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This paper provides an assessment of methodological development of online and blended learning research in each of the primary business disciplines. We present a summary of variables examined and quantitative analytical techniques used by discipline and in multi-disciplinary studies from 157 articles in refereed journals published from 2000 through 2010. We found widely varying research activity and methodological variety across disciplines, with most of the studies published using samples from information systems, management, or multi-disciplinary settings. However, a discipline’s number of studies and methodological rigor were not necessarily correlated. For example, although a relatively small number of studies of economics courses were published, this discipline was comparatively innovative in their selection and operationalization of variables of interest. The paper concludes with recommendations both by discipline and collectively for improving this emerging stream’s research quality, with particular emphasis on how each of the disciplines might incorporate more of their native analytical tools and techniques into conducting research on online teaching and learning.

Related Papers

Ben Arbaugh

In this literature review, we examine and assess the state of research of online and blended learning in the business disciplines with the intent of assessing the state of the field and identifying opportunities for meaningful future research. We review research from business disciplines such as Accounting, Economics, Finance, Information Systems (IS), Management, Marketing, and Operations/Supply Chain Management. We found the volume and quality of research in online and blended business education has increased dramatically during the past decade. However, the rate of progress is somewhat uneven across disciplines. IS, Management, and multi-disciplinary studies have the highest volumes of research activity, with markedly less activity in Finance and Economics. Furthermore, scholars of online and blended business education predominantly publish in learning and education journals of the business disciplines rather than also publishing in journals that focus on technology-mediated learning, thereby missing an opportunity to inform scholars in other disciplines about their work. The most common research streams across disciplines were outcome comparison studies with classroom-based learning and studies examining potential predictors of course outcomes. Results from the comparison studies suggest generally that online courses are at least comparable to classroom-based courses in achieving desired learning outcomes, while there is divergence in findings of comparisons of other course aspects. Collectively, the range of untested conceptual frameworks, the lack of discipline-specific theories, and the relative absence of a critical mass of researchers focused on the topic suggest ample opportunities for business scholars seeking to enter this research community.

quantitative research about online business

The Internet and Higher Education

Marianne Johnson , Bruce Niendorf , Ben Arbaugh

Prof. Sanjay Verma

The growth of online education has become a global phenomenon driven by emergence of new technologies, widespread adoption of the Internet, and intensifying demand for a skilled workforce for a digital economy. Online education is no longer a trend; it is slowly but surely becoming mainstream by 2025. This paper explores all efforts, accomplishments, issues, challenges, conclusions, and recommendations on this theme through meta-analysis of over 100 published papers since 2000. Through thorough content analysis, we provide useful recommendations for researchers and practitioners working in academia, industry, or government. We also propose a holistic model of interactions between diverse entities and stakeholders in the online tertiary business discipline education industry. This model will certainly be applicable with minor changes to other disciplines and other levels of education—primary and secondary. This model can be tested in piecemeal fashion by researchers using appropriate...

The International Journal of Management Education

Shailendra Palvia

Ben Arbaugh , Ashay Desai

This paper reviews studies of online and blended learning in management-oriented disciplines and management-related topics. The review shows that over the last decade, this emerging field has seen dramatic conceptual, methodological, and analytical advances. However, these advances have progressed within the particular disciplines at uneven rates. Studies examining courses in Organizational Behavior and Strategic Management have seen the most progress, with courses in Human Resources, Operations Management, and International Management receiving lesser attention. To date, studies of courses in Entrepreneurship are next to non-existent. Our review suggests that although several multi-course studies have been published, there is ample opportunity for research within the respective management disciplines. We also suggest topics and methodological issues requiring further study, including stronger delineations between online and blended management education; further examination of participant characteristics, particularly for instructors; and the influence of institutions located outside North America.

Ben Arbaugh , Alvin Hwang

This manuscript reviews and compares the use of multivariate statistical techniques in 85 studies of online and blended management education over the last decade relative to prescriptions for their use offered by both the organization studies and educational research communities. Although there is variation in the degree to which appropriate uses of the techniques have been employed, they appear to have been accepted and adopted at a much faster rate than typically is the case in organizational studies research. In fact, the nature of research samples to date indicates that the recent introduction of HLM techniques to this research stream may be premature. Other recommendations that emerge from the review include greater consideration of moderating effects, particularly of those that historically have been considered “control” variables, and reducing dependence upon EFA techniques for data reduction except when examining conceptual frameworks comprised of constructs borrowed from disparate fields. It is our hope that this review motivates further consideration of appropriate uses of these techniques in other areas of management education research.

Diane Fulton

The purpose of this research is to analyze the optimal pedagogical tools and methods for teaching quantitative disciplines in the newest delivery modes of blended and online education. This study will focus on a comprehensive literature review of quantitative disciplines in business and related areas. Which pedagogies are the same and which are different based on discipline? Practices, tools and approaches that are used and deemed effective in online learning will be overviewed and analyzed across disciplines in this exploratory research. The top rated skills and competencies for each quantitative discipline will be reviewed and summarized for similarities and differences. From this preliminary research, specific research proposals will be recommended for future research on quantitative discipline-specific best practices in the blended/online delivery of such courses.

Dr. Qaisar Abbas

Qaisar Abbas

Online education and its methods have been challenged by researchers since its widespread adoption. Over the past few decades, technology, globalization, and business model innovation have transformed business. Objectives of the study were to assess the effects of online learning on the performance of business students, explore the challenges that hinder online learning of business students and to provide strategies to improve online learning of business students. This study may help online course developers and teachers conceive, develop, and deploy online learning methods. Support staff who help establish curriculum, support services, and professional development may benefit from developing ways to satisfy students' requirements. There was a quantitative analysis carried out. The study used a survey to collect data, and its design was descriptive in nature. A survey consisting close ended questions related to various study variables were administered to a sample of 250 business students of Private universities in Islamabad Pakistan. Data collection was done through personal visits of the researcher. To evaluate the data, descriptive statistics are used, such as the mean, standard deviation and T-test. Study found the positive perceptions of academic performance and skills development which suggest that online learning can effectively contribute to students' educational outcomes. Furthermore, study identified challenges, such as technical issues and motivational barriers, underscore the need for targeted interventions to improve the online learning experience. Fostering interactive online content is recommended by the study, as it correlates positively with critical thinking and collaboration, key skills that contribute to academic success.

Journal of Eastern European and Central Asian research

Madina Duchshanova

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quantitative research about online business

Home Market Research

Quantitative Market Research: The Complete Guide

Quantitative Market Research

What is Quantitative Market Research?

Quantitative Market Research is a technique to ask questions to the target audience in an organized manner using surveys, polls or questionnaires. Received responses can be analyzed to make well-thought decisions for improving products and services, that will in turn help increase respondent satisfaction levels. Well-founded results can be achieved in case a large sample size that represents a population is surveyed.

The age of Information has transformed both selling as well as purchasing habits and norms. “Information” or “data” is now more valuable than gold. Companies rise and fall on the basis of how well they are able to collect and analyze data and make informed decisions based on the gathered insights.

LEARN ABOUT: Marketing Insight

Any evolved customer who makes a purchase online can tell how quickly businesses have become “customer-centric”. And the first step towards becoming a customer-centric business is through customer feedback and research design .

LEARN ABOUT: Market research vs marketing research

Quantitative Market Research Quote

For instance, “Based on your overall experience with us, how likely are you to recommend us to a friend or colleague?” – This one question, the Net Promoter Score question, changed the game for businesses across the Globe. With just 1 question, companies are now able to collect real data from real customers on how well their organic word-of-mouth referrals can grow their business and how less/more they have to spend on paid advertising and promotions or which area of their product or service quality requires improvements.

This is just 1 in hundreds of such Quantitative Market Research survey questions that have fundamentally and exponentially helped organizations, including nonprofits, charities, educational institutions and business alike, to make decisions that are based on real data!

Organizations are dependent on quantitative analysis for the statistical evaluation of data because it gives systematic, detailed information about the research problem at hand or the target audience. This market research technique revolves around surveys , questionnaires and polls and the data collected is evaluated numerically, statistically, mathematically to form better strategies and marketing plans.

LEARN ABOUT:  Market research industry

Methods to Conduct a quantitative market research

But before we dive into the steps that are required to carry out a successful Quantitative Market Research study, let’s look at a few more critical reasons why you need to do so.

LEARN ABOUT: Causal Research

Reasons to conduct Quantitative Market Research

  • Research is the first step for a successful marketing campaign, be it a new product launch, sales pitch positioning or conducting a data-oriented statistical analysis .
  • By conducting an online quantitative market research, insights about marketing activities like updating the website, social media page management or newsletters can also be received.
  • By implementing Quantitative Market Research, questions like “Who are currently buying my products/services?”, “Why are the others not buying my product?”, “How to reach out to my potential clientele?” are answered.
  • Quantitative research starts with survey creation, designing, and distribution. After the survey is sent out to the right people, data collection(active or passive data collection ) and analysis has to be done to get desired insights.

LEARN ABOUT: Best Data Collection Tools

steps of quantitative market research

Significance of Quantitative Market Research

As the name implies, Quantitative market research focuses on the quantity and structured collection of data. It began with face-to-face techniques and now has evolved into online surveys like those provided by QuestionPro. It is often used to capture data like customer behavior , size of the market, identifying reasons for product repurchase. This type of market research is usually based on a large number of samples.

LEARN ABOUT: Behavioral Research

Characteristics of Quantitative Market Research

The basic characteristics of quantitative market research are:

  • The premise that quantitative market research operates on is to confirm the hypothesis of the phenomena of how many.
  • The data collected is solely in the form of numbers and statistical formula can be applied to this data to come up quantified actionable insights.
  • Data collected and the mode of collection is very structured. It is a mix of questionnaires , surveys etc.
  • The research study is designed in a way that the questions are structured and the possible responses to these types of question are also structured. This is laid out well in advance before the study.
  • Since the questions are not open ended, they point towards certain answers so the scope for uncertainty is limited.

What is the methodology for creating a successful quantitative market research survey?

Quantitative market research is a highly scientific method of market research. It uses deductive reasoning to come to a conclusion and create actionable insights from the data collected. This research method works on the principle of developing a hypothesis, collecting data and then analyzing that data to further prove or disprove the hypothesis. The milestone based procedure of the quantitative design is:

  • Make an observation of something that is unknown to you. Investigate the theory that is related to your issue or the field that requires validation.
  • Create an in-depth hypothesis to validate your research and findings and end objective.
  • Plan for how to prove or disprove this hypothesis and create a structure to achieve this objective.
  • Collect and analyze your data. If your data validates your hypothesis, prepare for final validations and to present findings. If the data disproves your hypothesis, you can either start afresh with a new hypothesis or drop your current research.

The milestones mentioned above fall under 5 quantitative design types namely; survey research , descriptive research , correlational research , causal-comparative/ quasi-experimental research and experimental research .

LEARN MORE: Descriptive Research vs Correlational Research

What are the common techniques to conduct a quantitative market research?

Quantitative market research can be conducted by primary and secondary research types. Some of the Some of the most common ways to conduct a quantitative market research are:

Primary quantitative market research techniques

Primary techniques are the most common forms of conducting quantitative market research. Some of the most common and widely used forms are:

  • Cross-sectional research survey:  Cross-sectional market research is a quantitative market research method that analyzes data of variables collected at one given point of time across a sample population. population or a pre-defined subset. This research method has people who are similar in all demographics but the one that is under research.
  • Longitudinal research survey:  Longitudinal market research is a quantitative market research method where research is conducted over years or decades on a target demographic markets or certain individuals to collect statistical data. 

LEARN ABOUT: Research Process Steps

  • One-on-one Interviews: This quantitative data collection method was also traditionally conducted face-to-face but has shifted to telephonic and online platforms. Interviews offer a marketer the opportunity to gather extensive data from the participants. Quantitative interviews are immensely structured and play a key role in collecting information. There are two major sections of these online interviews:
  • Face-to-Face Interviews: An interviewer can prepare a list of important questions in addition to the already asked survey questions. This way, interviewees provide exhaustive details about the topic under discussion. An interviewer can manage to bond with the interviewee on a personal level which will help him/her to collect more details about the topic due to which the responses also improve. Interviewers can also ask for an explanation from the interviewees about unclear answers.
  • Online/Telephonic Interviews: Telephone-based interviews are no more a novelty but these quantitative interviews have also moved to online mediums such as Skype or Zoom. Irrespective of the distance between the interviewer and the interviewee and their corresponding time zones, communication becomes one-click away with online interviews. In case of telephone interviews, the interview is merely a phone call away.

Secondary quantitative market research techniques

Secondary techniques to conduct quantitative market research are a means to validating a hypothesis or drawing conclusions from empirical data and primary data. This research method is a form of observational research where historical data helps validate the statistical observations of the primary data. For example: mapping the purchase of snowblowers to the months where sales spike with historical data of inclement weather helps manage supply and demand as well as trained personnel during those months.

LEARN ABOUT:  Test Market Demand

5 steps needed for creating a successful quantitative market research survey:

  • Specify the Goal: Why do you want to conduct this market research? There should be a clear answer to this question so that the steps that follow are smoothly executed.
  • Have a Plan Sketched Out: Every step that needs to be achieved has to be put to paper like the tools that are required to carry out the research,  survey templates , the target audience etc. This may vary from project to project.
  • Collect Data: This is the most crucial step in this market research. Data is collected through 3 main mediums: online surveys, telephone interviews or email surveys .

Quantitative Market Research Analysis

  • Compile Reports: A report consisting of graphs, charts, and tables should be created so that the person in-charge of the report can incorporate the observed changes.  

Learn more about Quantitative Data

Guesswork or limited awareness of numbers can never result in the success of an organization. Quantitative market research offers the perfect medium for researchers to analyze customer behavior and adaptability so that the growth of the organization isn’t hampered.  

Quantitative market research questions – Use and Types

According to the objective of research, the survey creator can decide the type of questions to be used. To put it briefly:

  • Quantitative market research questions produce answers for “Who” and “What”.
  • Qualitative market research questions produce answers for “Why”.

Quantitative questions are usually close-ended and are simpler to analyze when compared to the qualitative counterparts which are open-ended and much harder to analyze. If you’re looking to obtain statistics and quantifiable results, you can implement quantitative market research questions.

These questions are easy for the respondents to answer. Due to their close-ended nature, a sizeable quantity of questions can be asked without having to worry about whether the respondents will get irritated by them or not.  Quantitative questions can start with “how” or “what” and can be used in questions such as “how frequently” or “how many” or “what are” or “what is the extent”.

The most used quantitative market research questions are:

Net promoter score : This question can be asked to evaluate customer satisfaction and brand shareability. It’s usually a 0-10 scale which provides a very filtered yet efficient perspective about brand recommendation. The respondents are divided on the basis of the provided input.

Improve Net Promoter Score

Likert-scale: It’s a psychometric question to evaluate customer opinions towards a particular situation with two polarities at each end of the scale. The Likert-scale question has a statement and 5, 7 or 9 response options for the respondents to choose from. These questions used for customer satisfaction , employee satisfaction , and academic surveys .

Likert scale example for 5 response options

Semantic-scale: Semantic differential rating scale is used to ask quantitative questions about ideologies, products or events with grammatical opposite options at the polar positions of the scale to measure their implicative meaning.  

Multiple-choice: These fundamental components of a survey can be vital in getting the best responses in quantitative research as they provide the exact options that an organization would want their respondents to choose from.

multiple choice questions

Matrix questions: These are multiple choice questions assembled in form of a matrix. They are extremely convenient for survey makers to create and analyze these kinds of questions and for respondents to construe and answer.


Read more: Survey Questions and Sample Survey Questions

Statistical Analysis in Quantitative market research

Quantitative market research uses a host of statistical analysis techniques to process the response data and derive meaningful and clear insights. These insights gathered from statistical analysis enables researchers to derive the final conclusion of the quantitative research.

LEARN ABOUT:   Statistical Analysis Methods

Here are 5 commonly used statistical analysis techniques:

  • Conjoint Analysis:

Conjoint analysis is a method used to identify the value of various attributes such as cost, features, benefits for the customers that lead to the purchase of a particular product or service. With increasing technology implementation features in devices and gadgets, this analysis method has been widely adopted for product pricing, market placement, and product launch.

  • TURF Analysis:

TURF (Total Unduplicated Reach and Frequency) analysis allows an organization to gain insights on a combination of products/services that’ll attract the highest number of customers. This is done by producing the reach and frequency of unduplicated data from the obtained responses.

  • GAP Analysis:

GAP analysis is used to calculate the difference between the desired and actual performance of a particular product/service. By measuring GAP analysis , an organization can make improvements to mend the gap and make their attributes more appealing to reduce the gap.

  • MaxDiff Analysis:

Also known as “best-worst” scaling, MaxDiff is choice-model used to acquire customer preferences of multiple characteristics such as product features, brand images, and preferences, activities around the branding etc. It does have some similarity to Conjoint analysis but is much simpler to implement and analyze.

  • Cross Tabulation:

Cross-tabulation is a statistical analysis tool that allows comparison of two or more categories in a brief tabular format for convenient data analysis .

Advantages of quantitative market research:

  • Produces numerically rational theories: The result of the quantitative research is based on numbers because of which results are extremely instrumental for an organization to make well-thought decisions to market a product/service in a better manner. The numbers analyzed in this can be then put into charts and graphs for better representation and review.
  • Easily calculable and analyzable data: Due to the exactness in the answers received for quantitative questions, it’s extremely favorable for research to evaluate the data.
  • Enhanced willingness of respondents: Quantitative research mostly comprises of close-ended questions which are quick and less time-consuming for the respondents to answer. This is an essential reason for high response rates for this market research.
  • Less investment to create brand awareness: These days, quantitative research is used for brand awareness which is generally conducted through online mediums. Cost invested in the research is thus reduced to create awareness about the brand.

Disadvantages of quantitative market research:

  • Statistical data isn’t always complete: Data could be collected from a huge number of people but there is no way to dig deep down into they “why” of an answer. Data isn’t actionable with just numbers and no concrete explanations to back that data.
  • Structured interviews and questionnaires: The biggest strength but also a weakness of quantitative market research questions is the limited scope to digress from a structured answer. Whilst this provides actionable numbers, the research questions do not allow to validate those numbers due to the nature of how the survey is set-up.
  • Sample size isn’t indicative of a larger population: If the respondents of the market research survey have attributes that do not match those of a larger demographic, the data collected cannot be equated to a larger sample as the data collected isn’t necessarily a representation of the larger audience.
  • Self-report isn’t always trustworthy data: People when given the liberty to respond to a survey are skeptical to give out too much information and if any information provided is incorrect or haphazard, that discounts the complete validity of the survey.

How does Quantitative Market Research  work  using QuestionPro?

QuestionPro offers a string of standard and advanced question types like single select, multi-select, Net Promoter Scale or Van Westendorp etc. that can be chosen to create a powerful survey. The survey has to be branded and personalized as per your company policies and also has to include logic and branching suitably.

Types of Customer Satisfaction Surveys

Distribution of surveys using the right mediums is an integral part of data collection. You can reach as many people as you wish to by using sources like Emails that can also be scheduled, QR code, Mobile application that allows offline data collection , Automated IVR surveys , and Web intercept surveys .

Distribute Customer Satisfaction Survey

Responses are updated on a dashboard as and when respondents take the survey. As a survey maker, you can keep an eye on the live updates of the customers who’ve started the survey but not yet finished it or who’ve completed it or who’ve just begun, on the dashboard.

LEARN ABOUT: Level of Analysis

Using techniques like Conjoint Analysis, SWOT Analysis, TURF Analysis, one can obtain a solid statistical understanding of the collected data for organizations and academicians. The updates in analytics are done in real-time using advanced analytics programs.

LEARN ABOUT: 12 Best Tools for Researchers

This marketing research method is used to know how alike do people think about a certain product and derive results for data-oriented decision making. When a new product is being launched or a product is being upgraded, quantitative market research can be put to use to know what the target audience thinks about the change and whether it will be well adapted.

LEARN ABOUT: Average Order Value


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Exploratory Research of Online Learning in Quantitative Business Courses

  • Diane Joyce Fulton Clayton State University
  • Richard Allen Fulton Troy University - Global Campus

The purpose of this research is to analyze the optimal pedagogical tools and methods for teaching quantitative disciplines in the newest delivery modes of blended and online education. This study will focus on a comprehensive literature review of quantitative disciplines in business and related areas. Which pedagogies are the same and which are different based on discipline? Practices, tools and approaches that are used and deemed effective in online learning will be overviewed and analyzed across disciplines in this exploratory research. The top rated skills and competencies for each quantitative discipline will be reviewed and summarized for similarities and differences.

Author Biographies

  • Diane Joyce Fulton, Clayton State University Dr. Diane Fulton has been an educator for 45 years and is Emeritus Professor at Clayton State University in the College of Business. She has taught Management and Marketing courses and most recently, Sports, Film and Entertainment courses at undergraduate and graduate levels. She has several books, book chapters and numerous scholarly publications in journals such as Planning, Small Business Journal, Enterpreneurship Journal, Organizational Dynamics, and Journal of Technology Research.

Assistant Professor Richard A. Fulton (M.S., Illinois State University) teaches computer science and information systems courses at Troy University (Alabama) for its Global Campus. He has recently published in The Journal of Technology Research, The Journal of Scientific Information on Political Theory and the International Journal of Innovation, Technology and Management.

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What Is Quantitative Business Analysis, and How Can it Help My Business?


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What Is Quantitative Business Analysis, and How Can it Help My Business?

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Every company owner wants to understand how their business is doing. And every potential investor wants to ensure they have as much information as possible about a company before they put their money into an organization. Running a company is a complicated and demanding process, and it’s not always easy to get a clear picture of the situation—especially when you’re in the middle of its affairs. Business analysis techniques provide us with a framework to ask relevant questions and understand just what’s going on within an organization.

In this blog post, we’ll take a closer look at the benefits of quantitative business analysis and the kinds of tools it brings to the table.

Let’s dive right in.

Qualitative vs. quantitative analysis

Qualitative vs. quantitative analysis

We can break business analysis into two types—qualitative and quantitative analysis. Both provide valuable insight, and using them together is the best way to evaluate a business successfully. So, before we get into the details of quantitative business analysis (QBA), let’s quickly compare the two approaches.

Qualitative analysis

Qualitative analysis involves examining aspects of the business and its market that cannot be quantified (expressed numerically). For example, we might use qualitative techniques to assess a business’s:

  • Core business model   — how does the company operate?
  • Motivation — what is the company trying to achieve?
  • Integrity and values — how does the company measure up ethically?
  • Corporate governance — do the people involved live up to the company values?
  • Target audience — what are the customers’ goals, aspirations, and fears?

Clearly, these are important factors in any business decision. They are also impossible to quantify. We can assess these aspects in different ways – many of which will be particular to us – but we can’t use them to crunch numbers and reach meaningful conclusions.

Quantitative analysis

Quantitative business analysis means using hard data to assess the health of a business and make predictions about its future. With QBA, we ask questions using specified parameters and variables and use numerical values to express the resulting data.

For example, as an investor assessing a potential investment, you might set minimum acceptable values for the following factors:

  • Earnings per share.
  • Return on equity.
  • Return on capital.
  • Free cashflow.

After analyzing the data, you would make a decision based on whether the business in question exceeds your minimum values or not.

What quantitative business analysis is used for

What quantitative business analysis is used for

Whether you’re an investor looking to assess the performance of a prospective investment, or a business owner aiming to make your business more efficient or profitable, quantitative business analysis provides you with the tools you need to make decisions. QBA techniques are often used to examine relationships between variables, such as:

  • Market share.

We can use QBA to assess most aspects of business performance and help us understand hidden correlations and relationships. Some typical applications are:

  • Forecasting.
  • Reducing costs and increasing profit.
  • Predicting customer behavior.
  • Understanding brand penetration.

The role of statistics

The role of statistics

Statistics often get a bad rap. You’re probably familiar with the phrase, “There are three kinds of lies: lies, damned lies, and statistics.” Although the origin of the phrase is uncertain (they’re often attributed to Mark Twain, but he attributed 19th-century British prime minister Benjamin Disraeli, and there’s no record of him using the phrase), many of us have taken the words to heart and are somewhat mistrustful of statistics.

While it’s true that statistics without proper context are meaningless and misleadingly presented statistics can be used for dubious purposes, statistical methods are the backbone of quantitative business analysis and give us powerful tools to aid our business decision-making.

Quantitative business analysis techniques

We’re not going to dig too deep into the technical side of QBA here, but it is advisable to get a basic understanding if you’re planning on combining quantitative business analysis and business decisions. There are a dizzying number of quantitative analysis methods that we can use in business analytics, but today we’ll stick to a few of the most commonly used techniques.

Break-even analysis

One of the more straightforward types of QBA, we use a break-even analysis to compare business spending with revenue over a specific time period to determine how much money the company has to bring in to cover its costs.

Regression analysis

We use regression analysis to assess the relationship between two or more variables. If there are two variables, we call it simple regression. Multiple regression involves three or more variables. An example would be identifying a correlation between how much we spend on materials to produce a product and the profit the product generates.

Time series analysis

With time series analysis, also known as trend analysis, we examine historical data to predict future performance. An example would be looking back over sales performance during past Christmases and using that data to make a forecast for the upcoming festive season. This method is best used for short-term forecasting.

How quantitative business analysis helps businesses

How quantitative business analysis helps businesses

By using verifiable, high-quality data to assess business performance and make forecasts, we free ourselves from the biases and emotional reactions that can cloud our judgment. We also find a deeper understanding of the currents and trends that shape the market but aren’t necessarily obvious.

We can use QBA not only to assess business performance, but improve it. By implementing plans based on the result of our analyses, we can see improvements in areas like:

  • Cost efficiency.
  • Team performance.
  • Sales forecasting.
  • Brand recognition.

How to implement quantitative business analysis

While you can utilize QBA techniques yourself, unless you’re a statistician or data scientist, it’s going to be a challenge with a steep learning curve. If the business you want to analyze is small, or you only want to answer one or two simple questions, a DIY approach could work. There are plenty of courses available online to help you learn the skills. Beware though, because poorly designed or implemented analysis is at best a waste of time and could cause damage if you make business decisions based on inaccurate data.

For most people, employing a professional is the best way to get reliable, meaningful results. An experienced business analyst will work with you to determine what data you want to gather, the most appropriate methods to collect them, and which techniques should be used for the analysis.

Whichever route you decide to take, the process will go something like this:

  • Define the questions — what exactly are you trying to learn about the business? It’s vital to be as precise as possible.
  • Determine which analysis technique to use — this will depend on what you’re trying to get out of the analysis.
  • Decide how you’re going to collect the data — for example, will you conduct an email campaign? Use a crowd-working platform? Physically ask people questions as they leave a store?
  • Implement the data collection methods — get the infrastructure in place and conduct any necessary staff training.
  • Gather the data — remember, the data needs to be high quality and relevant.
  • Perform the analysis — apply your chosen technique to crunch the numbers.
  • Use the findings to take action — once you have your results, do something with them. If you find yourself dismissing or ignoring the results, ask yourself why. Did you ask the wrong questions? Make mistakes with the data? Or are the results telling you something you’re reluctant to admit?

Things to remember

Things to remember

Before jumping into your analysis, make a plan. Lay out exactly what you want to achieve and how you’re going to achieve it. Here are a few things to bear in mind:

  • Ask the right questions — it’s crucial to ensure that the data you’re gathering is relevant and usable.
  • Use high-quality data — faulty data leads to false conclusions, so gather your data carefully.
  • Keep it simple — don’t try to analyze too much at once – you’ll run the risk of confusing the results.
  • Context is key — no business exists in a vacuum, so make sure to include competition and the broader market in your analyses.
  • Bad analysis is worse than no analysis — if in doubt, hire a professional!

The bottom line

Quantitative business analysis allows us to dig deep into data to understand business performance, identify patterns that might not be immediately obvious, and make reliable forecasts about the future. We can use QBA techniques to inform our business decisions and improve performance within our organizations.

QBA can be used alongside other types of analysis to form a complete picture of a company’s health. Because the methods involved are complex, it’s a good idea to consult a professional business analyst before you get started. Understanding the process is valuable, though, so it’s worth getting to grips with the terms and various techniques involved.

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

What Is Quantitative Research? | Definition, Uses & Methods

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

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

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

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

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

Table of contents

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

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

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

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

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

Quantitative research methods
Research method How to use Example
Control or manipulate an to measure its effect on a dependent variable. To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task. You compare self-ratings of procrastination behaviors between the groups after the intervention.
Ask questions of a group of people in-person, over-the-phone or online. You distribute with rating scales to first-year international college students to investigate their experiences of culture shock.
(Systematic) observation Identify a behavior or occurrence of interest and monitor it in its natural setting. To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds.
Secondary research Collect data that has been gathered for other purposes e.g., national surveys or historical records. To assess whether attitudes towards climate change have changed since the 1980s, you collect relevant questionnaire data from widely available .

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

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

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

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

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

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

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

  • Replication

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

  • Direct comparisons of results

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

  • Large samples

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

  • Hypothesis testing

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

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

  • Superficiality

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

  • Narrow focus

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

  • Structural bias

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

  • Lack of context

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

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

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

Research bias

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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

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

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Research Method

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

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What is Quantitative Research

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

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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.

Differences between Qualitative and Quantitative Research

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:

Types of Quantitative Research


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


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


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.

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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Research: Speed Matters When Companies Respond to Social Issues

  • Alison Wood Brooks,
  • Maya Balakrishnan,
  • Julian De Freitas

quantitative research about online business

An analysis of Instagram posts by Fortune 500 companies after George Floyd’s murder found that customers were skeptical of those who waited too long to make a statement.

Companies and their leaders face new pressures to make public statements about controversial and sometimes divisive social and political issues. New research shows that timing matters: consumers perceive a relationship between speed and authenticity, and discount statements from companies that wait too long to respond. Leaders can use four questions to understand when and how they should shape their response.

In the wake of George Floyd’s killing by Minneapolis police officers in 2020, individuals began protesting racial injustice both in person and online. But it wasn’t just individuals — many well-known companies in corporate America seemed to publicly align themselves with protestors, rather than staying silent or neutral.

  • Alison Wood Brooks is the O’Brien Associate Professor of Business Administration at Harvard Business School.
  • JN Jimin Nam is a doctoral candidate in marketing at Harvard Business School.
  • MB Maya Balakrishnan is a doctoral candidate in technology and operations management at Harvard Business School.
  • Julian De Freitas is an assistant professor in the marketing unit at Harvard Business School.

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