what is quantitative research in business

Quantitative research: Understanding the approaches and key elements

Quantitative research has many benefits and challenges but understanding how to properly conduct it can lead to a successful marketing research project.

Choosing the right quantitative approach

Editor’s note: Allison Von Borstel is the associate director of creative analytics at The Sound. This is an edited version of an article that originally appeared under the title “ Understanding Quantitative Research Approaches .”

What is quantitative research?

The systematic approaches that ground quantitative research involve hundreds or thousands of data points for one research project. The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell. 

Quantitative research aggregates voices and distills them into numbers that uncover trends, illuminates relationships and correlations that inform decision-making with solid evidence and clarity.

The benefits of quantitative approach es

Why choose a quantitative   approach? Because you want a very clear story grounded in statistical rigor as a guide to making smart, data-backed decisions. 

Quantitative approaches shine because they:

Involve a lot of people

Large sample sizes (think hundreds or thousands) enable researchers to generalize findings because the sample is representative of the total population.  

They are grounded in statistical rigor

Allowing for precise measurement and analysis of data, providing statistically significant results that bolster confidence in research.

Reduce bias

Structured data collection and analysis methods enhance the reliability of findings. 

Boost efficiency

Quantitative methods often follow a qualitative phase, allowing researchers to validate findings by reporting the perspective of hundreds of people in a fraction of the time. 

Widen the analysis’ scope

The copious data collected in just a 20-minute (max) survey positions researchers to evaluate a broad spectrum of variables within the data. This thorough comprehension is instrumental when dealing with complex questions that require in-depth analysis. 

Quantitative approaches have hurdles, which include:

Limited flexibility

Once a survey is fielded, or data is gathered, there’s no opportunity to ask a live follow-up question. While it is possible to follow-up with the same people for two surveys, the likelihood of sufficient responses is small. 

Battling bots

One of the biggest concerns in data quality is making sure data represents people and not bots. 

Missing body language cues

Numbers, words and even images lack the cues that a researcher could pick up on during an interview. Unlike in a qualitative focus group, where one might deduce that a person is uncertain of an answer, in quantitative research, a static response is what the researcher works with.

Understanding quantitative research methods 

Quantitative approaches approach research from the same starting point as qualitative approaches – grounded in business objectives with a specific group of people to study. 

Once research has kicked off, the business objective thoroughly explored and the approach selected, research follows a general outline:  

Consider what data is needed

Think about what type of information needs to be gathered, with an approach in mind. While most quantitative research involves numbers, words and images also count.

• Numbers: Yes, the stereotypical rows of numbers in spreadsheets. Rows that capture people’s opinions and attitudes and are coded to numbers for comparative analytics. Numerical analysis is used for everything from descriptive statistics to regression/predictive analysis. 
• Words:  Text analysis employs a machine learning model to identify sentiment, emotion and meaning of text. Often used for sentiment analysis or content classification, it can be applied to single-word responses, elaborate open-ends, reviews or even social media posts.
• Images: Image analysis extracts meaningful information from images. A computer vision model that takes images as inputs and outputs numerical information (e.g., having a sample upload their favorite bag of chips and yielding the top three brands).

Design a survey

Create a survey to capture the data needed to address the objective. During this process, different pathways could be written to get a dynamic data set (capturing opinions that derive from various lived experiences). Survey logic is also written to provide a smooth UX experience for respondents.    

Prepare the data

The quality of quantitative research rests heavily on the quality of data. After data is collected (typically by fielding a survey or collecting already-existing data, more on that in a bit), it’s time to clean the data. 

Begin the analysis process

Now that you have a robust database (including numbers, words or images), it’s time to listen to the story that the data tells. Depending on the research approach used, advanced analytics come into play to tease out insights and nuances for the business objective. 

Tell the story

Strip the quantitative jargon and convey the insights from the research. Just because it’s quantitative research does not mean the results have to be told in a monotone drone with a monochrome chart. Answer business objectives dynamically, knowing that research is grounded in statistically sound information. 

The two options: Primary vs. secondary research

The two methods that encompass quantitative approaches are primary (collecting data oneself) and secondary (relying on already existing data).

Primary  research  is primarily used  

Most research involves primary data collection – where the researcher collects data directly. The main approach in primary research is survey data collection.  

The types of survey questions

Span various measurement scales (nominal, ordinal, interval and ratio) using a mix of question types (single and multi-choice, scales, matrix or open-ends).  

Analysis methods

Custom surveys yield great data for a variety of methods in market analysis. Here are a couple favorites: 

• Crosstabulation : Used to uncover insights that might not be obvious at first glance. This analysis organizes data into categories, revealing trends or patterns between variables. 
• Sentiment analysis: Used to sift through text to gauge emotions, opinions and attitudes. This method helps understand perception, fine-tune strategies and effectively respond to feedback.
• Market sizing: Used to map out the dimensions of a market. By calculating the total potential demand for a product or service in a specific market, this method reveals the scope of opportunities needed to make informed decisions about investment and growth strategies. 
• Conjoint analysis : Used to uncover what people value most in products or services. It breaks down features into bits and pieces and asks people to choose their ideal combo. By analyzing these preferences, this analysis reveals the hidden recipe for customer satisfaction.
• Job-To-Be-Done : Used to understand the underlying human motivations that drive people to act. People are multifaceted and experience a myriad of situations each day – meaning that a brand’s competition isn’t limited to in-category. 
• Segmentation: Used to identify specific cohorts within a greater population. It groups people with similar characteristics, behaviors or needs together. This method helps tailor products or services to specific groups, boosting satisfaction and sales.

Statistical rigor

Regardless of method, a quantitative approach then enables researchers to draw inferences and make predictions based upon the confidence in the data (looking at confidence intervals, margin of error, etc.)

Let’s not forget secondary research

By accessing a wide range of existing information, this research can be a cost-effective way to gain insights or can supplement primary research findings. 

Here are popular options: 

Government sources

Government sources can be extremely in-depth, can range across multiple industries and markets and reflect millions of people. This type of data is often instrumental for longitudinal or cultural trends analysis. 

Educational institutions

Research universities conduct in-depth studies on a variety of topics, often aggregating government data, nonprofit data and primary data.  

Client data

This includes any research that was conducted for or by companies before the   present research project. Whether it’s data gathered from customer reviews or prior quantitative work, these secondary resources can help extend findings and detect trends by connecting past data to future data.

Quantitative research enhances research projects

Quantitative research approaches are so much more than “how much” or “how many,” they reveal the   why   behind people’s actions, emotions and behaviors. By using standardized collection methods, like surveys, quant instills confidence and rigor in findings.

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Quantitative Market Research: The Complete Guide

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

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

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

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 .

• 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.

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 .

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.

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.

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 .

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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Quantitative Market Research: A Guide + Examples

Quantitative market research is a numbers game.

It’s one of the four types of traditional market research; and a tried, trusted, and proven way to get answers to strategically important questions. 

Whether you’re already familiar with quantitative research, looking for practical examples, or considering using it in your business, I will cover everything you need to know.

What is quantitative market research?

Quantitative market research collects numerical data to help answer a research question or objective. Popular forms of quantitative research include surveys, polls, questionnaires, and demographical data from primary and secondary sources. The data can be easily quantified, compared, and analyzed to establish patterns, trends, and insights that disprove or prove a research question. It’s used by large and small organizations, thanks to modern market research tools like Similarweb.

What questions can quantitative market research answer?

Quantitative data can help a company find answers to strategic questions. It can help organizations find patterns, spot trends, make predictions, and establish averages. Most questions that can be answered by quantitative research help determine the: how, when, what, and where. Some of these include:

• What is the market size ?
• How have the needs of a market changed?
• What is the number of people that make up your target audience?
• How many people are interested in buying your product?
• Is there a market for your products?
• Where does my target audience spend most of their time online?
• The frequency that people buy your product or service?
• How many people are aware of your brand, product, or service?
• What type of people are your best customers?
• How long do people spend on your website?
• What percentage of customers are happy with your product or service?

Read More: 98 Quantitative Market Research Questions & Examples

Types of quantitative market research design

Quantitative market research deals with secondary and primary data–as long as it’s presented in numerical form. There are five key techniques of quantitative research design to know.

Experimental research

Experimental research (AKA true experimental research) is a research technique that analyzes to prove a theory. In most cases, it will involve several theories yet to be proved or disproved.

This type of design creates a controlled environment where multiple variables are examined and observed to establish the cause and effect they each have. Various data types of manipulated in the process and each impact is assessed. The study aims to determine the precise conditions in which the different variables affect each other.

A few examples of experimental quantitative research design include

• The effect of Black Friday Marketing on the success of a business.
• Impact of service delivery issues on the perceived reliability of a brand.
• The effect of a gift with purchase on customer satisfaction levels.

Choosing a suitable quantitative research method is vital, as data collection can be utilized for different effects. For instance, statistics can be correlational (which helps infer conclusions about differences) or descriptive (which help to summarize data).

Descriptive research

This type of quantitative research is used to learn more about a specific topic, for instance:

Through observation, it measures different variables and investigates each in detail. It aims to describe characteristics– and is focused more on the ‘what’ of a research problem than the ‘why’ behind it. Aptly named, it describes a research subject without investigating why it happens.

A few examples of descriptive research include:

• A company’s Black Friday marketing campaign description.
• The description of service delivery issues a company or its customers face.
• An outline of what companies offer a gift with online purchases.

Quasi-experimental research

This is similar to experimental research (aka casual comparative research), which seeks to evaluate cause-and-effect relations among variables. However, in the case of quasi-experimental research, the key difference is that it’s an independent and dependent variable that is used.

This type of quantitative research design takes at least two types of data, analyzing each together to examine the differences–using a typical cause-and-effect methodology. Research is usually undertaken in a near-natural setting, with information being gathered from two groups.

• A naturally occurring group that’s closely matched with the original environment.
• A group that is not naturally present.

In doing this, causal links can be made. However, not all casual links will be correct due to other variables impacting results.

Examples of descriptive quantitative design include:

• The effect of the Black Friday campaign’s success on employee productivity.
• Service delivery issues effect on the public perception of a brand.
• The effect of free gifts on customer loyalty.

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Correlational research

Correlational research is usually conducted to determine the relationship between two closely related entities. It looks at how each impacts the other and details the changes that occur.

This type of quantitative research design examines relationships between multiple data types. It will examine the extent to which they align with one another or where they differ. It will not delve into casual links any deeper than this.

Examples of quantitative correlational design include:

• The relationship between Black Friday campaign success and annual revenues.
• Correlations between delivery issues and brand reputation.
• The relationship between free gifts and their perceived loyalty.

Quantitative market research data collection methods

You have a few options when considering which type of quantitative research is best. The first thing you’ll need to do is choose the data collection method. Below, I’ve summarized three of the most common quantitative research data collection methods.

This applies to telephone, video conference, or face-to-face interviews. While it’s an ideal way to connect with individuals to collect data, it’s a method that utilizes resources due to the time it takes to set up and conduct them.

A market research survey is a cost-effective way to collect quantitative data. Information can be obtained from large groups of people quickly, and the survey itself is relatively easy to set up. Your survey questions must be carefully considered for the results to provide meaningful data . When creating any form of survey for this type of market research , the questions should remain close-ended, giving participants a yes/no answer or one that requests a numerical result.

A few examples of quantitative market research survey questions include:

• Would you recommend Similarweb to a colleague?

2. On a scale of 1-10, with one being the lowest and ten being the highest, how would you rate your experience with the Similarweb customer support team today?

3. Could you find the information you were looking for on our site today?

4. On a scale of 1-10, with one being the lowest and ten being the highest, how easy could you find the information you were looking for on our site today?

5. Was your query resolved in full by our support representative?

While similar, a poll is a shorter survey version. It’s often used to give researchers a point-in-time perspective of a large group of people. Data can be collected in person, over the phone, or online. The costs for polls can vary, depending on whether you buy questions on an existing poll, such as YouGov, or if you opt for a more bespoke survey that you create from scratch.

Fun fact: The origins of polls date back to the 19th century. They were first used in America to predict the outcome of the presidential elections.

Quantitative market research advantages and disadvantages

As with all market research, there are pros and cons to consider. While there are many benefits of using quantitative market research, it’s important to weigh these up with the drawbacks to ensure you make the best choice for your project.

Benefits of quantitative market research

The information you obtain directly results from the questions asked and the audience you choose. Get these two factors right, and you’ll reap the rewards in your research. Here’s a quick summary of the advantages doing quantitative research offers.

• Collect a vast volume of data efficiently with a larger sample pool.
• Get a generalized view of a target audience and demographic.
• Results can be processed quickly as they are highly structured.
• Easy comparison of results from different groups of participants.
• Its objective–relying on solid numbers with fewer variables.
• Number-based research is ideal for analysis.

Disadvantages of quantitative market research

While all quantitative market research collection methods can generate insightful data showing a wider opinion, there are limitations to consider.

• If respondents are not representative of your target audience, this could potentially impact the accuracy of results–it’s also known as a sampling error.
• The wording of questions can impact the findings–consider this carefully when designing interviews, polls, or surveys.
• Quantitative research is close-ended, with no ability to receive data about the ‘why’ or ‘how’ behind the numbers. Findings can only provide a small part of the story without two-way dialogue.
• You’ll need a hypothesis and an appropriate model to avoid invalid results or bias to collect and analyze the data.

What strategies are used to ensure the accuracy of quantitative market research?

Researchers employ several strategies to ensure the accuracy of their quantitative market research. This includes using various data sources to ensure that no single source is unduly influencing the results. Additionally, researchers may use advanced statistical techniques such as regression analysis and factor analysis to ensure that their results are accurate and valid. Lastly, researchers may employ survey design principles such as random and stratified sampling to ensure that the results represent the studied population.

Using Similarweb for quantitative market research

For all the advantages that quantitative market research offers, it’s hard to ignore the limitations. Things like timeliness, bias, and the close-ended nature of this method all matter when you need to make important decisions and don’t have time to take on a lengthy research project.

That’s where we come in.

Depending on your market research questions, there’s usually a faster way to achieve your goals with insights gained from digital research intelligence software like Similarweb. Whether you want to learn more about a target audience, market, industry, or competitors, you can get up-to-date intel that’s on point, easy to understand, and accurate.

Consider your research question, and see what insights and information are available to you right now. With a world of data at your fingertips, you can harness Similarweb Digital Research Intelligence to uncover telling facts, that inform research and strengthen your position. Use it for:

Market Research

Benchmarking

Audience Insights

Company Research

Consumer Journey Tracking

Use it to uncover the insights you need to make decisions and develop strategies that help you win. 

Wrapping up

With all types of market research, it’s important to take a balanced approach. Organizations that use quantitative market research to get numerical data must balance this with qualitative data to understand the sentiment behind the numbers. So, while quantitative research has its advantages, it must be done in tandem with other research types to provide a complete picture that tells you what, when, how, and why.

Similarweb’s suite of digital intelligence solutions offers unbiased, accurate, honest insights you can trust. Take it for a trial run today, and see how it can power up  your research and save you time. 

What are the four types of market research?

The four main types of market research include primary, secondary, quantitative, and qualitative. While there are subcategories, most research falls into one of these four key categories.

What is the difference between quantitative and qualitative market research?

Quantitative market research is focused on numbers. It collects numerical data to inform a research question or develop a theory. On the other hand, qualitative research is more about consumer sentiment, looking at how and why people feel a certain way about a product, service, or brand.

What are the benefits of quantitative market research?

As it deals with numerical data, quantitative research data can be analyzed quickly and consistently. Future replication is an easy and effective way to conduct a broad study across a large sample size. There are also fewer variables as data is close-ended. Both collection and analysis can be automated and costs less than qualitative research.

What types of questions can quantitative market research answer?

Quantitative research can help answer questions that explain what, how much, when, and where. It seeks to quantify attitudes, behaviors, and opinions but can also be used for establishing averages, making future predictions, and trendspotting.

Who is quantitative market research for?

Quantitative research data delivers information that can help shed light on a market or business. This makes it valuable to both established firms and start-ups of any size. Practically, it can help with market sizing, forecasting, market validation , and more.

What advancements have been made in quantitative market research?

In the past decade, technological advances have enabled quantitative market research to become even more precise and comprehensive. AI and machine learning have allowed researchers to collect and analyze large amounts of data faster and more accurately.

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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.

• 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, 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 – Methods, Types and Analysis

Table of Contents

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

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Researcher, Academic Writer, Web developer

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Home » Business » Quantitative vs Qualitative Business Research

Quantitative vs Qualitative Business Research

• December 25, 2023

Business research is vital for companies of all sizes to make informed decisions, identify growth opportunities, and gain competitive insights. There are two main types of business research – quantitative and qualitative . Understanding the difference between quantitative and qualitative research, including their respective uses, benefits, and limitations, is crucial for business owners to leverage them effectively.

What Is Quantitative Business Research

Quantitative business research focuses on quantifying behaviours, opinions, trends, and other variables by collecting and analysing measurable, numerical data. It answers questions related to “how many”, “how often”, and other statistics to uncover patterns and market trends. This data-driven approach provides hard evidence to support decision-making.

Some examples of quantitative research include:

• Surveys with closed-ended questions, ratings scales, and multiple choice responses that produce numbers and statistics.
• Behaviour tracking provides numerical data like website clicks, foot traffic, or sales revenue over time.
• Analytics presenting site visits, lead conversions, social media followers and other measurable digital metrics.
• Sales data tracking product, regional, seasonal and channel performance.
• Economic models use historical data to forecast future financials.

The defining characteristic of quantitative research is its use of closed-ended questions and numerical data that can be statistically analysed for actionable business insights.

Uses and Benefits of Quantitative Research

Quantitative research is extremely valuable for businesses to:

Track Performance and Trends

Regular quantitative data allows performance tracking over any timeframe to spot positive and negative trends. Monitoring the numbers helps assess if campaigns, operational changes or new offerings are working.

Quantify Customer Behaviour

Understanding sales volume, buying frequency, repeat purchase rates, social engagement and web activity provides measurable behaviour insights businesses use to tailor their customer experience.

Compare Metrics

Comparing indicator metrics before and after changes as well as between customer segments, regions, channels and products is key for data-driven decisions.

Forecast and Set Goals

Current and historical statistics help create financial projections and performance goals with hard targets business leaders can measure against.

Generalise Findings

The structured, statistical nature of quantitative data means results can be generalised to wider markets and audiences. This helps guide decision-making across the whole business.

Quantitative business research produces objective, numerical data that provides concrete evidence regarding customer behaviour and business performance. The statistics it delivers offer powerful support for business strategy and planning.

What Is Qualitative Business Research

While quantitative research focuses on hard stats and numbers, qualitative research collects and evaluates subjective, exploratory data that seeks to understand human behaviours, emotions, attitudes and perceptions. Rather than measuring “how many”, qualitative methods answer questions related to “why” and “how” to provide context and meaning behind the statistics.

Some common qualitative methods include:

• In-depth interviews with open-ended questions for participants to explain perceptions, feelings and interpretations related to products, messaging, services and more.
• Small focus group discussions allow dynamic exchanges around experiences, desires and beliefs to uncover new insights compared to one-on-one interviews.
• Observation studies directly examine how people spontaneously interact with physical spaces, signage, technology, prototypes, etc.
• Case studies explore a single person, organisation or situation in detail through interviews, observation, and other sources to uncover new research angles.
• Ethnographic research gathers data while embedded within a group or culture to gain deeper immersion.

The core element of qualitative research is it collects non-numerical data focused on understanding human perspectives, emotions and behaviours through open and exploratory questioning.

Uses and Benefits of Qualitative Research

While quantitative data shows “what” is happening, qualitative insight explores the all-important “why”.

Key ways businesses leverage qualitative research include:

Gain Empathy

Understanding customer perspectives through qualitative techniques like interviewing and observation builds empathy that drives better products and messaging.

Uncover New Angles

Open-ended qualitative questioning reveals new angles, opportunities and considerations that closed-ended quantitative surveys can overlook.

Context for Statistics

Adding emotional, behavioural and perceptual understanding gained via qualitative research brings powerful context and meaning to quantitative data.

Guide Strategy

Qualitative insights direct strategy and planning by exposing customer beliefs, pain points, desires and behaviours that statistics alone can’t reveal.

Optimise Offerings

Interviews, focus groups and observation studies expose how customers truly think and feel about offerings which optimisation teams use to improve products, services and experiences.

Reduce Risk

Early qualitative research detects flaws in upcoming launches, expansions and pivots that protect against costly assumptions and quantitative post-mortems.

Qualitative business research explores the critical human aspects behind the numbers to drive empathy, expose new opportunities and bring context to quantitative statistics businesses rely on for good reason.

Comparing Quantitative and Qualitative Business Research

While both varieties of business research deliver value, quantitative and qualitative approaches differ considerably.

Some key distinctions include:

Data Collected

• Quantitative – Numerical, structured metrics
• Qualitative – Text-based, exploratory insights

Collection Methods

• Quantitative – Large-scale surveys, analytics, modelling
• Qualitative – Interviews, focus groups, observation

Type of Questions

• Quantitative – Closed-ended
• Qualitative – Open-ended

Data Format

• Quantitative – Stats, graphs, percentages
• Qualitative – Transcripts, notes, audio/video

Sample Size

• Quantitative – Large sample sizes
• Qualitative – Small sample sizes

Type of Analysis

• Quantitative – Statistical analysis
• Qualitative – Thematic coding

Research Objective

• Quantitative – Measure behaviours
• Qualitative – Understand meanings

Key Strength

• Quantitative – Generalisable findings
• Qualitative – Deep emotional insights

Common Weakness

• Quantitative – surface-level explanations
• Qualitative – findings less generalisable

Business Use Cases

• Quantitative – Set targets, track performance, quantify behaviours, forecast
• Qualitative – Guide strategy, build empathy, contextualise stats, reduce risk

In summary:

• Quantitative research statistically measures behaviours and performance with generalisable findings
• Qualitative research uncovers deep emotional insights to understand and improve experiences

Instead of being an “either-or” choice, many businesses leverage both research varieties in tandem to make strategic decisions backed by multidimensional customer and market data.

Who Needs Quantitative vs Qualitative Research?

Virtually all businesses can benefit from both quantitative and qualitative data, though the ideal mix depends on your current business stage and goals:

Early-stage companies often focus on qualitative techniques:

• Interviews to vet ideas
• Observation to optimise prototypes
• Case studies to refine business models

Funding rounds require quantitative data like projecting total addressable market size and benchmarks.

Growing Businesses

Scaling businesses increasingly depend on quantitative metrics around customers, conversions, churn and more while still needing qualitative data to optimise offerings and messaging.

Enterprise Companies

Large companies combine enterprise-wide quantitative performance dashboards with qualitative insights from regionally focused interviews, ethnographies and case studies.

Online Businesses

Digital data like web analytics provides a wealth of quantitative data through qualitative techniques that help strengthen engagement and loyalty.

Brick & Mortar Businesses

In-person brands lean more heavily on qualitative field research while still tracking quantitative POS, inventory and other financial data.

In reality, virtually every business needs both types of data, though the specific use cases and ideal mix vary. Combining empathetic yet data-driven decision-making based on both numbers and emotional insights leads to the strongest market outcomes.

Choosing Quantitative, Qualitative or Both

So when should you adopt quantitative research, qualitative methods or both?

When to Use Quantitative Research

You need quantitative data if aiming to:

• Set performance targets
• Benchmark metrics
• Statistically track behaviours
• Quantify market size or share
• Identify correlating factors
• Model financial projections
• Gather generalisable findings

Quantitative data carries weight across all business functions from marketing to product, finance to sales. If your decisions explicitly depend on numerical evidence and statistical validity, opt for quantitative research.

When To Use Qualitative Research

Seek qualitative insights if looking to:

• Build empathy with customers
• Uncover latent needs
• Guide branding and messaging
• Contextualise behavioural data
• Determine emotional appeal
• Explore new concepts
• Reduce launch risk

Human-centred decisions around experience, engagement and connection should leverage qualitative techniques to go beyond the numbers.

When To Use Both

In practice, combining quantitative and qualitative delivers the most robust insights through:

• Validating emotional insights with statistics
• Adding empathy and meanings to performance numbers
• Learning why metrics are changing
• Inspiring new quantifiable hypotheses
• Confirming findings across methods

For business leaders aiming for reliable yet multidimensional market understanding, adopting both quantitative measurements and qualitative human insights is best.

Conducting Quantitative & Qualitative Research

Once you know which type of research is best for the business decision in question, next comes collecting quality data.

Quantitative Data Collection

Effective quantitative business research requires:

Defining clear hypotheses – Base inquiries on specific, measurable assumptions you can validate or disprove with statistical data.

Using adequate sample sizes – Ensure sample sizes reach minimum thresholds for findings to carry statistical significance when generalised.

Random sampling – Randomly select survey respondents and data sources without biases to achieve representative findings.

Leveraging existing data – First, examine if current data assets offer insights before conducting costly primary research.

Asking closed-ended questions – Craft survey and interview questions using numerical rating scales, rankings and preset response options yielding quantitative data.

Following analysis plans –Outline statistical analysis upfront to ensure data collected answers original hypotheses.

High-quality quantitative research distils metrics into generalisable insights.

Qualitative Data Collection

Skilled qualitative business research requires:

Starting open-minded – Explore topics openly without assumptions blinding you to unexpected insights.

Asking good open-ended questions – Pose questions that elicit long responses and stories, not just yes/no answers.

Probing deeper – Ask follow-up questions and encourage elaboration until reaching a depth of understanding.

Observing natural behaviours – Watch what people do, not just what they say they do.

Capturing context – Note emotions, environmental factors and body language shaping participant responses beyond text transcriptions.

Purposeful sampling – Recruit participants meeting screening criteria for relevant yet diverse perspectives.

Though small in scale, qualitative research uncovers influential emotional and behavioural drivers through rich dialogue and observation.

Data Analysis and Reporting

With quantitative and qualitative data gathered, next comes distilling insights through analysis:

Quantitative Analysis

Common quantitative analysis approaches incorporate:

Statistical analysis – Identify statistically significant survey findings and data trends using computational techniques.

Data visualisation – Transform statistics into digestible charts, graphs and infographics highlighting key takeaways.

Research reports – Synthesise numerical data, visualisations and analysis into presentations, white papers and decks.

Dashboards – Develop interactive dashboards allowing segmented data views for business monitoring.

Predictive modelling – Construct predictive algorithms and machine learning models based on emerging patterns.

Statistical rigour separates quality quantitative analysis driving business growth.

Qualitative Analysis

Core qualitative analysis activities include:

Thematic coding – Systematically tag qualitative data with codes representing recurring themes for aggregation.

Affinity diagramming – Visually cluster insights from interviews and observation into common groups.

Personas and journey mapping – Convert patterns into representative user narratives guiding strategy.

Motivation analysis – Link emotional and behavioural drivers to needs-fulfilling positioning.

Research reports – Compile findings, interview quotes, analyst interpretations and recommendations synthesised into digestible presentations.

Though small in scale, qualitative studies unpack powerful human motivations often missed by quantitative data.

Driving Business Growth with Quantitative and Qualitative Research

Leveraging both quantitative and qualitative research empowers businesses to set strategic goals, optimise performance, reduce risk, spot emerging opportunities, and build stakeholder trust, ultimately driving measurable growth.

Set Clear Targets

Hard quantitative KPIs enable concrete goal setting tied directly to business health across metrics like revenue, customer acquisition costs, churn rate, NPS scores and other vital signs. Dashboards track progress towards targets keeping teams accountable.

Optimise Resource Allocation

Quantifying addressable market size, customer behaviours, and growth opportunities facilitates data-driven resource planning towards maximal ROI on technology, people, marketing and innovation investments.

Substantiate Market Potential

Credible quantitative projections help demonstrate the total available market size and business potential when seeking funding, executive buy-in, or partner support for entering new spaces.

Enhance Offerings

Qualitative feedback provides crucial insights on optimising products, services and overall customer experience by uncovering pain points and delight opportunities that raw quantitative data often overlooks.

Refine Brand Positioning

Surveys, interviews and focus groups assessing emotional resonance to messaging and positioning options allow brands to fine tune both for maximum appeal to target consumer segments.

Reduce Downside Risk

Early quantitative and qualitative market research helps surface potential downsides of new initiatives through data-driven stress testing and identifying unconscious biases that may negatively influence decisions if left unaddressed.

Enable Agility

Ongoing performance benchmarking through quantitative metrics combined with qualitative insights on emerging behaviours provides crucial signals allowing businesses to adapt quickly to market changes.

Build Credibility

Data-backed decisions demonstrating rigorous analysis offer reassurance to sceptical leadership teams, investors and partners, rebuilding confidence in times of uncertainty.

Integrating multidimensional market insights equips business leaders to set their course through choppy waters towards growth targets with greater conviction and buy-in across the organisation. Both numbers and emotions matter.

Quantitative business data refers to measurable, numerical information collected by companies to guide data-driven decision making. This includes metrics like sales figures, web traffic, social followers, customer satisfaction scores and other statistics that can be analysed to uncover patterns, performance trends, behaviours, and opportunities.

Quantitative research methods include surveys, analytics, customer tracking, and financial modelling that produce hardened evidence regarding “how many”, “how often” and other quantifiable attributes of business operations and markets. These statistics help companies set targets, allocate resources, forecast growth, identify areas for improvement, and reduce risks for major decisions or new initiatives.

While qualitative insights around emotions, behaviours and meanings provide crucial context, quantitative data enables firms to objectively track progress towards concrete goals. Business leaders in marketing, product, finance and other departments leverage numerical data to quantify results, guide strategies and optimisations, create projections and substantiate market opportunities. Quality quantitative research collects adequate sample sizes without bias towards generalisable findings backed by statistical significance.

In summary, quantitative business data refers to any measurable statistics companies analyse to monitor outcomes, uncover trends and opportunities, forecast future performance, and derive data-driven decisions supporting growth. Tracking quantifiable metrics provides the tangible evidence executives and investors demand.

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Marketing Research - Quantitative and Qualitative

Last updated 7 Aug 2019

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A useful way of categorising market research is to make a distinction between research that is based on hard data, and research that is based on views and opinions. This is what we mean by quantitative & qualitative research.

QUANTITATIVE RESEARCH

What is quantitative research?

• Concerned with and based on data
• Addresses research questions such as “how many?” “how often”, “who?”, “when?” and “where?”
• Based on larger samples and is, therefore, more statistically valid
• Main methods of obtaining quantitative data are the various forms of survey – i.e. telephone, postal, face-to-face and online

Advantages of quantitative research

• Data relatively easy to analyse
• Numerical data provides insights into relevant trends
• Can be compared with data from other sources (e.g. competitors, history)

Drawbacks of quantitative research

• Focuses on data rather than explaining why things happen
• Doesn’t explain the reasons behind numerical trends
• May lack reliability if sample size and method is not valid

QUALITATIVE RESEARCH

What is qualitative research?

• Based on opinions, attitudes, beliefs and intentions
• Answers research questions such as “Why”? “Would? or “How?”
• Aims to understand why customers behave in a certain way or how they may respond to a new product or service
• Focus groups and interviews are common methods used to collect qualitative data

Advantages of qualitative research

• Essential for important new product development and launches
• Focused on understanding customer needs, wants, expectations = very useful insights for a business
• Can highlight issues that need addressing – e.g. why customers don’t buy
• Effective way of testing elements of the marketing mix – e.g. new branding, promotional campaigns

Drawbacks of qualitative research

• Expensive to collect and analyse – requires specialist research skills
• Based around opinions – always a risk that sample is not representative
• Secondary research
• Quantitative research
• Qualitative research
• Primary research
• Marketing research

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What Is Quantitative Analysis?

• Quantitative vs. Qualitative

Risk Reduction

• Pros and Cons

The Bottom Line

• Quantitative Analysis

Quantitative Analysis: A Simple Overview

Gordon Scott has been an active investor and technical analyst or 20+ years. He is a Chartered Market Technician (CMT).

Quantitative analysis (also known as quant analysis or QA) in finance is an approach that emphasizes mathematical and statistical analysis to help determine the value of a financial asset, such as a stock or option. Quantitative trading analysts (also known as " quants ") use a variety of data to develop trading algorithms and computer models, including historical investment and stock market data.

The information generated by these computer models helps investors analyze investment opportunities and develop what they believe will be a successful trading strategy . Typically, this trading strategy will include very specific information about entry and exit points , the expected risk of the trade, and the expected return.

The ultimate goal of financial quantitative analysis is to use quantifiable statistics and metrics to assist investors in making profitable investment decisions. In this article, we review the history of quantitative investing, compare it to qualitative analysis , and provide an example of a quant-based strategy in action.

Key Takeaways

• Quantitative analysis emerged from the rise of the computer era, which made it easier than ever before to analyze huge amounts of data in short amounts of time.
• Quantitative trading analysts (quants) identify trading patterns, build models to assess those patterns, and make predictions about the price and direction of securities.
• Once the models are built and the information is gathered, quants use the data to set up automated trades of securities.
• Quantitative analysis is different from qualitative analysis, which looks at non-statistical aspects of a company to make predictions.
• Quantitative analysis can be used to mitigate risk by identifying which investments provide the best level of return relative to an investor's preferred level of risk.

Origins of Quant Investing

Nobel Prize-winning economist Harry Markowitz is generally credited with beginning the quantitative investment movement when he published “Portfolio Selection” in the Journal of Finance in March 1952.   Markowitz introduced modern portfolio theory (MPT), which showed investors how to construct a diversified portfolio of assets capable of maximizing returns for various risk levels. Markowitz used math to quantify diversification and is cited as an early adopter of the concept that mathematical models could be applied to investing.

Robert Merton, a pioneer in modern financial theory, won a Nobel Prize for his research into mathematical methods for pricing derivatives .   The work of Markowitz and Merton laid the foundation for the quantitative (quant) approach to investing.

Quantitative vs. Qualitative Analysis

Unlike traditional qualitative investment analysts , quants don’t visit companies, meet the management teams, or research the products the firms sell to identify a competitive edge. They often don’t know or care about the qualitative aspects of the companies they invest in or the products or services these companies provide. Instead, they rely purely on math to make investment decisions.

Quants—who frequently have a scientific background and a degree in statistics or math—will use their knowledge of computers and programming languages to build customized trading systems that automate the trading process. The inputs to their programs might range from key financial ratios (such as the price-to-earnings ratio ) to more complex calculations, such as discounted cash flow (DCF) valuations.

Hedge fund managers embraced the methodology. Advances in computing technology further advanced the field, allowing complex algorithms could be calculated in the blink of an eye and creating automated trading strategies. The field flourished during the dotcom boom and bust .

Quant strategies stumbled in the Great Recession as they failed to account for the impact mortgage-backed securities had on the market and economy as a whole. However, quant strategies remain in use today and have gained notable attention for their role in high-frequency trading (HFT), which relies on math to make trading decisions.

Quantitative investing is also widely practiced both as a stand-alone discipline and in conjunction with traditional qualitative analysis for both return enhancement and risk mitigation.

Quantitative analysts don't look at who manages a company, what its balance sheet looks like, what products it makes, or any other qualitative factor. They focus entirely on the numbers and choose the investment that, mathematically speaking, offers the best return for the lowest level of risk.

Data Used in Quantitative Analysis

The rise of the computer era made it possible to crunch enormous volumes of data in extraordinarily short periods of time. This has led to increasingly complex quantitative trading strategies, as traders seek to identify consistent patterns, model those patterns, and use them to predict price movements in securities.

Quants implement their strategies using publicly available data. The identification of patterns enables them to set up automatic triggers to buy or sell securities.

For example, a trading strategy based on trading volume patterns may have identified a correlation between trading volume and prices. So if the trading volume on a particular stock rises when the stock’s price hits $25 per share and drops when the price hits $30, a quant might set up an automatic buy at $25.50 and an automatic sell at $29.50.

Similar strategies can be based on earnings, earnings forecasts , earnings surprises, and a host of other factors. In each case, pure quant traders don’t care about the company’s sales prospects, management team, product quality, or any other aspect of its business. They are placing their orders to buy and sell based strictly on the numbers accounted for in the patterns they have identified.

Quantitative analysis can be used to identify patterns that may lend themselves to profitable security trades, but that isn’t its only value. While making money is a goal every investor can understand, quantitative analysis can also be used to reduce risk.

The pursuit of so-called “risk-adjusted returns” involves comparing risk measures such as alpha, beta, r-squared, standard deviation, and the Sharpe ratio to identify the investment that will deliver the highest level of return for the given level of risk. The idea is that investors should take no more risk than is necessary to achieve their targeted level of return.

So if the data reveals that two investments are likely to generate similar returns, but that one will be significantly more volatile in terms of up and down price swings, quants (and common sense) would recommend the less risky investment.

Risk-parity portfolios are an example of quant-based strategies in action. The basic concept involves making asset allocation decisions based on market volatility . When volatility declines, the level of risk-taking in the portfolio goes up. When volatility increases, the level of risk-taking in the portfolio goes down .

Example of Quantitative Analysis

To make the example a little more realistic, consider a portfolio that divides its assets between cash and an S&P 500 index fund . Using the Chicago Board Options Exchange Volatility Index ( VIX ) as a proxy for stock market volatility, when volatility rises, our hypothetical portfolio would shift its assets toward cash.

When volatility declines, our portfolio would shift assets to the S&P 500 index fund. Models can be significantly more complex than the one we reference here, perhaps including stocks, bonds, commodities, currencies, and other investments, but the concept remains the same.

Pros and Cons of Quant Trading

Like any trading strategy, quantitative analysis offers both advantages and disadvantages.

• Unemotional : In quant trading, the patterns and numbers are all that matter. It is an effective buy-sell discipline, as it can be executed consistently, unhindered by the emotion that is often associated with financial decisions.
• Cost-effective : Firms that rely on quant strategies don't need to hire large teams of analysts and portfolio managers or travel to assess potential investments. They use computers to analyze the data and execute the trades.

Disadvantages

• Vulnerable to manipulated data : Quant analysis involves culling through vast amounts of data. Choosing the right data is by no means a guarantee, just as trading patterns that appear to suggest certain outcomes may work perfectly until they don’t. Even when a pattern appears to work, validating the patterns can be a challenge.
• Qualitative factors matter : Inflection points , such as the stock market downturn of 2008-09, can be tough on these strategies, as patterns can change suddenly. Humans can see a scandal or management change as it is developing, while a purely mathematical approach cannot necessarily do so.
• Widely used : A strategy becomes less effective as an increasing number of investors attempt to employ it. Patterns that work will become less effective as more and more investors try to profit from them.

What Is Quant Finance?

Quant finance, short for quantitative finance, is using large datasets and mathematical models to analyze patterns in financial markets. It is used by traders to make predictions about how markets will behave, then buy or sell securities based on those predictions.

What Is a Quant?

Quants or quant traders are traders who use quantitative analysis to analyze financial markets and make trading decisions.

What Is the Difference Between Quantitative Analysis and Qualitative Analysis?

Quantitative analysis uses statistical models to make predictions or reach conclusions based solely on things that can be measured. Qualitative analysis makes predictions using subjective, non-numerical data, such as opinions, attitudes, or experiences.

Many investment strategies use a blend of both quantitative and qualitative strategies. They use quant strategies to identify potential investments and then use qualitative analysis to take their research efforts to the next level in identifying the final investment.

They may also use qualitative insight to select investments and quant data for risk management . While both quantitative and qualitative investment strategies have their proponents and their critics, the strategies do not need to be mutually exclusive.

Cowles Foundation for Research in Economics at Yale University. " Portfolio Selection, Efficient Diversification of Investments ."

CFA Institute Research Foundation. " Robert C. Merton and the Science of Finance ," Page 1.

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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

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

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

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

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

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

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eCommerce Customer Research Analyst

Posted 21 May 2024

Burlington, Massachusetts - United States

Req Id 275248

Work Your Magic with us!  Start your next chapter and join MilliporeSigma.

Ready to explore, break barriers, and discover more? We know you’ve got big plans – so do we! Our colleagues across the globe love innovating with science and technology to enrich people’s lives with our solutions in Healthcare, Life Science, and Electronics. Together, we dream big and are passionate about caring for our rich mix of people, customers, patients, and planet. That's why we are always looking for curious minds that see themselves imagining the unimaginable with us.

This role does not offer sponsorship for work authorization. External applicants must be eligible to work in the US. 

The Customer Research Team in the Digital & eCommerce Customer Experience organization is seeking an experienced customer researcher and insights professional with a robust quantitative skillset and experience. In this role, you will partner with stakeholders to identify research needs – both responding to gaps they have identified and anticipating additional areas of research that would be beneficial for the business. Your primary responsibilities will include developing comprehensive and detailed research plans, conducting primary and secondary research with customers and the market, analyzing data for various disparate data sources, and developing reports with cohesive stories. The ideal candidate will be able to go beyond the data to identify and convey actionable insights and recommendations from the research that help meet business goals and objectives and improve the overall experience for our customers. Key accountabilities include:

Quantitative Research and Analysis:

• Identify research needs through conversations with product owners, business partners, and business leads.
• Identify appropriate methodologies to use to ensure that the research output delivers the desired value and impact.
• Assess and utilize the appropriate data sources to develop cohesive and holistic analyses.
• Conduct data modeling for effective data visualization and interpretation.
• Analyze customer feedback data to identify areas for improvement across all touchpoints.
• Recommend data-driven solutions to enhance customer satisfaction and brand loyalty.
• Research and analyze the competitive landscape, identifying market trends, competitor strengths and weaknesses, and potential new market opportunities.

Team Building and Collaboration:

• Work with the team to ensure interconnectedness of insights and identified opportunities, while building off already existing learnings.
• Sharing best practices and identifying opportunities for improving the quant research process for the team.
• Building relationships and working closely with cross functional partners to understand the business needs and partner objectives in detail.
• Guide partners and stakeholders towards the best approach(es) to collect data to help shape the product, CX, and overall digital strategies.

Report Development, Communication, and Presentation:

• Tailor content in reports at differing levels of detail and differing areas of focus for various audiences ranging from product managers to senior executives.
• Develop impactful reports with compelling visuals that convey often complex data and results in an easy-to-understand manner.
• Tell the story for what we learned, why it is important, and how we can improve our CX
• Present learnings to the rest of the Customer Research team as well as business partners and senior business leaders.

Business Acumen and Curiosity:

• Understand business objectives and design studies to discover current and prospective customers’ preferences, needs, and pain points.
• Draft hypotheses to test based on understanding of the business and other research that has been completed.
• Be curious and continue the ask the “why” behind results, especially those that are unexpected.
• Anticipate challenges and gaps in understanding to proactively suggest studies and shape business team roadmaps.

Who you are:

Minimum Qualifications:

• Bachelor’s Degree in Marketing, Economics, Engineering or other business discipline.
• 5+ years of experience in marketing insights & strategy roles.
• 1+ years of experience with survey administration tools (e.g., Qualtrics, Medallia, etc.) and survey data analysis tools (e.g., Q, Marketsight, Jump, SPSS, etc.).

Preferred Qualifications

• Master’s Degree in Marketing or MBA.
• Proficiency with commercial data analysis tools (e.g., Tableau, BI Tools, Palantir, Looker Studio, Big Query, etc.) for data processing, data modelling, and advanced data analysis to handle datasets of 30-100 million+ rows.
• Experience with eCommerce and designing digital experiences.
• Experience developing insights for multinational corporations within an enterprise-level direct to consumer or B2B landscape.
• Experience analyzing Google analytics data to identify trends, conduct funnel analysis, and uncover growth opportunities.
• Experience with detailed and complex quantitative analyses.
• Ability to understand and adapt to different cultures and approaches for different markets / geographies.
• Research agency or consulting background.
• Proven track record of collaborating cross-functionally, educating, and influencing teams to achieve common goals.
• Familiarity with qualitative research techniques.
• Familiarity or experience with product management principles and working with product Researchteams.
• A passion for innovation and a desire to get to the root cause (the why).
• Experience with agile principles and execution.

What we offer: We are curious minds that come from a broad range of backgrounds, perspectives, and life experiences. We celebrate all dimensions of diversity and believe that it drives excellence and innovation, strengthening our ability to lead in science and technology. We are committed to creating access and opportunities for all to develop and grow at your own pace. Join us in building a culture of inclusion and belonging that impacts millions and empowers everyone to work their magic and champion human progress!   Apply now and become a part of our diverse team!

If you would like to know more about what diversity, equity, and inclusion means to us, please visit https://www.emdgroup.com/en/company/press-positions.html

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