Qualitative and quantitative data: key differences

To make things clearer, we’ve compiled a list of key differences, with a quick explanation.

Nature of data

✔️ Qualitative research focuses on textual data, gathering qualitative data through methods like interviews and focus groups. All to gain insights into the subjective nature of human experiences. 

✔️ Quantitative research deals with numeric data, employing quantitative data collection methods to gather numerical values that can be analyzed using inferential statistics.

Analysis methods

✔️ Qualitative data analysis involves interpreting non-numerical data, often through thematic or content analysis, to uncover patterns and meanings. 

✔️ Quantitative analysis , however, relies on statistical analyses to test hypotheses and draw conclusions based on numerical data, using descriptive and inferential statistics to quantify relationships and differences.

Research aims and objectives

✔️ Qualitative research aims to explore the depth, meaning, and complexity of phenomena. It focuses on the subjective interpretation of data to provide in-depth insights. 

✔️ Quantitative research seeks to quantify variables and generalize findings from a sample to a larger population. The goal is to identify trends, test theories, and establish causal relationships.

📚 Read: how to analyze survey data and best practices for that .

Approach to data collection

✔️ Qualitative researchers gather qualitative data through open-ended questions and discussions. Understanding the participants’ context and perspectives is the goal.

✔️ Quantitative researchers , on the other hand, collect data through structured methods like surveys and experiments. Here, the focus is on generating quantifiable evidence that can be statistically analyzed.

Role of the researcher

✔️ In qualitative research , the researcher often plays a more active role in interpreting data, with a focus on analyzing qualitative insights and the subjective experiences of participants. 

✔️ Quantitative researchers maintain a more detached stance, focusing on objective measurement and analysis to ensure that the findings are not influenced by the researcher’s biases.

📚 Read: what is non-response bias and why it matters?

Key similarities of qualitative and quantitative data collecting methods

As those two methods differ, there are also similarities.

Objective of understanding

Both qualitative and quantitative research share the objective of understanding human behavior, social phenomena, or specific research questions.  

Whether through qualitative or quantitative data, both approaches aim to gain insights into their respective areas of study, contributing valuable knowledge to the field.

Use of mixed methods

Another similarity is the increasing use of mixed methods, combining qualitative and quantitative research in a single study . 

The approach uses the strengths of both methods to provide a more comprehensive understanding of research questions, It allows researchers to explore complex issues with both depth and breadth.

Importance of rigorous data collection

They emphasize the importance of rigorous data collection processes . 

When collecting qualitative or quantitative data, they ensure that the data is reliable and valid so they can make accurate conclusions.

Contribution to knowledge

These research methods contribute to the expansion of knowledge within various fields . 

They explore new concepts and test theories. They also help to fill gaps in understanding, contributing to the development of new theories and practices.

Ethical considerations

Both qualitative and quantitative research are bound by ethical considerations. They ensure the research is conducted responsibly and with respect for participants . 

For example: obtaining informed consent, ensuring confidentiality, and minimizing any potential harm to participants. All to highlight the shared values and standards that guide research practices across methodologies.

📚 Read: 10 tricks to help you build better surveys

How to tackle qualitative vs quantitative in surveys – best practices

Do you feel quite overwhelmed? Check out our tips!

Balancing qualitative and quantitative questions

Start with quantitative data questions to get statistical insights, then use qualitative questions to explore respondents’ thoughts and feelings in more depth. 

It’s a balanced approach that combines numerical value with subjective insights for a deeper understanding.

Designing effective qualitative questions

When crafting qualitative questions, go for open-ended questions that encourage detailed responses. Use qualitative research methods like thematic analysis to identify patterns and themes in the responses. 

You can gain a deeper understanding of the numbers and their context by understanding the nuances behind them.

Utilizing quantitative data for broad insights

Quantitative questions should be designed to collect numeric data that can be easily analyzed through statistical analysis. We can use this quantitative data to catch the trends, patterns, and general behaviors across a large sample. 

Leveraging descriptive statistics and quantitative data analysis, researchers can quantify attitudes and opinions. They get a broad overview of the study population.

Analyzing qualitative data thoroughly

Qualitative data analysis requires a detailed approach to interpreting open-ended responses. Techniques such as qualitative analysis and thematic analysis help researchers to explore textual data, uncover underlying meanings and gain qualitative insights. 

I t’s essential to understand the “why” behind the numbers in quantitative data.

Applying statistical analyses to quantitative data

For quantitative data, employ statistical analyses to validate findings and draw conclusions. Data patterns can be summarized using descriptive statistics and inferential statistics. 

Then, you may get robust and reliable results for both quantitative and qualitative research.

Leveraging mixed methods for comprehensive insights

Adopt a mixed methods approach that combines qualitative and quantitative research – it’s a way for enriched data collection and analysis. 

Researchers then can explore a topic through qualitative data and then measure those findings with quantitative data, or vice versa. 

Ensuring quality in data collection methods

High-quality data collection is vital, no matter if the focus is on qualitative or quantitative data. Reliable data collection methods, such as: 

• carefully designed surveys and focus groups for qualitative data, 
• and structured questionnaires for quantitative data, 

Provide the research findings with validity and reliability.

Navigating the advantages and disadvantages

Understanding the advantages and disadvantages of qualitative vs quantitative research is key to choosing the right approach for your study. 

While qualitative studies offer depth and detail, quantitative studies provide breadth and generalizability. 

When deciding how to tackle their survey design for optimal results, researchers should consider: 

• their research goals, 
• the nature of the data, 
• and the intended analysis methods 

Source: Designs.ai

Key takeaways

• Combining quantitative and qualitative data in surveys provides a comprehensive understanding of research topics.
• Quantitative data refers to numerical information that can be statistically analyzed for broad insights.
• Qualitative methods involve open-ended questions that explore participants’ thoughts and experiences in depth.
• A quantitative researcher focuses on collecting and analyzing numerical data to identify trends and patterns.
• Gap analysis can benefit from both quantitative and qualitative data to identify and address discrepancies between current and desired states.
• Nursing research often utilizes both qualitative and quantitative studies to improve patient care and healthcare practices.
• Developing strong research skills is essential for effectively designing and conducting mixed-methods research.
• Focus group discussions are a valuable qualitative method for gathering detailed feedback and insights.
• A quantitative study aims to quantify variables and often uses statistical methods to test hypotheses.
• A qualitative study seeks to understand the meaning, characteristics, and descriptions of phenomena, providing rich, detailed insights.

Conclusion on qualitative and quantitative research

Mastering the blend of quantitative and qualitative research is super important to unlocking deeper insights.

Crunching numbers for clear trends? Diving into discussions for nuanced understanding? Each method offers its own strengths. 

As you refine your research skills, remember: the best insights come from combining the clarity of quantitative data with the depth of qualitative analysis. 

Now, it’s your turn to take these strategies and turn them into actionable insights. And it wouldn’t be smooth without the surveying tool. Sign up for SurveyLab , and make data collection a breeze.

FAQ on qualitative vs quantitative data

Do you have any questions? Check out our answers.

Qualitative data includes non-numerical information from interviews, focus groups, and open-ended survey questions. It helps understand human behavior and attitudes deeply.

Quantitative data consists of numerical information from surveys, experiments, and observations, useful for analyzing trends and patterns.

They should use qualitative data to explore new topics deeply, understand complex issues, or when developing new theories and concepts.

Quantitative data helps measure variables, test hypotheses, and generalize findings to larger populations through statistical analysis.

Mixed methods combine the strengths of both qualitative and quantitative approaches, providing comprehensive insights into research topics.

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