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Quantitative and Qualitative Research

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

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

What is the purpose of quantitative research?

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

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

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

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

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

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

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

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

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

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Table of contents

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

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

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

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

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

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

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

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

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

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

• Replication

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

• Direct comparisons of results

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

• Large samples

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

• Hypothesis testing

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

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

• Superficiality

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

• Narrow focus

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

• Structural bias

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

• Lack of context

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

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 .

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.

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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

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

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Quantitative Research

What is Quantitative Research?

Quantitative research is the methodology which researchers use to test theories about people’s attitudes and behaviors based on numerical and statistical evidence. Researchers sample a large number of users (e.g., through surveys) to indirectly obtain measurable, bias-free data about users in relevant situations.

“Quantification clarifies issues which qualitative analysis leaves fuzzy. It is more readily contestable and likely to be contested. It sharpens scholarly discussion, sparks off rival hypotheses, and contributes to the dynamics of the research process.” — Angus Maddison, Notable scholar of quantitative macro-economic history

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See how quantitative research helps reveal cold, hard facts about users which you can interpret and use to improve your designs.

Use Quantitative Research to Find Mathematical Facts about Users

Quantitative research is a subset of user experience (UX) research . Unlike its softer, more individual-oriented “counterpart”, qualitative research , quantitative research means you collect statistical/numerical data to draw generalized conclusions about users’ attitudes and behaviors . Compare and contrast quantitative with qualitative research, below:

Qualitative Research

You Aim to Determine

The “what”, “where” & “when” of the users’ needs & problems – to help keep your project’s focus on track during development

The “why” – to get behind how users approach their problems in their world

Highly structured (e.g., surveys) – to gather data about what users do & find patterns in large user groups

Loosely structured (e.g., contextual inquiries) – to learn why users behave how they do & explore their opinions

Number of Representative Users

Ideally 30+

Often around 5

Level of Contact with Users

Less direct & more remote (e.g., analytics)

More direct & less remote (e.g., usability testing to examine users’ stress levels when they use your design)

Statistically

Reliable – if you have enough test users

Less reliable, with need for great care with handling non-numerical data (e.g., opinions), as your own opinions might influence findings

Quantitative research is often best done from early on in projects since it helps teams to optimally direct product development and avoid costly design mistakes later. As you typically get user data from a distance—i.e., without close physical contact with users—also applying qualitative research will help you investigate why users think and feel the ways they do. Indeed, in an iterative design process quantitative research helps you test the assumptions you and your design team develop from your qualitative research. Regardless of the method you use, with proper care you can gather objective and unbiased data – information which you can complement with qualitative approaches to build a fuller understanding of your target users. From there, you can work towards firmer conclusions and drive your design process towards a more realistic picture of how target users will ultimately receive your product.

Quantitative analysis helps you test your assumptions and establish clearer views of your users in their various contexts.

Quantitative Research Methods You Can Use to Guide Optimal Designs

There are many quantitative research methods, and they help uncover different types of information on users. Some methods, such as A/B testing, are typically done on finished products, while others such as surveys could be done throughout a project’s design process. Here are some of the most helpful methods:

A/B testing – You test two or more versions of your design on users to find the most effective. Each variation differs by just one feature and may or may not affect how users respond. A/B testing is especially valuable for testing assumptions you’ve drawn from qualitative research. The only potential concerns here are scale—in that you’ll typically need to conduct it on thousands of users—and arguably more complexity in terms of considering the statistical significance involved.

Analytics – With tools such as Google Analytics, you measure metrics (e.g., page views, click-through rates) to build a picture (e.g., “How many users take how long to complete a task?”).

Desirability Studies – You measure an aspect of your product (e.g., aesthetic appeal) by typically showing it to participants and asking them to select from a menu of descriptive words. Their responses can reveal powerful insights (e.g., 78% associate the product/brand with “fashionable”).

Surveys and Questionnaires – When you ask for many users’ opinions, you will gain massive amounts of information. Keep in mind that you’ll have data about what users say they do, as opposed to insights into what they do . You can get more reliable results if you incentivize your participants well and use the right format.

Tree Testing – You remove the user interface so users must navigate the site and complete tasks using links alone. This helps you see if an issue is related to the user interface or information architecture.

Another powerful benefit of conducting quantitative research is that you can keep your stakeholders’ support with hard facts and statistics about your design’s performance—which can show what works well and what needs improvement—and prove a good return on investment. You can also produce reports to check statistics against different versions of your product and your competitors’ products.

Most quantitative research methods are relatively cheap. Since no single research method can help you answer all your questions, it’s vital to judge which method suits your project at the time/stage. Remember, it’s best to spend appropriately on a combination of quantitative and qualitative research from early on in development. Design improvements can be costly, and so you can estimate the value of implementing changes when you get the statistics to suggest that these changes will improve usability. Overall, you want to gather measurements objectively, where your personality, presence and theories won’t create bias.

Learn More about Quantitative Research

Take our User Research course to see how to get the most from quantitative research.

See how quantitative research methods fit into your design research landscape .

This insightful piece shows the value of pairing quantitative with qualitative research .

Find helpful tips on combining quantitative research methods in mixed methods research .

Questions related to Quantitative Research

Qualitative and quantitative research differ primarily in the data they produce. Quantitative research yields numerical data to test hypotheses and quantify patterns. It's precise and generalizable. Qualitative research, on the other hand, generates non-numerical data and explores meanings, interpretations, and deeper insights. Watch our video featuring Professor Alan Dix on different types of research methods.

This video elucidates the nuances and applications of both research types in the design field.

In quantitative research, determining a good sample size is crucial for the reliability of the results. William Hudson, CEO of Syntagm, emphasizes the importance of statistical significance with an example in our video. 

He illustrates that even with varying results between design choices, we need to discern whether the differences are statistically significant or products of chance. This ensures the validity of the results, allowing for more accurate interpretations. Statistical tools like chi-square tests can aid in analyzing the results effectively. To delve deeper into these concepts, take William Hudson’s Data-Driven Design: Quantitative UX Research Course . 

Quantitative research is crucial as it provides precise, numerical data that allows for high levels of statistical inference. Our video from William Hudson, CEO of Syntagm, highlights the importance of analytics in examining existing solutions. 

Quantitative methods, like analytics and A/B testing, are pivotal for identifying areas for improvement, understanding user behaviors, and optimizing user experiences based on solid, empirical evidence. This empirical nature ensures that the insights derived are reliable, allowing for practical improvements and innovations. Perhaps most importantly, numerical data is useful to secure stakeholder buy-in and defend design decisions and proposals. Explore this approach in our Data-Driven Design: Quantitative Research for UX Research course and learn from William Hudson’s detailed explanations of when and why to use analytics in the research process.

After establishing initial requirements, statistical data is crucial for informed decisions through quantitative research. William Hudson, CEO of Syntagm, sheds light on the role of quantitative research throughout a typical project lifecycle in this video:

 During the analysis and design phases, quantitative research helps validate user requirements and understand user behaviors. Surveys and analytics are standard tools, offering insights into user preferences and design efficacy. Quantitative research can also be used in early design testing, allowing for optimal design modifications based on user interactions and feedback, and it’s fundamental for A/B and multivariate testing once live solutions are available.

To write a compelling quantitative research question:

Create clear, concise, and unambiguous questions that address one aspect at a time.

Use common, short terms and provide explanations for unusual words.

Avoid leading, compound, and overlapping queries and ensure that questions are not vague or broad.

According to our video by William Hudson, CEO of Syntagm, quality and respondent understanding are vital in forming good questions. 

He emphasizes the importance of addressing specific aspects and avoiding intimidating and confusing elements, such as extensive question grids or ranking questions, to ensure participant engagement and accurate responses. For more insights, see the article Writing Good Questions for Surveys .

Survey research is typically quantitative, collecting numerical data and statistical analysis to make generalizable conclusions. However, it can also have qualitative elements, mainly when it includes open-ended questions, allowing for expressive responses. Our video featuring the CEO of Syntagm, William Hudson, provides in-depth insights into when and how to effectively utilize surveys in the product or service lifecycle, focusing on user satisfaction and potential improvements.

He emphasizes the importance of surveys in triangulating data to back up qualitative research findings, ensuring we have a complete understanding of the user's requirements and preferences.

Descriptive research focuses on describing the subject being studied and getting answers to questions like what, where, when, and who of the research question. However, it doesn’t include the answers to the underlying reasons, or the “why” behind the answers obtained from the research. We can use both f qualitative and quantitative methods to conduct descriptive research. Descriptive research does not describe the methods, but rather the data gathered through the research (regardless of the methods used).

When we use quantitative research and gather numerical data, we can use statistical analysis to understand relationships between different variables. Here’s William Hudson, CEO of Syntagm with more on correlation and how we can apply tests such as Pearson’s r and Spearman Rank Coefficient to our data.

This helps interpret phenomena such as user experience by analyzing session lengths and conversion values, revealing whether variables like time spent on a page affect checkout values, for example.

Random Sampling: Each individual in the population has an equitable opportunity to be chosen, which minimizes biases and simplifies analysis.

Systematic Sampling: Selecting every k-th item from a list after a random start. It's simpler and faster than random sampling when dealing with large populations.

Stratified Sampling: Segregate the population into subgroups or strata according to comparable characteristics. Then, samples are taken randomly from each stratum.

Cluster Sampling: Divide the population into clusters and choose a random sample.

Multistage Sampling: Various sampling techniques are used at different stages to collect detailed information from diverse populations.

Convenience Sampling: The researcher selects the sample based on availability and willingness to participate, which may only represent part of the population.

Quota Sampling: Segment the population into subgroups, and samples are non-randomly selected to fulfill a predetermined quota from each subset.

These are just a few techniques, and choosing the right one depends on your research question, discipline, resource availability, and the level of accuracy required. In quantitative research, there isn't a one-size-fits-all sampling technique; choosing a method that aligns with your research goals and population is critical. However, a well-planned strategy is essential to avoid wasting resources and time, as highlighted in our video featuring William Hudson, CEO of Syntagm.

He emphasizes the importance of recruiting participants meticulously, ensuring their engagement and the quality of their responses. Accurate and thoughtful participant responses are crucial for obtaining reliable results. William also sheds light on dealing with failing participants and scrutinizing response quality to refine the outcomes.

The 4 types of quantitative research are Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research. Descriptive research aims to depict ‘what exists’ clearly and precisely. Correlational research examines relationships between variables. Causal-comparative research investigates the cause-effect relationship between variables. Experimental research explores causal relationships by manipulating independent variables. To gain deeper insights into quantitative research methods in UX, consider enrolling in our Data-Driven Design: Quantitative Research for UX course.

The strength of quantitative research is its ability to provide precise numerical data for analyzing target variables.This allows for generalized conclusions and predictions about future occurrences, proving invaluable in various fields, including user experience. William Hudson, CEO of Syntagm, discusses the role of surveys, analytics, and testing in providing objective insights in our video on quantitative research methods, highlighting the significance of structured methodologies in eliciting reliable results.

To master quantitative research methods, enroll in our comprehensive course, Data-Driven Design: Quantitative Research for UX . 

This course empowers you to leverage quantitative data to make informed design decisions, providing a deep dive into methods like surveys and analytics. Whether you’re a novice or a seasoned professional, this course at Interaction Design Foundation offers valuable insights and practical knowledge, ensuring you acquire the skills necessary to excel in user experience research. Explore our diverse topics to elevate your understanding of quantitative research methods.

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What is the primary goal of quantitative research in design?

• To analyze numerical data and identify patterns
• To explore abstract design concepts for implementation
• To understand people's subjective experiences and opinions

Which of the following methods is an example of quantitative research?

• Conduct a focus groups to collect detailed user feedback
• Participate in open-ended interviews to explore user experiences
• Run usability tests and measure task completion times

What is one key advantage of quantitative research?

• It allows participants to express their opinions in a flexible manner.
• It provides researchers with detailed narratives of user experiences and perspectives.
• It produces standardized, comparable data that researchers can statistically analyze.

What is a significant challenge of quantitative research?

• It lacks objectivity which makes its results difficult to reproduce.
• It may oversimplify complex user behaviors into numbers and miss contextual insights.
• It often results in biased or misleading conclusions.

How can designers effectively combine qualitative and quantitative research?

• They can collect quantitative data first, followed by qualitative insights to explain the findings.
• They can completely replace quantitative methods with qualitative approaches.
• They can treat them as interchangeable methods to gather similar data.

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Literature on Quantitative Research

Here’s the entire UX literature on Quantitative Research by the Interaction Design Foundation, collated in one place:

Learn more about Quantitative Research

Take a deep dive into Quantitative Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

All open-source articles on Quantitative Research

Best practices for qualitative user research.

• 3 years ago

Card Sorting

Understand the User’s Perspective through Research for Mobile UX

• 11 mths ago

7 Simple Ways to Get Better Results From Ethnographic Research

Question Everything

Tree Testing

Adding Quality to Your Design Research with an SSQS Checklist

• 8 years ago

How to Fit Quantitative Research into the Project Lifecycle

Why and When to Use Surveys

Correlation in User Experience

First-Click Testing

Rating Scales in UX Research: The Ultimate Guide

What to Test

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Quantitative Methods

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• Patricia Luna Gutierrez 5 &
• John C. Oldroyd 6  

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Quantitative analysis ; Quantitative research methods ; Study design

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

Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

Independent Researcher, Masatepe, Nicaragua

Patricia Luna Gutierrez

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

John C. Oldroyd

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

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

• Quantitative Research
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About Research Methods

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

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

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

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

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

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

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

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

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

Library GIS Services

Other Data Services at Berkeley

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

General Research Methods Resources

Here are some general resources for assistance:

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

Consultants

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

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

What is quantitative research?

Last updated

20 February 2023

Reviewed by

Quantitative methods and data are used by some business owners, for example, to evaluate their business, diagnose issues, and identify opportunities.

Quantitative research is used throughout the natural and social sciences, including economics, sociology, chemistry, biology, psychology, and marketing. 

Researchers use quantitative research to get objective, robust, and representative answers from individuals. Researchers gather quantitative data from sample groups of people and generalize it to a larger population. This is to, in some instances, explain a given phenomenon and answer questions about the population, such as product preferences, political persuasion, or demography.

For example, a hotel owner in the US can conduct quantitative research, perhaps via a questionnaire, on a small sample of their customers to understand their opinions about their products and services. The analyzed quantitative data from this questionnaire can be generalized to the larger population of their customers. The hotel can use these opinions to maintain or improve its service provision.

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• Quantitative research methods

Researchers employ various quantitative research methods to determine certain phenomena.

Observation

This method involves gathering information by simply observing behaviors or counting subjects relevant to a study. For example, a researcher could sit in a classroom and observe students when a teacher is teaching, recording those who are and are not paying attention.

Survey is one of the most popular and well-known quantitative methods. It involves asking individuals questions either physically or, most typically nowadays, online. These questions are usually in the form of a questionnaire that individuals can respond to, using a mix of single, multichoice, ranking, rating, and occasionally open-ended questions .

For example, a researcher could administer a questionnaire to first-year international college students about their college experiences using various question formats.

Experimental

This scientific approach is conducted with two sets of data, i.e., independent and dependent variables . Usually, researchers approach experimental studies with specific hypotheses to test. They may use two groups of participants: one who would receive the “treatment” and one who would not.

For example, a researcher might wish to test a short-term mindfulness treatment for individuals with depression. In this case, the independent or manipulated variable would be the mindfulness treatment group. One group would receive the mindfulness treatment, and another would not. In this case, the “experiment” would be to see if the individuals who received the mindfulness treatment experienced fewer depressive symptoms than those who did not.

• What is quantitative analysis?

Quantitative analysis is a process that involves manipulating and evaluating collected, measurable data. The goal is to understand the behavior of a given phenomenon and answer a research question (and, in a scientific setting, prove or disprove a hypothesis).

A business owner, for example, may analyze quantitative sales data and consumer quantitative data using a questionnaire. By doing this, the owner can figure out if their business is doing well or if they need to make changes to improve.

If you are a business owner, you could consider quantitative analysis to better understand your business's past, present, and potential future.

• What do quantitative analysts do?

A quantitative analyst is an expert in designing, developing, and implementing algorithms to answer research questions. They use quantitative research methods to help companies make appropriate business and financial decisions.

The primary responsibility of a quantitative analyst is to apply quantitative methods to identify opportunities and evaluate risks.

Quantitative analysts are important to staff in any business because:

They manage portfolio risks

They test a new trading strategy

They program and implement a new trading strategy

They improve signals used to evaluate trade ideas

• Understanding quantitative analysis

Analysts use quantitative analysis to analyze a business's past, present, and future. You can also use quantitative analysis to determine the progress of your business.

State governments also use quantitative analysis to make monetary and other economic policy decisions. It is used in the financial services industry to analyze investment opportunities. For example, a business owner can use quantitative analysis to determine when to sell or purchase securities based on macroeconomic conditions.

Quantitative analysis versus qualitative analysis

If you are pursuing a career in research or business analysis, it is essential to understand the two concepts—quantitative and qualitative analysis.

Quantitative analysis, at a very basic level, relies on using numbers and discrete values collected from the research. In contrast, qualitative analysis relies on content (e.g., language or text data) that either can’t be expressed in numbers or doesn’t have sufficient scale to be counted or coded.

A business owner wanting to better understand their business might use a representative quantitative sample of customers to generate insight by completing a questionnaire. A website owner could analyze quantitative metrics associated with their website to understand which aspects of the site are working well and which elements need to be optimized. These include the length of visit, number of links clicked, and areas of the site visited.

Various measures could be correlated by sales (or other outcomes) to determine the UX and marketing strategy linked to the site.

Businesses might use qualitative analysis to get a greater depth of understanding or look at the ‘why’ behind the ‘what.’ For example, they might ask customers, who gave a low quantitative score for a provided product, why they gave that rating and how they might improve the said product.

• Advantages of quantitative research

Quantitative research, done right, can help drive a business's success and generate a general understanding of key business metrics and customer behavior, wants, and needs. Quantitative research should be considered for the following reasons: 

It is efficient and fast

An experienced quantitative researcher can complete the reporting and analysis phase efficiently and quickly with a defined reporting structure and outputs while taking some time to define and structure questions (versus unstructured qualitative data ).

It is objective and requires limited interpretation

Quantitative research relies on standardized statistical processes and rules to answer research questions. If performed correctly, data generated from small sample groups can be extrapolated to represent the views of larger populations.

It is focused

Owing to its structure, the goals of quantitative research are determined at the beginning of the study, forcing researchers to clearly understand and define the objectives of their studies.

• Disadvantages of quantitative research

It’s only appropriate in certain cases

This method is only relevant when data can be captured and reflected in numbers. It cannot be used in situations where data is non-numerical, e.g., long-form verbal or textual responses that are not easily coded down into numerical responses.

It’s challenging to analyze the data collected

When quantitative research is collected, it can be difficult to make sense of the numbers without knowing statistical methods. Knowledge of research methods and data analytic techniques is essential for drawing conclusions about the study questions. These programs and methods take time to learn and can be time-consuming and complicated.

• What are the limitations of quantitative research?

Requires vast resources

This method requires a considerable investment of time, energy, and finance. One needs to prepare and structure questions, test their understanding and relevance, and determine how to distribute them to the respondents. Some respondents may expect payment or incentives to respond to the questions (this may be in the form of entry into a prize draw.)

Requires many respondents

Quantitative research generally requires access to (relative to other methods) large samples to ensure inferences made from the research are robust and reliable. Finding this audience, especially where the incidence is low can be both time-consuming and expensive.

Research is limited in its scope

What quantitative research can explore is limited due to the need to agree on the specific questions to be asked and analyzed versus qualitative research. The latter doesn’t define specific numbers and forms of questions in advance.

Why is it called quantitative research?

It is called quantitative research because it involves the use of ‘quantities’ of things—things that can be expressed in numbers or measured.

What does quantitative research answer?

Quantitative research answers questions measuring value or size, which can be expressed in numbers. It answers questions such as how many, how much, and how often.

For example, you can study the number of individuals who wish to study at American universities and their traits. Questions can include how many come from low, medium, or high socio-economic brackets, how many want to study law versus humanities, and what proportion feel excited versus anxious about the prospect of undertaking higher education.

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Key Concepts in Quantitative Research

In this module, we are going to explore the nuances of quantitative research, including the main types of quantitative research, more exploration into variables (including confounding and extraneous variables), and causation.

Content includes:

• Flaws, “Proof”, and Rigor
• The Steps of Quantitative Methodology
• Major Classes of Quantitative Research
• Experimental versus Non-Experimental Research
• Types of Experimental Research
• Types of Non-Experimental Research
• Research Variables
• Confounding/Extraneous Variables
• Causation versus correlation/association

Objectives:

• Discuss the flaws, proof, and rigor in research.
• Describe the differences between independent variables and dependent variables.
• Describe the steps in quantitative research methodology.
• Describe experimental, quasi-experimental, and non-experimental research studies
• Describe confounding and extraneous variables.
• Differentiate cause-and-effect (causality) versus association/correlation

Flaws, Proof, and Rigor in Research

One of the biggest hurdles that students and seasoned researchers alike struggle to grasp, is that research cannot “ prove ” nor “ disprove ”. Research can only support a hypothesis with reasonable, statistically significant evidence.

Indeed. You’ve heard it incorrectly your entire life. You will hear professors, scientists, radio ads, podcasts, and even researchers comment something to the effect of, “It has been proven that…” or “Research proves that…” or “Finally! There is proof that…”

We have been duped. Consider the “ prove ” word a very bad word in this course. The forbidden “P” word. Do not say it, write it, allude to it, or repeat it. And, for the love of avocados and all things fluffy, do not include the “P” word on your EBP poster. You will be deducted some major points.

We can only conclude with reasonable certainty through statistical analyses that there is a high probability that something did not happen by chance but instead happened due to the intervention that the researcher tested. Got that? We will come back to that concept but for now know that it is called “statistical significance”.

All research has flaws. We might not know what those flaws are, but we will be learning about confounding and extraneous variables later on in this module to help explain how flaws can happen.

Remember this: Sometimes, the researcher might not even know that there was a flaw that occurred. No research project is perfect. There is no 100% awesome. This is a major reason why it is so important to be able to duplicate a research project and obtain similar results. The more we can duplicate research with the same exact methodology and protocols, the more certainty we have in the results and we can start accounting for flaws that may have sneaked in.

Finally, not all research is equal. Some research is done very sloppily, and other research has a very high standard of rigor. How do we know which is which when reading an article? Well, within this module, we will start learning about some things to look for in a published research article to help determine rigor. We do not want lazy research to determine our actions as nurses, right? We want the strongest, most reliable, most valid, most rigorous research evidence possible so that we can take those results and embed them into patient care. Who wants shoddy evidence determining the actions we take with your grandmother’s heart surgery?

Independent Variables and Dependent Variables

As we were already introduced to, there are measures called “variables” in research. This will be a bit of a review but it is important to bring up again, as it is a hallmark of quantitative research. In quantitative studies, the concepts being measured are called variables (AKA: something that varies). Variables are something that can change – either by manipulation or from something causing a change. In the article snapshots that we have looked at, researchers are trying to find causes for phenomena. Does a nursing intervention cause an improvement in patient outcomes? Does the cholesterol medication cause a decrease in cholesterol level? Does smoking cause  cancer?

The presumed cause is called the independent variable. The presumed effect is called the dependent variable. The dependent variable is “dependent” on something causing it to change. The dependent variable is the outcome that a researcher is trying to understand, explain, or predict.

Think back to our PICO questions. You can think of the intervention (I) as the independent variable and the outcome (O) as the dependent variable.

The independent variable is manipulated by the researcher or can be variants of influence. Whereas the dependent variable is never manipulated.

Variables do not always measure cause-and-effect. They can also measure a direction of influence.

Here is an example of that: If we compared levels of depression among men and women diagnosed with pancreatic cancer and found men to be more depressed, we cannot conclude that depression was caused by gender. However, we can note that the direction of influence   clearly runs from gender to depression. It makes no sense to suggest the depression influenced their gender.

In the above example, what is the independent variable (IV) and what is the dependent variable (DV)? If you guessed gender as the IV and depression as the DV, you are correct! Important to note in this case that the researcher did not manipulate the IV, but the IV is manipulated on its own (male or female).

Researchers do not always have just one IV. In some cases, more than one IV may be measured. Take, for instance, a study that wants to measure the factors that influence one’s study habits. Independent variables of gender, sleep habits, and hours of work may be considered. Likewise, multiple DVs can be measured. For example, perhaps we want to measure weight and abdominal girth on a plant-based diet (IV).

Now, some studies do not have an intervention. We will come back to that when we talk about non-experimental research.

The point of variables is so that researchers have a very specific measurement that they seek to study.

Let’s look at a couple of examples:

Now you try! Identify the IVs and DVs:

IV and DV Case Studies (Leibold, 2020)

Case Three:   Independent variable: Healthy Lifestyle education with a focus on physical activity; Dependent variable: Physical activity rate before and after education intervention, Heart rate before and after education intervention, Blood pressures before and after education intervention.

Case Four:   Independent variable: Playing classical music; Dependent variable:  Grade point averages post classical music, compared to pre-classical music.

Case Five: Independent variable: No independent variable as there is no intervention.  Dependent variable: The themes that emerge from the qualitative data.

The Steps in Quantitative Research Methodology

Now, as we learned in the last module, quantitative research is completely objective. There is no subjectivity to it. Why is this? Well, as we have learned, the purpose of quantitative research is to make an inference about the results in order to generalize these results to the population.

In quantitative studies, there is a very systematic approach that moves from the beginning point of the study (writing a research question) to the end point (obtaining an answer). This is a very linear and purposeful flow across the study, and all quantitative research should follow the same sequence.

• Identifying a problem and formulating a research question . Quantitative research begins with a theory . As in, “something is wrong and we want to fix it or improve it”.  Think back to when we discussed research problems and formulating a research question. Here we are! That is the first step in formulating a quantitative research plan.
• Formulate a hypothesis . This step is key. Researchers need to know exactly what they are testing so that testing the hypothesis can be achieved through specific statistical analyses.
• A thorough literature review .  At this step, researchers strive to understand what is already known about a topic and what evidence already exists.
• Identifying a framework .  When an appropriate framework is identified, the findings of a study may have broader significance and utility (Polit & Beck, 2021).
• Choosing a study design . The research design will determine exactly how the researcher will obtain the answers to the research question(s). The entire design needs to be structured and controlled, with the overarching goal of minimizing bias and errors. The design determines what data will be collected and how, how often data will be collected, what types of comparisons will be made. You can think of the study design as the architectural backbone of the entire study.
• Sampling . The researcher needs to determine a subset of the population that is to be studied. We will come back to the sampling concept in the next module. However, the goal of sampling is to choose a subset of the population that adequate reflects the population of interest.
• I nstruments to be used to collect data (with reliability and validity as a priority). Researchers must find a way to measure the research variables (intervention and outcome) accurately. The task of measuring is complex and challenging, as data needs to be collected reliably (measuring consistently each time) and valid. Reliability and validity are both about how well a method measures something. The next module will cover this in detail.
• Obtaining approval for ethical/legal human rights procedures . As we will learn in an upcoming module, there needs to be methods in place to safeguard human rights.
• Data collection . The fun part! Finally, after everything has been organized and planned, the researcher(s) begin to collect data. The pre-established plan (methodology) determines when data collection begins, how to accomplish it, how data collection staff will be trained, and how data will be recorded.
• Data analysis . Here comes the statistical analyses. The next module will dive into this.
• Discussion . After all the analyses have been complete, the researcher then needs to interpret the results and examine the implications. Researchers attempt to explain the findings in light of the theoretical framework, prior evidence, theory, clinical experience, and any limitations in the study now that it has been completed. Often, the researcher discusses not just the statistical significance, but also the clinical significance, as it is common to have one without the other.
• Summary/references . Part of the final steps of any research project is to disseminate (AKA: share) the findings. This may be in a published article, conference, poster session, etc. The point of this step is to communicate to others the information found through the study.  All references are collected so that the researchers can give credit to others.
• Budget and funding . As a last mention in the overall steps, budget and funding for research is a consideration. Research can be expensive. Often, researchers can obtain a grant or other funding to help offset the costs.

Edit: Steps in Quantitative Research video. Step 12 should say “Dissemination” (sharing the results).

Experimental, Quasi-Experimental, and Non-Experimental Studies

To start this section, please watch this wonderful video by Jenny Barrow, MSN, RN, CNE, that explains experimental versus nonexperimental research.

(Jenny Barrow, 2019)

Now that you have that overview, continue reading this module.

Experimental Research : In experimental research, the researcher is seeking to draw a conclusion between an independent variable and a dependent variable. This design attempts to establish cause-effect relationships among the variables. You could think of experimental research as experimenting with “something” to see if it caused “something else”.

A true experiment is called a Randomized Controlled Trial (or RCT). An RCT is at the top of the echelon as far as quantitative experimental research. It’s the gold standard of scientific research. An RCT, a true experimental design, must have 3 features:

• An intervention : The experiment does something to the participants by the option of manipulating the independent variable.
• Control : Some participants in the study receive either the standard care, or no intervention at all. This is also called the counterfactual – meaning, it shows what would happen if no intervention was introduced.
• Randomization : Randomization happens when the researcher makes sure that it is completely random who receives the intervention and who receives the control. The purpose is to make the groups equal regarding all other factors except receipt of the intervention.

Note: There is a lot of confusion with students (and even some researchers!) when they refer to “ random assignment ” versus “ random sampling ”. Random assignment  is a signature of a true experiment. This means that if participants are not truly randomly assigned to intervention groups, then it is not a true experiment. We will talk more about random sampling in the next module.

One very common method for RCT’s is called a pretest-posttest design .  This is when the researcher measures the outcome before and after the intervention. For example, if the researcher had an IV (intervention/treatment) of a pain medication, the DV (pain) would be measured before the intervention is given and after it is given. The control group may just receive a placebo. This design permits the researcher to see if the change in pain was caused by the pain medication because only some people received it (Polit & Beck, 2021).

Another experimental design is called a crossover design . This type of design involves exposing participants to more than one treatment. For example, subject 1 first receives treatment A, then treatment B, then treatment C. Subject 2 might first receive treatment B, then treatment A, and then treatment C. In this type of study, the three conditions for an experiment are met: Intervention, randomization, and control – with the subjects serving as their own control group.

Control group conditions can be done in 4 ways:

• No intervention is used; control group gets no treatment at all
• “Usual care” or standard of care or normal procedures used
• An alternative intervention is uses (e.g. auditory versus visual stimulation)
• A placebo or pseudo-intervention, presumed to have no therapeutic value, is used

Quasi-Experimental Research : Quasi-experiments involve an experiment just like true experimental research. However, they lack randomization and some even lack a control group.  Therefore, there is implementation and testing of an intervention, but there is an absence of randomization.

For example, perhaps we wanted to measure the effect of yoga for nursing students. The IV (intervention of yoga) is being offered to all nursing students and therefore randomization is not possible. For comparison, we could measure quality of life data on nursing students at a different university. Data is collected from both groups at baseline and then again after the yoga classes. Note, that in quasi-experiments, the phrase “comparison group” is sometimes used instead of “control group” against which outcome measures are collected.

Sometimes there is no comparison group either. This would be called a one-group pretest-posttest design .

Non-Experimental Research : Sometimes, cause-problem research questions cannot be answered with an experimental or quasi-experimental design because the IV cannot be manipulated. For example, if we want to measure what impact prerequisite grades have on student success in nursing programs, we obviously cannot manipulate the prerequisite grades. In another example, if we wanted to investigate how low birth weight impacts developmental progression in children, we cannot manipulate the birth weight. Often, you will see the word “observational” in lieu of non-experimental researcher. This does not mean the researcher is just standing and watching people, but instead it refers to the method of observing data that has already been established without manipulation.

There are various types of non-experimental research:

Correlational research : A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. In the example of prerequisites and nursing program success, that is a correlational design. Consider hypothetically, a researcher is studying a correlation between cancer and marriage. In this study, there are two variables: disease and marriage. Let us say marriage has a negative association with cancer. This means that married people are less likely to develop cancer.

Cohort design (also called a prospective design) : In a cohort study, the participants do not have the outcome of interest to begin with. They are selected based on the exposure status of the individual. They are then followed over time to evaluate for the occurrence of the outcome of interest. Cohorts may be divided into exposure categories once baseline measurements of a defined population are made. For example, the Framingham Cardiovascular Disease Study (CVD) used baseline measurements to divide the population into categories of CVD risk factors. Another example:  An example of a cohort study is comparing the test scores of one group of people who underwent extensive tutoring and a special curriculum and those who did not receive any extra help. The group could be studied for years to assess whether their scores improve over time and at what rate.

Retrospective design : In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. For example, a researcher might examine the medical histories of 1000 elderly women to identify the causes of health problems.

Case-control design : A study that compares two groups of people: those with the disease or condition under study (cases) and a very similar group of people who do not have the condition. For example, investigators conducted a case-control study to determine if there is an association between colon cancer and a high fat diet. Cases were all confirmed colon cancer cases in North Carolina in 2010. Controls were a sample of North Carolina residents without colon cancer.

Descriptive research : Descriptive research design is a type of research design that aims to obtain information to systematically describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem, rather than the why. For example, the researcher might wish to discover the percentage of motorists who tailgate – the prevalence  of a certain behavior.

There are two other designs to mention, which are both on a time continuum basis.

Cross-sectional design : All data are collected at a single point in time. Retrospective studies are usually cross-sectional. The IV usually concerns events or behaviors occurring in the past. One cross-sectional study example in medicine is a data collection of smoking habits and lung cancer incidence in a given population. A cross-sectional study like this cannot solely determine that smoking habits cause lung cancer, but it can suggest a relationship that merits further investigation. Cross-sectional studies serve many purposes, and the cross-sectional design is the most relevant design when assessing the prevalence of disease, attitudes and knowledge among patients and health personnel, in validation studies comparing, for example, different measurement instruments, and in reliability studies.

Longitudinal design : Data are collected two or more times over an extended period. Longitudinal designs are better at showing patterns of change and at clarifying whether a cause occurred before an effect (outcome). A challenge in longitudinal studies is attrition or the loss of participants over time. In a longitudinal study subjects are followed over time with continuous or repeated monitoring of risk factors or health outcomes, or both. Such investigations vary enormously in their size and complexity. At one extreme a large population may be studied over decades. An example of a longitudinal design is a multiyear comparative study of the same children in an urban and a suburban school to record their cognitive development in depth.

Confounding and Extraneous Variables

Confounding variables  are a type of extraneous variable that occur which interfere with or influence the relationship between the independent and dependent variables. In research that investigates a potential cause-and-effect relationship, a confounding variable is an unmeasured third variable that influences both the supposed cause and the supposed effect.

It’s important to consider potential confounding variables and account for them in research designs to ensure results are valid. You can imagine that if something sneaks in to influence the measured variables, it can really muck up the study!

Here is an example:

You collect data on sunburns and ice cream consumption. You find that higher ice cream consumption is associated with a higher probability of sunburn. Does that mean ice cream consumption causes sunburn?

Here, the confounding variable is temperature: hot temperatures cause people to both eat more ice cream and spend more time outdoors under the sun, resulting in more sunburns.

To ensure the internal validity of research, the researcher must account for confounding variables. If he/she fails to do so, the results may not reflect the actual relationship between the variables that they are interested in.

For instance, they may find a cause-and-effect relationship that does not actually exist, because the effect they measure is caused by the confounding variable (and not by the independent variable).

Here is another example:

The researcher finds that babies born to mothers who smoked during their pregnancies weigh significantly less than those born to non-smoking mothers. However, if the researcher does not account for the fact that smokers are more likely to engage in other unhealthy behaviors, such as drinking or eating less healthy foods, then he/she might overestimate the relationship between smoking and low birth weight.

Extraneous variables are any variables that the researcher is not investigating that can potentially affect the outcomes of the research study. If left uncontrolled, extraneous variables can lead to inaccurate conclusions about the relationship between IVs and DVs.

Extraneous variables can threaten the internal validity of a study by providing alternative explanations for the results. In an experiment, the researcher manipulates an independent variable to study its effects on a dependent variable.

In a study on mental performance, the researcher tests whether wearing a white lab coat, the independent variable (IV), improves scientific reasoning, the dependent variable (DV).

Students from a university are recruited to participate in the study. The researcher manipulates the independent variable by splitting participants into two groups:

• Participants in the experimental   group are asked to wear a lab coat during the study.
• Participants in the control group are asked to wear a casual coat during the study.

All participants are given a scientific knowledge quiz, and their scores are compared between groups.

When extraneous variables are uncontrolled, it’s hard to determine the exact effects of the independent variable on the dependent variable, because the effects of extraneous variables may mask them.

Uncontrolled extraneous variables can also make it seem as though there is a true effect of the independent variable in an experiment when there’s actually none.

In the above experiment example, these extraneous variables can affect the science knowledge scores:

• Participant’s major (e.g., STEM or humanities)
• Participant’s interest in science
• Demographic variables such as gender or educational background
• Time of day of testing
• Experiment environment or setting

If these variables systematically differ between the groups, you can’t be sure whether your results come from your independent variable manipulation or from the extraneous variables.

In summary, an extraneous variable is anything that could influence the dependent variable. A confounding variable influences the dependent variable, and also correlates with or causally affects the independent variable.

Cause-and-Effect (Causality) Versus Association/Correlation  

A very important concept to understand is cause-and-effect, also known as causality, versus correlation. Let’s look at these two concepts in very simplified statements. Causation means that one thing caused  another thing to happen. Correlation means there is some association between the two thing we are measuring.

It would be nice if it were as simple as that. These two concepts can indeed by confused by many. Let’s dive deeper.

Two or more variables are considered to be related or associated, in a statistical context, if their values change so that as the value of one variable increases or decreases so does the value of the other variable (or the opposite direction).

For example, for the two variables of “hours worked” and “income earned”, there is a relationship between the two if the increase in hours is associated with an increase in income earned.

However, correlation is a statistical measure that describes the size and direction of a relationship between two or more variables. A correlation does not automatically mean that the change in one variable caused the change in value in the other variable.

Theoretically, the difference between the two types of relationships is easy to identify — an action or occurrence can cause another (e.g. smoking causes an increase in the risk of developing lung cancer), or it can correlate with another (e.g. smoking is correlated with alcoholism, but it does not cause alcoholism). In practice, however, it remains difficult to clearly establish cause and effect, compared with establishing correlation.

Simplified in this image, we can say that hot and sunny weather causes an increase in ice cream consumption. Similarly, we can demise that hot and sunny weather increases the incidence of sunburns. However, we cannot say that ice cream caused a sunburn (or that a sunburn increases consumption of ice cream). It is purely coincidental. In this example, it is pretty easy to anecdotally surmise correlation versus causation. However, in research, we have statistical tests that help researchers differentiate via specialized analyses.

Here is a great Khan Academy video of about 5 minutes that shows a worked example of correlation versus causation with regard to sledding accidents and frostbite cases:

https://www.khanacademy.org/test-prep/praxis-math/praxis-math-lessons/gtp–praxis-math–lessons–statistics-and-probability/v/gtp–praxis-math–video–correlation-and-causation

References & Attribution

“ Light bulb doodle ” by rawpixel licensed CC0 .

“ Magnifying glass ” by rawpixel licensed CC0

“ Orange flame ” by rawpixel licensed CC0 .

Jenny Barrow. (2019). Experimental versus nonexperimental research. https://www.youtube.com/watch?v=FJo8xyXHAlE

Leibold, N. (2020). Research variables. Measures and Concepts Commonly Encountered in EBP. Creative Commons License: BY NC

Polit, D. & Beck, C. (2021).  Lippincott CoursePoint Enhanced for Polit’s Essentials of Nursing Research  (10th ed.). Wolters Kluwer Health.

Evidence-Based Practice & Research Methodologies Copyright © by Tracy Fawns is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Quantitative Research: What It Is, Practices & Methods

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

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

What is Quantitative Research?

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

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

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

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

Quantitative Research Characteristics

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

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

Quantitative Research Methods

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

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

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

A. Techniques and Types of Studies

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

01. Survey Research

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

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

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

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

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

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

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

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

02. Correlational Research

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

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

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

Example of Correlational Research Questions :

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

03. Causal-comparative Research

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

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

Example of Causal-Comparative Research Questions:

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

04. Experimental Research

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

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

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

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

B. Data Collection Methodologies

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

01. Data Collection Methodologies: Sampling Methods

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

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

There are four main types of probability sampling:

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

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

There are five non-probability sampling models:

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

02. Data collection methodologies: Using surveys & polls

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

Using surveys for primary quantitative research

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

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

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

Use of different question types

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

Survey Distribution and Survey Data Collection

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

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

Survey example

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

Using polls for primary quantitative research

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

C. Data Analysis Techniques

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

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

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

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

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

Secondary Quantitative Research Methods

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

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

The following are five popularly used secondary quantitative research methods:

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

Quantitative Research Examples

Some examples of quantitative research are:

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

What are the Advantages of Quantitative Research?

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

Collect Reliable and Accurate Data:

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

Quick Data Collection:

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

Wider Scope of Data Analysis:

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

Eliminate Bias:

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

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

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

Quantitative Research vs Qualitative Research

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

Quantitative Research

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

Qualitative Research

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

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

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

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

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

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

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

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

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

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

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Quantitative research

Affiliation.

• 1 Faculty of Health and Social Care, University of Hull, Hull, England.
• PMID: 25828021
• DOI: 10.7748/ns.29.31.44.e8681

This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys – the principal research designs in quantitative research – are described and key features explained. The importance of the double-blind randomised controlled trial is emphasised, alongside the importance of longitudinal surveys, as opposed to cross-sectional surveys. Essential features of data storage are covered, with an emphasis on safe, anonymous storage. Finally, the article explores the analysis of quantitative data, considering what may be analysed and the main uses of statistics in analysis.

Keywords: Experiments; measurement; nursing research; quantitative research; reliability; surveys; validity.

• Biomedical Research / methods*
• Double-Blind Method
• Evaluation Studies as Topic
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• Randomized Controlled Trials as Topic
• United Kingdom

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Your ultimate guide to quantitative research.

10 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analysed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or  demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in  primary research . Qualitative research can also  come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as psychology, economics, sociology, and marketing.

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will  aim to use a sample size that is representative  of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

Learn how Qualtrics can enhance & simplify the quantitative research process

Qualitative vs quantitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Qualitative research focuses on the thoughts, feelings, and values of a participant, to understand why people act in the way they do. They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender, social class, political alignment, likely favoured products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behaviour and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

A survey is a simple-to-create and easy-to-distribute research method, which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

Face-to-face or phone interviews

Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions. The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

Polls can be a shorter version of surveys, used to get a ‘flavour’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Per cent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented.

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead,  data analysis will suggest a conclusion, which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarise our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so  bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons:  Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations:  Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying  how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness  and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

Learn how Qualtrics can enhance & simplify the quantitative research process

Related resources

Market intelligence 9 min read, qualitative research questions 11 min read, ethnographic research 11 min read, business research methods 12 min read, qualitative research design 12 min read, business research 10 min read, qualitative research interviews 11 min read, request demo.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

What is the difference between quantitative and qualitative?

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

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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Ever had a tough time with quantitative research? You're not alone! 

Quantitative research is the process of collecting and analyzing numerical data to understand and study various phenomena using statistical methods. Many find this tedious process tricky. 

But don't worry! 

Our complete guide is here to guide you through the important steps and tricks to handle this challenge with confidence. We've even added some examples to make it easier. 

So, let's dive in and learn together!

• 1. Quantitative Research Definition - What is Quantitative Research?
• 2. Data Collection in Quantitative Research
• 3. Data Analysis in Quantitative Research
• 4. Types of Quantitative Research Methods for Students and Researchers
• 5. Types of Data Collection Methodologies in Quantitative Research
• 6. Quantitative vs. Qualitative Research
• 7. Advantages and Strengths of Quantitative Research
• 8. Disadvantages and Weaknesses of Quantitative Research

Quantitative Research Definition - What is Quantitative Research?

Quantitative research involves gathering and studying numerical data. Its applications include identifying trends, making forecasts, testing cause-and-effect links, and drawing broader conclusions applicable to larger groups.

In this method, researchers employ tools such as surveys, experiments, and observations to gather data. Whereas in qualitative research, you deal with non-numeric data, such as text, video, or audio.

Quantitative research is extensively applied in natural and social sciences, including biology, chemistry, psychology, economics, sociology, and marketing, among others.

Characteristics of Quantitative Research

Here are some distinct quantitative research characteristics:

• Large Sample Sizes: Quantitative studies often involve larger sample sizes, allowing for more robust statistical analyses and generalizability of findings.
• Statistical Analysis: Statistical techniques and tools are extensively used to analyze data, unveiling patterns, relationships, and significance.
• Objective and Replicable: Quantitative research aims for objectivity and replicability. Other researchers should be able to conduct the same study and obtain similar results.
• Closed-Ended Questions: Surveys and questionnaires typically use closed-ended questions with predefined response options, making data analysis more straightforward.
• Quantifiable Variables: Researchers identify and measure variables that can be quantified, such as age, income, or test scores, for precise analysis.
• Hypothesis Testing: It often involves testing hypotheses and making inferences about populations based on sample data.
• Cross-Sectional or Longitudinal: Studies can be cross-sectional (data collected at a single point in time) or longitudinal (data collected over an extended period).
• Generalizability: Quantitative research seeks to generalize findings from a sample to a larger population, provided the sample is representative.

These characteristics make quantitative research different from qualitative research.

Data Collection in Quantitative Research

Data collection is the systematic process of gathering information for research purposes. It is a critical starting point, ensuring that the information gathered is relevant, accurate, and comprehensive.

• Structured Instruments - Quantitative research typically employs structured instruments like surveys and questionnaires. These tools ensure consistency in data gathering by posing the same set of questions to each participant.
• Sampling Methods - Researchers use various sampling techniques, such as random sampling, stratified sampling, or convenience sampling, to select a representative group from the target population.
• Objective Observation - Data collection often involves objective observations of phenomena. This may include recording numerical data, such as counting occurrences or measuring attributes.
• Experimental Control - In experimental research, control over variables is essential. Researchers manipulate one or more variables to observe their impact on the outcome, maintaining control over external factors.

Data Analysis in Quantitative Research

Data analysis is the second important aspect of quantitative research. After collecting the data, the data is analyzed with statistical methods. When analyzing, it is important that the results are relevant and related to the objective and aim of the research.

Below are some common statistical analysis methods that are used to analyze the collected data.

• SWOT Analysis - It stands for Strengths, Weaknesses, Opportunities, and Threats. Businesses use this kind of analysis to evaluate their performance and develop appropriate strategies.
• Conjoint Analysis - This kind of analysis helps businesses to identify how customers make difficult purchasing decisions. The businesses involved in direct sales and purchases know this and use the analysis to make the decisions.
• Cross-tabulation - A preliminary statistical analysis helps understand patterns, trends, and relationships between the various factors of the research.
• TURF Analysis - It stands for Totally Unduplicated Reach and Frequency Analysis. It is conducted to collect and analyze the data and responses of a chosen or favored target group.

Afterward, other methods like inferential statistics could be used to gather the results. 

Types of Quantitative Research Methods for Students and Researchers

‘What are the four types of quantitative research?’

Quantitative research has four distinct types, and all four of them are regarded as primary research methods. Primary quantitative research is more common and useful than secondary research methods. 

It is mainly because, in them, the researcher collects the data directly. He does not depend on previous research and collects the data from scratch. 

Below are the four types of quantitative research methods.

Survey Research 

This type of research is conducted through means of online surveys, online polls, and questionnaires. A group of people is chosen for the survey, and the method is used by big and small organizations and companies. They use it to understand their customers better.

Ideally, the survey is done through face-to-face meetings and interviews. Now, it is conducted through various online methodologies. Below are the common types of surveys.

• Cross-Sectional Survey - This research is conducted on a selected group of people at a certain point in time. The researcher evaluates several things. The selected group of people has similarities in all aspects except the ones chosen by the researcher. This kind of research is used for industries like retail, small-scale businesses, and healthcare industries.
• Longitudinal Survey - This research is based on observing a specific group of people for a set duration. The duration could be days, months, or even years. The researcher observes the change in behavior of the selected group of people.

This kind of research is used in the fields of applied sciences, medicine, and marketing.

Correlational Research 

Correlational research is conducted to identify the relationship between two entities. These entities must be closely related and have a significant impact on each other.

This research is conducted to identify, evaluate, and understand the correlation between the variables and how they depend on each other.

The researchers use mathematical and statistical methods to understand this correlation. Some factors that they consider include relationships, trends, and patterns between these variables.

Sometimes, the researchers make changes in one of the variables to notice the effect on the other one.

Causal-comparative Research 

This research is also known as quasi-experimental research. It is based on the cause and effect relationship between the two variables. Here, one of the variables is dependent on the other one, but the other one is independent. The researcher does not change the independent variable.

The research is not limited to statistical analysis only but includes other groups and variables also. The research could be conducted on the variables, no matter the kind of relationship they have. The statistical analysis method is used to acquire the results.

Experimental Research

This kind of research is based on proving or contradicting a theory or statement. It is also known as true experimentation and is usually focused on single or multiple theories.

The respective theory is not proven yet, and the research method is commonly used in natural sciences.

There could be some theories involved in this research. Due to this, it is more common in social sciences.

Types of Data Collection Methodologies in Quantitative Research

After determining the kind of research, finding the right data collection method is the most important step. Data could be collected through both the sampling and surveys and polls method.

Sampling Data Collection Method

In quantitative research, two types of sampling methods are used: probability and non-probability sampling.

1. Probability Sampling 

The data is collected by sifting some individuals from the general population and creating samples. The individuals, data samples are chosen randomly and without any particular selection criteria.

Probability sampling is further divided into the following kinds.

• Simple Random Sampling - This kind of data selection is the simplest one, and the participants are chosen randomly. This kind of sampling is conducted on a large population.
• Stratified Random Sampling - In this sampling, the population is divided into several groups and strata. The participants for the research are chosen randomly from those groups.
• Cluster Sampling - In cluster sampling, the population is divided into several clusters based on geography and demography.
• Systematic Sampling - In this, the samples from the population are chosen at regular intervals. These intervals are predefined, and usually, they are calculated based on the population or size of the target sample.

2. Non-Probability Sampling 

In this kind of data collection, the researcher uses his knowledge and experience to choose the samples. The researcher is involved and has a set of criteria. Due to this, not all individuals have the chance to be selected for the research.

Below are the main types of non-probability sampling frameworks.

• Convenience Sampling - These kinds of samples are probably the easiest to obtain. They are chosen only because they are the easiest ones to obtain. They are usually closer to the researcher, and these samples are easy to work with because there are no rigid parameters.
• Consecutive Sampling - This is similar to convenience sampling, but the researcher could choose a specific group of people for his research. The researcher could repeat the process with other groups of samples.
• Quota Sampling - The researchers select some specific elements based on the researcher’s target personalities and traits. Based on this, different individuals in the groups have equal chances of getting selected.
• Snowball Sampling - This kind of sampling is done on a target audience or a chosen group that is difficult to contact. In this, the chosen group is difficult to put together.
• Judgemental Sampling - This kind of sample is built based on the researcher’s skills, experience, and preferences.

Survey and Polls Data Collection Method

After the sample or group is chosen, the researcher could use polls or surveys to collect the required research data.

In this kind of research, the data is collected from a selected group of people. The data is used to identify new trends and collect information about different things and topics. Through the survey, the researcher could reach a wider population.

Based on the time allocated for the research, it could be used to collect more information and data.

When creating questions and options for the survey, the researchers use four measurement scales or criteria. These four parameters include nominal, interval, ordinal, and ratio measurement scales. Without them, no multiple-choice questions could be created.

The questions used for the survey must be close-ended. These could be a mix of different kinds of questions, and the responses could be analyzed through different rating scales.

After creating the survey, the next thing is to distribute it. Below are some of the commonly used survey distribution methods.

• Email - The most common method of distributing the survey is email management software to dispense the survey to your selected participants.
• Buying the Respondents - This is also a quite famous and widely used survey distribution method. Select the respondent and have him respond to the survey. Since the respondents would be knowledgeable, they will help in maximizing the results.
• Embedding the Survey on a Website - This is a great way of getting more responses and targeted results. Embedding the survey on a website works because the researcher is at the right place and close to the brand.
• Social Distribution - Distributing the survey through a social media platform helps collect more responses from the right audience.
• QR Code - The survey is stored in the QR code, and it is printed in magazines or on business cards.
• SMS Survey - It is the most convenient way of collecting more responses and data.

Like surveys, polls are also used to collect the data. It also has close-ended quantitative research questions, and election and exit polls are commonly used in this survey.

Quantitative vs. Qualitative Research

Quantitative and qualitative research are major kinds of research. They are mainly used in the subjects that follow detailed research patterns. How does it differ from quantitative research? 

Below is a detailed comparison of the two kinds of research.

Want to know more about the differences between these types of research? Check out this extensive read on qualitative vs. quantitative research to get more insights!

Advantages and Strengths of Quantitative Research

Quantitative research offers several advantages to researchers. Some of the main reasons why researchers use this kind of research are discussed below: 

• The Data Can Be Replicated - The research and study could be replicated. The data collection methods and definitions of the concepts are clear and easy to understand.
• The Results Can Be Compared Easily - The same study could be conducted in different cultural settings and sample groups. The results could also be compared statistically.
• Usage of Large Samples - Data and information from large samples could be processed and analyzed using different research procedures.
• Hypothesis Could be Tested - The researcher could use formal hypothesis testing. He could report the data collection, research variables, research predictions, and testing techniques before forecasting and establishing any conclusion.
• The Data Collection is Quick - The data could be collected easily and from a wider population. The usage of statistical methods and conducting and analyzing results is also easy and to the point.
• The Data Analysis is Inclusive - Quantitative data and research offer a wider population for sampling. They could be analyzed through research and analysis procedures.

Due to all of these advantages, researchers prefer using this kind of research method. It is easy to sample, collect, and analyze data and repeat the procedure easily.

Disadvantages and Weaknesses of Quantitative Research

Despite the benefits for the researchers, quantitative research design has some limitations. It may not be suitable for more complex and detailed kinds of topics.

Below are some common quantitative research limitations.

• Superficial - since the research includes limited and precise research samples. In quantitative research, the research is presented in numbers. They could be explained in detail through qualitative data and research.
• Limited Focus - the focus is narrow and limited, and the researcher would have to ignore other relevant and important variables.
• Biased Structure - structural biases could exist and affect sampling methods, data collection, and measurement results.
• Lack of Proper Conditions - sometimes, quantitative research may not include other important factors to collect the data.

Due to these reasons, quantitative research is not an ideal choice for detailed kinds of research. For them, qualitative research works better.

To help you further, we have added some useful examples of quantitative research here.

Quantitative Research Examples

Below are some helpful quantitative research examples to help you understand it better.

Sample Quantitative Research

Quantitative Research Example for Students

Now that you've got the hang of how to do quantitative research and why it's valuable, you're all set to begin your research journey.

The qualitative research method shows the idea and perception of your targeted audience. However, not every student is able to choose the right approach while writing a research paper. It requires a thorough understanding of both qualitative research and quantitative research methods.

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

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

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Characteristics of Quantitative Research

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

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

Its main characteristics are :

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

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

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

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

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

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

Basic Research Design for Quantitative Studies

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

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

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

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

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

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

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

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

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

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

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

Strengths of Using Quantitative Methods

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

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

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

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

Limitations of Using Quantitative Methods

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

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

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

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

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

SAGE Research Methods Online and Cases

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This article has a correction. Please see:

• Correction: How to appraise quantitative research - April 01, 2019

• Xabi Cathala 1 ,
• Calvin Moorley 2
• 1 Institute of Vocational Learning , School of Health and Social Care, London South Bank University , London , UK
• 2 Nursing Research and Diversity in Care , School of Health and Social Care, London South Bank University , London , UK
• Correspondence to Mr Xabi Cathala, Institute of Vocational Learning, School of Health and Social Care, London South Bank University London UK ; cathalax{at}lsbu.ac.uk and Dr Calvin Moorley, Nursing Research and Diversity in Care, School of Health and Social Care, London South Bank University, London SE1 0AA, UK; Moorleyc{at}lsbu.ac.uk

https://doi.org/10.1136/eb-2018-102996

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Introduction

Some nurses feel that they lack the necessary skills to read a research paper and to then decide if they should implement the findings into their practice. This is particularly the case when considering the results of quantitative research, which often contains the results of statistical testing. However, nurses have a professional responsibility to critique research to improve their practice, care and patient safety. 1  This article provides a step by step guide on how to critically appraise a quantitative paper.

Title, keywords and the authors

The authors’ names may not mean much, but knowing the following will be helpful:

Their position, for example, academic, researcher or healthcare practitioner.

Their qualification, both professional, for example, a nurse or physiotherapist and academic (eg, degree, masters, doctorate).

This can indicate how the research has been conducted and the authors’ competence on the subject. Basically, do you want to read a paper on quantum physics written by a plumber?

The abstract is a resume of the article and should contain:

Introduction.

Research question/hypothesis.

Methods including sample design, tests used and the statistical analysis (of course! Remember we love numbers).

Main findings.

Conclusion.

The subheadings in the abstract will vary depending on the journal. An abstract should not usually be more than 300 words but this varies depending on specific journal requirements. If the above information is contained in the abstract, it can give you an idea about whether the study is relevant to your area of practice. However, before deciding if the results of a research paper are relevant to your practice, it is important to review the overall quality of the article. This can only be done by reading and critically appraising the entire article.

The introduction

Example: the effect of paracetamol on levels of pain.

My hypothesis is that A has an effect on B, for example, paracetamol has an effect on levels of pain.

My null hypothesis is that A has no effect on B, for example, paracetamol has no effect on pain.

My study will test the null hypothesis and if the null hypothesis is validated then the hypothesis is false (A has no effect on B). This means paracetamol has no effect on the level of pain. If the null hypothesis is rejected then the hypothesis is true (A has an effect on B). This means that paracetamol has an effect on the level of pain.

Background/literature review

The literature review should include reference to recent and relevant research in the area. It should summarise what is already known about the topic and why the research study is needed and state what the study will contribute to new knowledge. 5 The literature review should be up to date, usually 5–8 years, but it will depend on the topic and sometimes it is acceptable to include older (seminal) studies.

Methodology

In quantitative studies, the data analysis varies between studies depending on the type of design used. For example, descriptive, correlative or experimental studies all vary. A descriptive study will describe the pattern of a topic related to one or more variable. 6 A correlational study examines the link (correlation) between two variables 7  and focuses on how a variable will react to a change of another variable. In experimental studies, the researchers manipulate variables looking at outcomes 8  and the sample is commonly assigned into different groups (known as randomisation) to determine the effect (causal) of a condition (independent variable) on a certain outcome. This is a common method used in clinical trials.

There should be sufficient detail provided in the methods section for you to replicate the study (should you want to). To enable you to do this, the following sections are normally included:

Overview and rationale for the methodology.

Participants or sample.

Data collection tools.

Methods of data analysis.

Ethical issues.

Data collection should be clearly explained and the article should discuss how this process was undertaken. Data collection should be systematic, objective, precise, repeatable, valid and reliable. Any tool (eg, a questionnaire) used for data collection should have been piloted (or pretested and/or adjusted) to ensure the quality, validity and reliability of the tool. 9 The participants (the sample) and any randomisation technique used should be identified. The sample size is central in quantitative research, as the findings should be able to be generalised for the wider population. 10 The data analysis can be done manually or more complex analyses performed using computer software sometimes with advice of a statistician. From this analysis, results like mode, mean, median, p value, CI and so on are always presented in a numerical format.

The author(s) should present the results clearly. These may be presented in graphs, charts or tables alongside some text. You should perform your own critique of the data analysis process; just because a paper has been published, it does not mean it is perfect. Your findings may be different from the author’s. Through critical analysis the reader may find an error in the study process that authors have not seen or highlighted. These errors can change the study result or change a study you thought was strong to weak. To help you critique a quantitative research paper, some guidance on understanding statistical terminology is provided in  table 1 .

• View inline

Some basic guidance for understanding statistics

Quantitative studies examine the relationship between variables, and the p value illustrates this objectively.  11  If the p value is less than 0.05, the null hypothesis is rejected and the hypothesis is accepted and the study will say there is a significant difference. If the p value is more than 0.05, the null hypothesis is accepted then the hypothesis is rejected. The study will say there is no significant difference. As a general rule, a p value of less than 0.05 means, the hypothesis is accepted and if it is more than 0.05 the hypothesis is rejected.

The CI is a number between 0 and 1 or is written as a per cent, demonstrating the level of confidence the reader can have in the result. 12  The CI is calculated by subtracting the p value to 1 (1–p). If there is a p value of 0.05, the CI will be 1–0.05=0.95=95%. A CI over 95% means, we can be confident the result is statistically significant. A CI below 95% means, the result is not statistically significant. The p values and CI highlight the confidence and robustness of a result.

Discussion, recommendations and conclusion

The final section of the paper is where the authors discuss their results and link them to other literature in the area (some of which may have been included in the literature review at the start of the paper). This reminds the reader of what is already known, what the study has found and what new information it adds. The discussion should demonstrate how the authors interpreted their results and how they contribute to new knowledge in the area. Implications for practice and future research should also be highlighted in this section of the paper.

A few other areas you may find helpful are:

Limitations of the study.

Conflicts of interest.

Table 2 provides a useful tool to help you apply the learning in this paper to the critiquing of quantitative research papers.

Quantitative paper appraisal checklist

• 1. ↵ Nursing and Midwifery Council , 2015 . The code: standard of conduct, performance and ethics for nurses and midwives https://www.nmc.org.uk/globalassets/sitedocuments/nmc-publications/nmc-code.pdf ( accessed 21.8.18 ).
• Gerrish K ,
• Moorley C ,
• Tunariu A , et al
• Shorten A ,

Competing interests None declared.

Patient consent Not required.

Provenance and peer review Commissioned; internally peer reviewed.

Correction notice This article has been updated since its original publication to update p values from 0.5 to 0.05 throughout.

Linked Articles

• Miscellaneous Correction: How to appraise quantitative research BMJ Publishing Group Ltd and RCN Publishing Company Ltd Evidence-Based Nursing 2019; 22 62-62 Published Online First: 31 Jan 2019. doi: 10.1136/eb-2018-102996corr1

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Qualitative Research Design

This course is part of Qualitative Research Design and Methods for Public Health Specialization

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What you'll learn

Design a qualitative research project to respond to specific public health problems/questions.

Design strategies and instruments for qualitative data collection linked to study objectives and appropriate for the population of interest.

Address ethical concerns in qualitative research design and implementation.

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There are 6 modules in this course

This course introduces qualitative research, compares and contrasts qualitative and quantitative research approaches, and provides an overview of qualitative methods for data collection. It outlines a step-by-step approach to qualitative research design that begins by identifying a public health topic of interest, works to hone in on a specific research problem, and then specifies research questions, objectives, and specific aims. The course emphasizes the iterative nature of research design in qualitative inquiry and highlights the importance of specifying a population of interest, an appropriate sampling strategy, and potential approaches to recruitment. It introduces the relationship between these considerations and key concepts such as saturation and transferability in qualitative research. Finally, the course considers ethical concerns specific to qualitative research and potential solutions. Learners of this course will not only be able to put what they learn into practice, but they'll also develop a portfolio of qualitative research materials for career advancement.

Introduction to Qualitative Research

In this first week, you'll get the chance to explore characteristics and approaches of both qualitative and quantitative research, understand their differences, and acknowledge how both are complementary.

What's included

6 videos 4 readings 1 quiz 1 peer review

6 videos • Total 20 minutes

• Welcome to the Course! • 2 minutes • Preview module
• What Is Research? • 2 minutes
• What Is Qualitative Research? • 3 minutes
• How Is Qualitative Different From Quantitative? • 4 minutes
• Five Basic Approaches to Qualitative Research • 4 minutes
• Qualitative and Quantitative As Complementary Methods • 3 minutes

4 readings • Total 145 minutes

• Course Outline and Grading Information • 5 minutes
• Introduction to Qualitative Research • 20 minutes
• Qualitative Inquiry • 90 minutes
• Mixed Methods Design • 30 minutes

1 quiz • Total 20 minutes

• Practice • 20 minutes

1 peer review • Total 60 minutes

• Week 1: Comparing Quantitative and Qualitative Studies • 60 minutes

Qualitative Methods

This week, we'll look at two types of data that can be collected and dive into the three main data collection methods used in qualitative studies. Finally, we'll wrap up by discussing the concept of saturation and consider the lingering question, "how much data is enough?"

6 videos 2 readings 1 quiz 1 peer review

6 videos • Total 15 minutes

• A Look at This Week • 2 minutes • Preview module
• Types of Data • 1 minute
• Observation • 4 minutes
• Interviews • 3 minutes
• Focus Group Discussions • 2 minutes
• Saturation: How Much Data to Collect? • 2 minutes

2 readings • Total 135 minutes

• Data Collection • 90 minutes
• Saturation Point • 45 minutes
• Week 2: Exploring Qualitative Methods • 60 minutes

Objective-Driven Design

In our third week, we'll discuss how to develop a problem statement from a topic of interest, craft research questions and aims, and discuss how this process all relates to objective-driven design.

5 videos 2 readings 1 quiz 1 peer review

5 videos • Total 21 minutes

• A Look at This Week • 3 minutes • Preview module
• What Is Objective-Driven Design? • 1 minute
• Uncovering a Problem • 6 minutes
• Developing & Refining a Problem Statement • 3 minutes
• Developing Research Questions & Specific Aims • 6 minutes

2 readings • Total 75 minutes

• Setting Up a Qualitative Project • 30 minutes
• Resources for Developing Your Research Problem & Question • 45 minutes
• Week 3: Identifying Your Research Problem • 60 minutes

Methods, Population, Sampling, & Recruitment

For our fourth week, we'll take a look at how to choose data collection methods that are best suited for your aims, explore the various sampling and recruitment strategies to select participants, and finally consider how research design is an iterative process.

• A Look at This Week • 1 minute • Preview module
• Aligning Methods with Aims • 2 minutes
• Identifying a Setting and Honing in on a Population • 2 minutes
• Sampling Strategies • 6 minutes
• Recruitment Strategies • 2 minutes
• Iterative Design • 0 minutes

2 readings • Total 95 minutes

• Resources for Selecting Appropriate Methods • 35 minutes
• Resources for Identifying Your Strategies • 60 minutes

1 quiz • Total 30 minutes

• Practice • 30 minutes
• Week 4: Identifying Your Methods, Population, Sampling, & Recruitment Strategies • 60 minutes

Research Ethics

Our fifth week is all about ethical considerations in qualitative research. As researchers, we need to be aware and take precautions to ensure our work with human subjects is never exploitative, deceitful, or harmful. We'll go over the key ethical principles to keep in mind when beginning a qualitative study.

4 videos 3 readings 1 quiz 1 peer review 1 discussion prompt

4 videos • Total 12 minutes

• Overview of Research Ethics • 4 minutes
• Key Issues and Benefits of Qualitative Research • 5 minutes
• Strategies for IRBs and Ethics Committees • 1 minute

3 readings • Total 130 minutes

• CITI Certification for Human Subjects Research • 10 minutes
• Studies with Questionable Ethics • 30 minutes
• Resources for Ethics in Qualitative Research • 90 minutes
• Week 5: Considering Ethics in Research • 60 minutes

1 discussion prompt • Total 10 minutes

• Ethics Around the World • 10 minutes

Course Project and Design Examples

In this final week, you'll combine all items of your research design for a final submission. We'll also review real cases of graduates in the MPH program to see the different approaches that can be taken.

5 videos 2 readings 1 peer review

5 videos • Total 47 minutes

• Elements of Research Design: An Example (from Tamar Goldenberg's MPH Thesis Research) • 5 minutes
• Candace Girod: Menstrual Hygiene Management at Schools in Nairobi, Kenya • 13 minutes
• Wendy Avila: Exclusive Breast-Feeding among Women in Managua, Nicaragua • 15 minutes
• Jasmine Kelly: Maternal & Child Nutrition in Rural Tanzania • 11 minutes

2 readings • Total 60 minutes

• Interview Series • 30 minutes
• Preparing Your Research Design • 30 minutes
• Week 6: Your Qualitative Research Design • 60 minutes

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

Emory University, located in Atlanta, Georgia, is one of the world's leading research universities. Its mission is to create, preserve, teach and apply knowledge in the service of humanity.

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Learner reviews

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104 reviews

Reviewed on Oct 3, 2023

It was really a difficult nut to crack but a very useful and descriptive course for those people who wants to do research

Reviewed on Jul 16, 2021

Excellent Course. Improved my understanding of Qualitative Research Design through actually doing it for my research.

Reviewed on Jun 1, 2023

This course is very use full and practical course.

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