how does quantitative research help a business

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?

Last updated

20 February 2023

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

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

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

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

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• Quantitative research methods

Researchers employ various quantitative research methods to determine certain phenomena.

Observation

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

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

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

Experimental

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

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

• What is quantitative analysis?

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

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

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

• What do quantitative analysts do?

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

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

Quantitative analysts are important to staff in any business because:

They manage portfolio risks

They test a new trading strategy

They program and implement a new trading strategy

They improve signals used to evaluate trade ideas

• Understanding quantitative analysis

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

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

Quantitative analysis versus qualitative analysis

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

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

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

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

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

• Advantages of quantitative research

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

It is efficient and fast

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

It is objective and requires limited interpretation

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

It is focused

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

• Disadvantages of quantitative research

It’s only appropriate in certain cases

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

It’s challenging to analyze the data collected

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

• What are the limitations of quantitative research?

Requires vast resources

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

Requires many respondents

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

Research is limited in its scope

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

Why is it called quantitative research?

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

What does quantitative research answer?

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

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

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Home » Quantitative Research – Methods, Types and Analysis

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Business Analytics: What It Is & Why It's Important

• 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

• Improve efficiency and productivity (64 percent)
• Achieve more effective decision-making (56 percent)
• Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

Check out our video on business analytics below, and subscribe to our YouTube channel for more explainer content!

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

• Descriptive : The interpretation of historical data to identify trends and patterns
• Diagnostic : The interpretation of historical data to determine why something has happened
• Predictive : The use of statistics to forecast future outcomes
• Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses, and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

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Your ultimate guide to quantitative research.

12 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 biology, chemistry, 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.

Free Download:  Improve your market research with our Ultimate Guide to Market Research eBook

Qualitative vs quantitative research

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

Quantitative 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:

• Surveys : 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: 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
• Percent 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 methods 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.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis methods and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analysing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualisations quickly:

Find out how Qualtrics XM solution can help improve research team’s quantitative methods

Related resources

Market intelligence tools 10 min read, qualitative research questions 11 min read, primary vs secondary research 14 min read, business research methods 12 min read, ethnographic research 11 min read, business research 10 min read, qualitative research design 12 min read, request demo.

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Importance Of Quantitative Research In Business And Marketing

Statistics reveal that over 88% of marketers believe data-driven strategies are crucial for success, underscoring the pivotal role that quantitative research plays in shaping effective business and marketing strategies. Quantitative research isn’t just a tool; it’s a cornerstone that empowers businesses to make informed choices, identify trends, and gauge customer preferences with precision. In this article, we delve into the compelling reasons why quantitative research stands as a bedrock of modern business and marketing endeavors, exploring its far-reaching impacts and applications.

What is Quantitative Research?

Quantitative research is a systematic and empirical approach to gathering and analyzing numerical data to uncover patterns, relationships, and trends. It involves the use of structured methodologies to collect data that can be quantified and statistically analyzed. By utilizing mathematical and statistical techniques, researchers aim to derive meaningful insights and draw conclusions from the collected data. This method is particularly valuable in providing concrete and measurable information, contributing to evidence-based decision-making in various fields.

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Brief Overview of Its Significance in Business and Marketing

In the dynamic landscape of business and marketing, quantitative research plays a pivotal role in shaping strategies and enhancing decision-making processes. Through quantitative research, businesses can measure consumer behavior, preferences, and attitudes with precision. This data-driven approach enables organizations to understand market trends, evaluate product performance, and assess the effectiveness of marketing campaigns. By quantifying variables and employing statistical analysis, businesses can identify actionable insights that drive growth, optimize operations, and gain a competitive edge in their respective industries.

Comparison with Qualitative Research

While both quantitative and qualitative research are valuable, they differ significantly in their approaches and objectives. Quantitative research focuses on measurable data and statistical analysis, aiming to generalize findings to larger populations. In contrast, qualitative research seeks to delve into the depth and context of individuals’ experiences and opinions through open-ended questions and qualitative data. Quantitative research utilizes closed questions and aims to establish trends and patterns, whereas qualitative research is geared toward understanding the “why” behind behaviors and attitudes.

Key Characteristics and Features

Key characteristics of quantitative research include its reliance on numerical data, statistical analysis, and generalizability. It follows a structured and systematic approach, with a focus on objectivity and replicability. Quantitative research aims to quantify variables, measure relationships, and provide reliable insights that can guide decision-making. By employing established statistical techniques, researchers can draw objective conclusions from the data collected.

Common Quantitative Research Techniques

• Survey Research:  Using structured questionnaires to collect data from a representative sample. Closed-ended questions yield quantifiable responses for statistical analysis.
• Correlational Research:  Investigating the statistical relationships between two or more variables, assessing associations without implying causation.
• Experimental Research:  Manipulating independent variables to establish cause-and-effect relationships, often conducted in controlled settings.
• Cross-Tabulation:  Categorizing and analyzing data based on demographic subgroups to identify trends and patterns.
• Data Cleaning and Analysis:  Preprocessing data to ensure accuracy and remove outliers or errors, followed by statistical analyses such as t-tests or ANOVA to derive meaningful insights.

In summary, quantitative research methodology offers a structured and data-driven approach to exploring phenomena, making it an essential tool for uncovering objective insights and informing decision-making in diverse fields. By comparing and contrasting it with qualitative research, understanding its key characteristics, and exploring common techniques, professionals can harness the power of quantitative research effectively.

I. Market Analysis and Segmentation

Target Audience Identification: Quantitative research plays a pivotal role in market analysis by enabling businesses to precisely identify and understand their target audiences. Through systematic data collection and statistical analysis, organizations can gain insights into demographic attributes, preferences, and behaviors of potential customers. By analyzing numerical data, businesses can identify trends and patterns that help define the characteristics of their ideal customers. This information empowers companies to tailor their marketing strategies, messaging, and product offerings to resonate with the specific needs and preferences of their target audience. For instance, a skincare brand can utilize quantitative research to identify age groups, gender distribution, and income levels of individuals most likely to purchase their products, thereby optimizing their marketing efforts for maximum impact.

Consumer Behavior Patterns: Quantitative research offers a powerful lens through which businesses can analyze consumer behavior patterns. By collecting numerical data on purchasing habits, preferences, and responses to marketing initiatives, companies can uncover valuable insights. For instance, an e-commerce platform can utilize quantitative research to understand which product categories are most popular among different customer segments, helping them allocate resources effectively. By examining shopping cart abandonment rates and conversion metrics, businesses can pinpoint areas for improvement in the online shopping experience and enhance user satisfaction. Moreover, quantitative analysis can reveal the impact of various marketing campaigns on consumer engagement, enabling businesses to fine-tune strategies for maximum ROI.

II. Product Development and Innovation

Test and Refine Product Concepts: Quantitative research serves as a reliable tool for testing and refining product concepts. Businesses can conduct surveys or experiments to gather numerical data on consumer responses to different product prototypes or features. By analyzing quantitative data, organizations can ascertain which attributes resonate most with their target audience. This approach empowers companies to optimize product designs, functionalities, and features based on concrete feedback. For instance, a tech company developing a new smartphone can utilize quantitative research to gather data on user preferences regarding screen size, battery life, camera quality, and other key features, ensuring that the final product aligns with consumer expectations.

Predict Market Acceptance: Quantitative research aids businesses in predicting the potential market acceptance of new products or services. By conducting surveys and analyzing numerical data, companies can gauge consumer interest, willingness to adapt, and purchase intent. This data-driven approach provides insights into the viability and demand for new offerings, helping companies make informed decisions about resource allocation, production, and marketing strategies. For example, an automobile manufacturer planning to introduce an electric vehicle can employ quantitative research to assess potential customers’ attitudes toward electric vehicles, charging infrastructure availability, and price sensitivity.

III. Performance Measurement and Optimization

Evaluate Marketing Campaigns: Quantitative research is a vital tool for evaluating the effectiveness of marketing campaigns. By collecting numerical data on key performance indicators (KPIs) such as click-through rates, conversion rates, and customer engagement metrics, businesses can measure the impact of their marketing efforts. Through statistical analysis, organizations can identify which campaigns resonate most with their target audience and generate the highest returns. This data-driven evaluation enables companies to allocate resources strategically and refine their marketing strategies for optimal outcomes.

Track Customer Satisfaction: Quantitative research enables businesses to systematically track and measure customer satisfaction levels. By employing structured surveys and questionnaires, organizations can gather numerical data on customer experiences, feedback, and perceptions of their products or services. Analyzing this data provides insights into areas of improvement and areas of strength. For instance, a hospitality chain can utilize quantitative research to assess guest satisfaction with accommodation, amenities, and customer service, identifying opportunities to enhance guest experiences and loyalty.

In summation, Quantitative research is an invaluable asset in the realms of business and marketing. Its applications span market analysis, consumer behavior insights, product development, and performance evaluation. By leveraging numerical data and statistical analysis, businesses can make informed decisions, drive innovation, and stay ahead of the curve in today’s competitive landscape.

IV. Advertising and Promotion

Message Effectiveness Assessment: Quantitative research is a valuable tool for assessing the effectiveness of advertising messages. By conducting surveys or experiments, businesses can gather numerical data on how different messages resonate with their target audience. This data-driven approach allows organizations to measure metrics such as recall, comprehension, and emotional impact. Analyzing these metrics enables businesses to determine which messages are most memorable, understandable, and persuasive to their audience. For instance, a cosmetics brand launching a new makeup line can utilize quantitative research to assess which ad copy and visuals generate the highest levels of consumer engagement and message retention.

Media Channel Optimization: Quantitative research plays a crucial role in optimizing media channels for advertising campaigns. By collecting numerical data on consumer preferences, behavior, and media consumption habits, businesses can make informed decisions about where to allocate their advertising budget. Through statistical analysis, organizations can identify which media channels (e.g., television, social media, print) are most effective at reaching their target audience. This information helps businesses maximize their reach and engagement by tailoring their advertising efforts to the platforms preferred by their audience. For example, a technology company launching a new gadget can employ quantitative research to determine whether its target audience is more active on social media or tech-related websites, allowing them to allocate resources accordingly.

Quantitative research empowers businesses to make data-driven decisions in the realms of advertising and promotion. By analyzing numerical data and conducting systematic assessments, organizations can refine their messaging strategies and optimize their media channel choices for maximum impact and ROI.

V. Customer Satisfaction and Feedback

Surveys and Feedback Analysis: Quantitative research is instrumental in assessing customer satisfaction and analyzing feedback. By conducting structured surveys and questionnaires, businesses can collect numerical data that quantifies customers’ opinions and experiences. These surveys can cover various aspects of the customer journey, such as product satisfaction, service quality, and overall experience. The collected data can then be analyzed using statistical techniques to identify trends, patterns, and correlations. This analysis provides valuable insights into areas of strength and areas that require improvement, allowing organizations to make informed decisions based on data-driven feedback. For example, an e-commerce company can utilize quantitative research to gauge customer satisfaction levels after making a purchase and analyze factors that contribute to positive or negative experiences.

Continuous Improvement Initiatives: Quantitative research plays a pivotal role in driving continuous improvement initiatives. By systematically collecting and analyzing numerical data from customer feedback, businesses can identify areas where enhancements are needed. These insights can guide strategic decisions to refine products, services, and processes. Additionally, organizations can use quantitative data to set measurable performance benchmarks and track progress over time. For instance, a hotel chain can implement quantitative research to monitor customer feedback related to cleanliness, staff friendliness, and amenities. By analyzing this data, the chain can identify trends and take proactive steps to enhance guest experiences.

Quantitative research empowers businesses to proactively address customer satisfaction and feedback. Through structured surveys and rigorous analysis, organizations can gain actionable insights that drive continuous improvement efforts, resulting in enhanced customer experiences and increased loyalty.

Advantages of Quantitative Research

I. Objectivity and Reliability

Quantitative research is characterized by its objectivity and reliability. By relying on numerical data and statistical methods, researchers can minimize the impact of personal biases and subjectivity on the results. The structured nature of quantitative research ensures consistency in data collection and analysis, leading to reliable findings. Objective measurements and standardized procedures contribute to the credibility of the research outcomes, making them more trustworthy for decision-making.

II. Data-Driven Decision Making

One of the significant advantages of quantitative research is its ability to facilitate data-driven decision-making. The empirical approach of gathering and analyzing numerical data allows organizations to base their decisions on concrete evidence rather than speculation or intuition. Businesses can make informed choices about product development, marketing strategies, customer satisfaction initiatives, and more by relying on the insights derived from quantitative research.

III. Scalability and Generalizability

Quantitative research offers the advantage of scalability and generalizability. With a representative sample and rigorous research design, findings from quantitative studies can be extended to larger populations or broader groups. This capability to draw insights about a larger segment of the population is invaluable for businesses seeking to understand customer preferences, market trends, and behaviors on a larger scale.

IV. Statistical Analysis for Insights

Quantitative research employs sophisticated statistical analysis techniques to extract insights from data. These statistical methods allow researchers to uncover patterns, relationships, and associations that might not be immediately apparent. Whether it’s identifying correlations between variables, testing hypotheses, or detecting trends over time, statistical analysis enhances the depth and breadth of insights obtained from quantitative research.

V. Long-Term Trend Identification

Another advantage of quantitative research is its potential for identifying long-term trends. By collecting numerical data over extended periods, researchers can detect patterns and changes that unfold gradually. This longitudinal perspective enables businesses to adapt to evolving market conditions, track shifts in consumer behavior, and make strategic adjustments over time.

Incorporating quantitative research into your strategy equips you with objective, reliable insights that inform decision-making, foster understanding of broad trends, and drive data-driven initiatives. While quantitative research is instrumental in uncovering facts and trends, it’s essential to complement it with qualitative methods to explore the underlying “why” and gain a comprehensive understanding of complex phenomena.

Steps in Conducting Quantitative Research

1. Problem Formulation and Research Questions

The first step in conducting quantitative research is to clearly define the problem or research question you want to address. This involves identifying a specific topic or issue that you want to investigate using quantitative methods. The research question should be focused, clear, and relevant to the field of study.

2. Hypothesis Development

Once the research question is defined, formulate one or more hypotheses that provide a clear statement of the expected relationship between variables. Hypotheses guide the research process by outlining the expected outcomes that will be tested and analyzed during the study.

3. Sampling Strategy and Data Collection

Selecting a representative sample from the target population is a crucial step. The sampling strategy determines how participants will be chosen to ensure that the findings can be generalized to the broader population. The sample size and sampling method (random sampling, stratified sampling, etc.) should be carefully considered to minimize bias and enhance the study’s external validity. After selecting the sample, data is collected using structured instruments such as surveys, questionnaires, or experiments.

IV. Data Analysis Techniques

Data analysis involves applying appropriate statistical techniques to the collected data. The choice of analysis methods depends on the research questions, hypotheses, and types of data collected. Common data analysis techniques include descriptive statistics (mean, median, mode), inferential statistics (t-tests, ANOVA), regression analysis, and correlation analysis. The goal is to uncover patterns, relationships, and associations in the data.

V. Interpretation of Results

Interpreting the results involves making sense of the data analysis in relation to the research question and hypotheses. Researchers examine statistical outputs and draw conclusions about whether the hypotheses were supported or rejected based on the data. It’s important to discuss the implications of the findings, the significance of the relationships observed, and any limitations of the study.

Throughout the steps, researchers need to adhere to ethical guidelines, ensure data privacy and confidentiality, and maintain the rigor of the research design. The interpretation of results should consider the broader context of the field of study and contribute to the body of knowledge in that area.

Conducting quantitative research requires careful planning, methodological expertise, and a systematic approach to ensure that the study is scientifically sound and the results are reliable and valid. Integrating qualitative insights and findings from other research methods can provide a comprehensive understanding of complex phenomena and contribute to well-rounded conclusions.

Challenges and Considerations

I. Sample Size and Representativeness

One of the challenges in quantitative research is determining an appropriate sample size that accurately represents the population of interest. A small sample size might not provide reliable results, while an excessively large one can be resource-intensive and unnecessary. Achieving a balance between sample size and representativeness is crucial to ensure the findings can be generalized to the broader population.

II. Survey Design and Questionnaire Construction

Designing effective surveys and constructing well-structured questionnaires is a critical consideration. Poorly designed surveys can lead to biased responses, inaccurate data, and difficulty in data analysis. Researchers must carefully craft questions, ensure clarity, avoid leading or loaded questions, and consider the order and format of questions to obtain reliable and valid data.

III. Data Analysis Complexity

Quantitative research often involves complex data analysis techniques, especially when dealing with a large number of variables or intricate statistical models. Researchers may encounter challenges in selecting the appropriate statistical methods, interpreting results accurately, and handling missing or skewed data. Proper training and expertise in statistical analysis are essential to ensure accurate interpretations.

IV. Addressing Potential Biases

Despite the objective nature of quantitative research, biases can still influence the research process. Selection bias, response bias, and non-response bias are examples of biases that can distort results. Researchers need to implement strategies to minimize biases, such as random sampling, ensuring diverse participation, and analyzing non-response patterns.

V. Ethical Considerations and Data Privacy

Ethical considerations are paramount in quantitative research. Researchers must obtain informed consent from participants, protect their privacy, and adhere to ethical guidelines. Additionally, with increasing concerns about data privacy, ensuring that collected data is stored securely and used only for its intended purpose is essential to maintain trust and compliance with regulations.

Navigating these challenges and considerations is essential to conduct robust and credible quantitative research. Proper planning, careful methodology design, expert statistical analysis, and ethical awareness contribute to the reliability and validity of research outcomes. Integrating quantitative research with other research methods, such as qualitative research, can provide a more comprehensive understanding of complex phenomena and enhance the overall quality of insights.

Case Studies

A. real-world examples showcasing successful applications.

• Market Segmentation and Targeting: A retail company uses quantitative research to segment its customer base by demographics, buying behavior, and preferences. This allowed them to tailor their marketing strategies to different segments and enhance customer engagement.
• Product Development: An electronics manufacturer conducted quantitative research to understand consumer preferences for features in a new smartphone. By analyzing the data, they identified the most desired features and integrated them into the final product design.
• Advertising Effectiveness: An advertising agency utilized quantitative research to measure the impact of an advertising campaign on brand awareness and consumer attitudes. The insights gained helped the agency fine-tune future campaigns for better results.

B. Highlighting the Role of Quantitative Research in Decision Making

• Evidence-Based Decision Making: A pharmaceutical company used quantitative research to evaluate the effectiveness of a new medication. By conducting clinical trials and analyzing data, they were able to provide evidence to support the medication’s efficacy and safety, leading to informed regulatory decisions.
• Market Entry Strategy: An international food chain uses quantitative research to assess the potential success of entering a new market. They collected data on consumer preferences, competition, and economic indicators to make data-driven decisions on market entry timing and locations.
• Customer Satisfaction Enhancement: A hotel chain employed quantitative research to measure customer satisfaction levels and identify areas for improvement. By analyzing guest feedback and ratings, they implemented changes that led to increased guest satisfaction and loyalty.

Quantitative research plays a crucial role in providing actionable insights that inform strategic decisions across industries. By collecting and analyzing numerical data, organizations can make informed choices that lead to improved products, services, and customer experiences. It allows businesses to measure the impact of their actions and optimize strategies for better outcomes.

Future Trends and Innovations

1. integration of big data and machine learning.

The future of quantitative research is closely tied to the integration of big data and machine learning technologies. As data continues to grow exponentially, researchers are exploring ways to harness this wealth of information to gain deeper insights. Big data analytics allows researchers to process and analyze massive datasets, revealing patterns and trends that were previously difficult to uncover. Machine learning algorithms, on the other hand, can identify complex relationships within the data, making predictions and recommendations based on historical patterns.

Researchers will increasingly rely on big data and machine learning to:

• Identify subtle correlations and trends across vast datasets.
• Predict consumer behavior and preferences with higher accuracy.
• Personalize marketing strategies and product recommendations .
• Enhance decision-making by analyzing a wider range of variables.

2. Automation of Data Collection and Analysis

The automation of data collection and analysis is another significant trend in quantitative research. Advances in technology, such as online surveys, mobile apps, and IoT devices, enable researchers to collect data more efficiently and in real-time. Automated data analysis tools and software can quickly process and interpret data, reducing the time and effort required for manual analysis.

Key benefits of automation in quantitative research include:

• Faster data collection and analysis, leading to quicker insights.
• Reduced human error in data entry and analysis processes.
• Improved scalability for large-scale studies.
• Enhanced agility in adapting research strategies based on real-time data.

3. Cross-Disciplinary Collaboration

Quantitative research is becoming increasingly interdisciplinary, with researchers from various fields collaborating to tackle complex problems. Cross-disciplinary collaboration allows for a broader perspective on research questions and the application of diverse methodologies. For instance, economists, psychologists, and sociologists might work together to study consumer behavior and its economic impact.

Benefits of cross-disciplinary collaboration include:

• Incorporating insights from multiple disciplines to gain comprehensive insights.
• Leveraging complementary expertise to address multifaceted research questions.
• Fostering innovation by bringing together diverse perspectives and methodologies.

In conclusion, the future of quantitative research is marked by the integration of big data and machine learning, the automation of data collection and analysis, and increased cross-disciplinary collaboration. These trends will enhance the accuracy, efficiency, and scope of quantitative research, enabling researchers to extract deeper insights and make informed decisions across various domains. At Kadence, we are committed to staying at the forefront of these innovations to help you achieve your research objectives effectively.

Quantitative research stands as a vital pillar in the realm of market research. Its reliance on hard facts, numerical data, and statistical analysis empowers researchers to obtain an objective and comprehensive understanding of people’s opinions and behaviors. By employing structured instruments like surveys and experiments, quantitative research generates reliable insights into social phenomena and allows for predictions, comparisons, and generalizations based on concrete numerical data.

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Home » Quantitative Research: Definition, Methods, and Examples

Quantitative Research: Definition, Methods, and Examples

June 13, 2023 max 8min read.

This article covers:

What Is Quantitative Research?

Quantitative research methods .

• Data Collection and Analysis

Types of Quantitative Research

• Advantages and Disadvantages of Quantitative Research

Examples of Quantitative Research

Picture this: you’re a product or project manager and must make a crucial decision. You need data-driven insights to guide your choices, understand customer preferences, and predict market trends. That’s where quantitative research comes into play. It’s like having a secret weapon that empowers you to make informed decisions confidently.

Quantitative research is all about numbers, statistics, and measurable data. It’s a systematic approach that allows you to gather and analyze numerical information to uncover patterns, trends, and correlations. 

Quantitative research provides concrete, objective data to drive your strategies, whether conducting surveys, analyzing large datasets, or crunching numbers.

In this article, we’ll dive and learn all about quantitative research; get ready to uncover the power of numbers.

Quantitative Research Definition:

Quantitative research is a systematic and objective approach to collecting, analyzing, and interpreting numerical data. It measures and quantifies variables, employing statistical methods to uncover patterns, relationships, and trends.

Quantitative research gets utilized across a wide range of fields, including market research, social sciences, psychology, economics, and healthcare. It follows a structured methodology that uses standardized instruments, such as surveys, experiments, or polls, to collect data. This data is then analyzed using statistical techniques to uncover patterns and relationships.

The purpose of quantitative research is to measure and quantify variables, assess the connections between variables, and draw objective and generalizable conclusions. Its benefits are numerous:

• Rigorous and scientific approach : Quantitative research provides a comprehensive and scientific approach to studying phenomena. It enables researchers to gather empirical evidence and draw reliable conclusions based on solid data.
• Evidence-based decision-making : By utilizing quantitative research, researchers can make evidence-based decisions. It helps in developing informed strategies and evaluating the effectiveness of interventions or policies by relying on data-driven insights.
• Advancement of knowledge : Quantitative research contributes to the advancement of knowledge by building upon existing theories. It expands understanding in various fields and informs future research directions, allowing for continued growth and development.

Here are various quantitative research methods:

Survey research : This method involves collecting data from a sample of individuals through questionnaires, interviews, or online surveys. Surveys gather information about people’s attitudes, opinions, behaviors, and characteristics.

Experimentation: It is a research method that allows researchers to determine cause-and-effect relationships. In an experiment, participants randomly get assigned to different groups. While the other group does not receive treatment or intervention, one group does. The outcomes of the two groups then get measured to analyze the effects of the treatment or intervention.

Here are the steps involved in an experiment:

• Define the research question. What do you want to learn about?
• Develop a hypothesis. What do you think the answer to your research question is?
• Design the experiment. How will you manipulate the variables and measure the outcomes?
• Recruit participants. Who will you study?
• Randomly assign participants to groups. This ensures that the groups are as similar as possible.
• Apply the treatments or interventions. This is what the researcher is attempting to test the effects of.
• Measure the outcomes. This is how the researcher will determine whether the treatments or interventions had any effect.
• Analyze the data. This is how the researcher will determine whether the results support the hypothesis.
• Draw conclusions. What do the results mean?
• Content analysis : Content analysis is a systematic approach to analyzing written, verbal, or visual communication. Researchers identify and categorize specific content, themes, or patterns in various forms of media, such as books, articles, speeches, or social media posts.
• Secondary data analysis : It is a research method that involves analyzing data already collected by someone else. This data can be from various sources, such as government reports, previous research studies, or large datasets like surveys or medical records. 

Researchers use secondary data analysis to answer new research questions or gain additional insights into a topic.

Data Collection and Analysis for Quantitative Research

Quantitative research is research that uses numbers and statistics to answer questions. It often measures things like attitudes, behaviors, and opinions.

There are three main methods for collecting quantitative data:

• Surveys and questionnaires: These are structured instruments used to gather data from a sample of people.
• Experiments and controlled observations: These are conducted in a controlled setting to measure variables and determine cause-and-effect relationships.
• Existing data sources (secondary data): This data gets collected from databases, archives, or previous studies.

Data preprocessing and cleaning is the first step in data analysis. It involves identifying and correcting errors, removing outliers, and ensuring the data is consistent.

Descriptive statistics is a branch of statistics that deals with the description of the data. It summarizes and describes the data using central tendency, variability, and shape measures.

Inferential statistics again comes under statistics which deals with the inference of properties of a population from a sample. It tests hypotheses, estimates parameters, and makes predictions.

Here are some of the most common inferential statistical techniques:

• Hypothesis testing : This assesses the significance of relationships or differences between variables.
• Confidence intervals : This estimates the range within which population parameters likely fall.
• Correlation and regression analysis : This examines relationships and predicts outcomes based on variables.
• Analysis of variance (ANOVA) : This compare means across multiple groups or conditions.

Statistical software and tools for data analysis can perform complex statistical analyses efficiently. Some of the most popular statistical software packages include SPSS, SAS, and R.

Here are some of the main types of quantitative research methodology:

• Descriptive research describes a particular population’s characteristics, trends, or behaviors. For example, a descriptive study might look at the average height of students in a school, the number of people who voted in an election, or the types of food people eat.
• Correlational research checks the relationship between two or more variables. For example, a correlational study might examine the relationship between income and happiness or stress and weight gain. Correlational research can show that two variables are related but cannot show that one variable causes the other.
• Experimental research is a type of research that investigates cause-and-effect relationships. In an experiment, researchers manipulate one variable (the independent variable) and measure the impact on another variable (the dependent variable). This allows researchers to make inferences about the relationship between the two variables.
• Quasi-experimental research is similar to experimental research. However, it does not involve random assignment of participants to groups. This can be due to practical or ethical considerations, such as when assigning people to receive a new medication randomly is impossible. In quasi-experimental research, researchers try to control for other factors affecting the results, such as the participant’s age, gender, or health status.
• Longitudinal research studies change patterns over an extended time. For example, a longitudinal study might examine how children’s reading skills develop over a few years or how people’s attitudes change as they age. But longitudinal research can be expensive and time-consuming. Still, it can offer valuable insights into how people and things change over time.

 Advantages and Disadvantages of Quantitative Research

Here are the advantages and downsides of quantitative research:

Advantages of Quantitative Research:

• Objectivity: Quantitative research aims to be objective and unbiased. This is because it relies on numbers and statistical methods, which reduce the potential for researcher bias and subjective interpretation.
• Generalizability: Quantitative research often involves large sample sizes, which increases the likelihood of obtaining representative data. The study findings are more likely to apply to a wider population.
• Replicability: Using standardized procedures and measurement instruments in quantitative research enhances replicability. This means that other researchers can repeat the study using the same methods to test the reliability of the findings.
• Statistical analysis: Quantitative research employs various statistical techniques for data analysis. This allows researchers to identify data patterns, relationships, and associations. Additionally, statistical analysis can provide precision and help draw objective conclusions.
• Numerical precision: Quantitative research produces numerical data that can be analyzed using mathematical calculations. This numeric precision allows for clear comparisons and quantitative interpretations.

Disadvantages of Quantitative Research :

• Lack of Contextual Understanding : Quantitative research often focuses on measurable variables, which may limit the exploration of complex phenomena. It may overlook the social, cultural, and contextual factors that could influence the research findings.
• Limited Insight : While quantitative research can identify correlations and associations, it may not uncover underlying causes or explanations of these relationships. It may provide answers to “what” and “how much,” but not necessarily “why.”
• Potential for Simplification : The quantification of data can lead to oversimplification, as it may reduce complex phenomena into numerical values. This simplification may overlook nuances and intricacies important to understanding the research topic fully.
• Cost and Time-Intensive : Quantitative research requires significant resources. It includes time, funding, and specialized expertise. Researchers must collect and analyze large amounts of numerical data, which can be lengthy and expensive.
• Limited Flexibility : A systematic and planned strategy typically gets employed in quantitative research. It signifies the researcher’s use of a predetermined data collection and analysis approach. As a result, you may be more confident that your study gets conducted consistently and equitably. But it may also make it more difficult for the researcher to change the research plan or pose additional inquiries while gathering data. This could lead to missing valuable insights.

Here are some real-life examples of quantitative research:

• Market Research : Quantitative market research is a type of market research that uses numerical data to understand consumer preferences, buying behavior, and market trends. This data typically gets gathered through surveys and questionnaires, which are then analyzed to make informed business decisions.
• Health Studies : Quantitative research, such as clinical trials and epidemiological research, is vital in health studies. Researchers collect numerical data on treatment effectiveness, disease prevalence, risk factors, and patient outcomes. This data is then analyzed statistically to draw conclusions and make evidence-based recommendations for healthcare practices.
• Educational Research : Quantitative research is used extensively in educational studies to examine various aspects of learning, teaching methods, and academic achievement. Researchers collect data through standardized tests, surveys, or observations. The reason for this approach is to analyze factors influencing student performance, educational interventions, and educational policy effectiveness.
• Social Science Surveys : Social science researchers often employ quantitative research methods. The aim here is to study social phenomena and gather data on individuals’ or groups’ attitudes, beliefs, and behaviors. Large-scale surveys collect numerical data, then statistically analyze to identify patterns, trends, and associations within the population.
• Opinion Polls : Opinion polls and public opinion research rely heavily on quantitative research techniques. Polling organizations conduct surveys with representative samples of the population. The companies do this intending to gather numerical data on public opinions, political preferences, and social attitudes. The data then gets analyzed to gauge public sentiment and predict election outcomes or public opinion on specific issues.
• Economic Research : Quantitative research is widely used in economic studies to analyze economic indicators, trends, and patterns. Economists collect numerical data on GDP, inflation, employment, and consumer spending. Statistical analysis of this data helps understand economic phenomena, forecast future trends, and inform economic policy decisions.

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Qualitative research is about understanding and exploring something in depth. It uses non-numerical data, like interviews, observations, and open-ended survey responses, to gather rich, descriptive insights. Quantitative research is about measuring and analyzing relationships between variables using numerical data.

Quantitative research gets characterized by the following:

• The collection of numerical information
• The use of statistical analysis
• The goal of measuring and quantifying phenomena
• The purpose of examining relationships between variables
• The purpose of generalizing findings to a larger population
• The use of large sample sizes
• The use of structured surveys or experiments
• The usage of statistical techniques to analyze data objectively

The primary goal of quantitative research is to gather numerical data and analyze it statistically to uncover patterns, relationships, and trends. It aims to provide objective and generalizable insights using systematic data collection methods, standardized instruments, and statistical analysis techniques. Quantitative research seeks to test hypotheses, make predictions, and inform decision-making in various fields.

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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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What is Quantitative Data?

Data professionals work with two types of data: quantitative and qualitative. What is quantitative data? What is qualitative data? In simple terms, quantitative data is measurable while qualitative data is descriptive—think numbers versus words.

If you plan on working as a data analyst or a data scientist (or in any field that involves conducting research, like psychology), you’ll need to get to grips with both. In this post, we’ll focus on quantitative data. We’ll explain exactly what quantitative data is, including plenty of useful examples. We’ll also show you what methods you can use to collect and analyze quantitative data.

By the end of this post, you’ll have a clear understanding of quantitative data and how it’s used.

We’ll cover:

• What is quantitative data? (Definition)
• What are some examples of quantitative data?
• What’s the difference between quantitative and qualitative data?
• What are the different types of quantitative data?
• How is quantitative data collected?
• What methods are used to analyze quantitative data?
• What are the advantages and disadvantages of quantitative data?
• Should I use quantitative or qualitative data in my research?
• What are some common quantitative data analysis tools?
• What is quantitative data? FAQs
• Key takeaways

So: what is quantitative data? Let’s find out.

1. What is quantitative data? (Definition)

Quantitative data is, quite simply, information that can be quantified. It can be counted or measured, and given a numerical value—such as length in centimeters or revenue in dollars. Quantitative data tends to be structured in nature and is suitable for statistical analysis. If you have questions such as “How many?”, “How often?” or “How much?”, you’ll find the answers in quantitative data.

2. What are some examples of quantitative data?

Some examples of quantitative data include:

• Revenue in dollars
• Weight in kilograms
• Age in months or years
• Length in centimeters
• Distance in kilometers
• Height in feet or inches
• Number of weeks in a year

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

It’s hard to define quantitative data without comparing it to qualitative data—so what’s the difference between the two?

While quantitative data can be counted and measured, qualitative data is descriptive and, typically, unstructured. It usually takes the form of words and text—for example, a status posted on Facebook or an interview transcript are both forms of qualitative data. You can also think of qualitative data in terms of the “descriptors” you would use to describe certain attributes. For example, if you were to describe someone’s hair color as auburn, or an ice cream flavor as vanilla, these labels count as qualitative data.

Qualitative data cannot be used for statistical analysis; to make sense of such data, researchers and analysts will instead try to identify meaningful groups and themes.

You’ll find a detailed exploration of the differences between qualitative and quantitative data in this post . But, to summarize:

• Quantitative data is countable or measurable, relating to numbers; qualitative data is descriptive, relating to words.
• Quantitative data lends itself to statistical analysis; qualitative data is grouped and categorized according to themes.
• Examples of quantitative data include numerical values such as measurements, cost, and weight; examples of qualitative data include descriptions (or labels) of certain attributes, such as “brown eyes” or “vanilla flavored ice cream”.

Now we know the difference between the two, let’s get back to quantitative data.

4. What are the different types of quantitative data?

There are two main types of quantitative data: discrete and continuous .

Discrete data

Discrete data is quantitative data that can only take on certain numerical values. These values are fixed and cannot be broken down. When you count something, you get discrete data. For example, if a person has three children, this is an example of discrete data. The number of children is fixed—it’s not possible for them to have, say, 3.2 children.

Another example of discrete quantitative data could be the number of visits to your website; you could have 150 visits in one day, but not 150.6 visits. Discrete data is usually visualized using tally charts, bar charts, and pie charts.

Continuous data

Continuous data, on the other hand, can be infinitely broken down into smaller parts. This type of quantitative data can be placed on a measurement scale; for example, the length of a piece of string in centimeters, or the temperature in degrees Celsius. Essentially, continuous data can take any value; it’s not limited to fixed values. What’s more, continuous data can also fluctuate over time—the room temperature will vary throughout the day, for example. Continuous data is usually represented using a line graph.

Continuous data can be further classified depending on whether it’s interval data or ratio data . Let’s take a look at those now.

Interval vs. ratio data

Interval data can be measured along a continuum, where there is an equal distance between each point on the scale. For example: The difference between 30 and 31 degrees C is equal to the difference between 99 and 100 degrees. Another thing to bear in mind is that interval data has no true or meaningful zero value . Temperature is a good example; a temperature of zero degrees does not mean that there is “no temperature”—it just means that it’s extremely cold!

Ratio data is the same as interval data in terms of equally spaced points on a scale, but unlike interval data, ratio data does have a true zero . Weight in grams would be classified as ratio data; the difference between 20 grams and 21 grams is equal to the difference between 8 and 9 grams, and if something weighs zero grams, it truly weighs nothing.

Beyond the distinction between discrete and continuous data, quantitative data can also be broken down into several different types:

• Measurements: This type of data refers to the measurement of physical objects. For example, you might measure the length and width of your living room before ordering new sofas.
• Sensors: A sensor is a device or system which detects changes in the surrounding environment and sends this information to another electronic device, usually a computer. This information is then converted into numbers—that’s your quantitative data. For example, a smart temperature sensor will provide you with a stream of data about the temperature of the room throughout the day.
• Counts: As the name suggests, this is the quantitative data you get when you count things. You might count the number of people who attended an event, or the number of visits to your website in one week.
• Quantification of qualitative data: This is when qualitative data is converted into numbers. Take the example of customer satisfaction. If a customer said “I’m really happy with this product”, that would count as qualitative data. You could turn this into quantitative data by asking them to rate their satisfaction on a scale of 1-10.
• Calculations: This is any quantitative data that results from mathematical calculations, such as calculating your final profit at the end of the month.
• Projections: Analysts may estimate or predict quantities using algorithms, artificial intelligence, or “manual” analysis. For example, you might predict how many sales you expect to make in the next quarter. The figure you come up with is a projection of quantitative data.

Knowing what type of quantitative data you’re working with helps you to apply the correct type of statistical analysis. We’ll look at how quantitative data is analyzed in section five.

5. How is quantitative data collected?

Now we know what quantitative data is, we can start to think about how analysts actually work with it in the real world. Before the data can be analyzed, it first needs to be generated or collected. So how is this done?

Researchers (for example, psychologists or scientists) will often conduct experiments and studies in order to gather quantitative data and test certain hypotheses. A psychologist investigating the relationship between social media usage and self-esteem might devise a questionnaire with various scales—for example, asking participants to rate, on a scale of one to five, the extent to which they agree with certain statements.

If the survey reaches enough people, the psychologist ends up with a large sample of quantitative data (for example, an overall self-esteem score for each participant) which they can then analyze.

Data analysts and data scientists are less likely to conduct experiments, but they may send out questionnaires and surveys—it all depends on the sector they’re working in. Usually, data professionals will work with “naturally occurring” quantitative data, such as the number of sales per quarter, or how often a customer uses a particular service.

Some common methods of data collection include:

• Analytics tools, such as Google Analytics
• Probability sampling

Questionnaires and surveys

• Open-source datasets on the web

Analytics tools

Data analysts and data scientists rely on specialist tools to gather quantitative data from various sources. Google Analytics, for example, will gather data pertaining to your website; at a glance, you can see metrics such as how much traffic you got in one week, how many page views per minute, and average session length—all useful insights if you want to optimize the performance of your site.

Aside from Google Analytics, which tends to be used within the marketing sector, there are loads of tools out there which can be connected to multiple data sources at once. Tools like RapidMiner, Knime, Qlik, and Splunk can be integrated with internal databases, data lakes, cloud storage, business apps, social media, and IoT devices, allowing you to access data from multiple sources all in one place.

You can learn more about the top tools used by data analysts in this guide

Sampling is when, instead of analyzing an entire dataset, you select a sample or “section” of the data. Sampling may be used to save time and money, and in cases where it’s simply not possible to study an entire population. For example, if you wanted to analyze data pertaining to the residents of New York, it’s unlikely that you’d be able to get hold of data for every single person in the state. Instead, you’d analyze a representative sample.

There are two types of sampling: Random probability sampling, where each unit within the overall dataset has the same chance of being selected (i.e. included in the sample), and non-probability sampling, where the sample is actively selected by the researcher or analyst—not at random. Data analysts and scientists may use Python (the popular programming language) and various algorithms to extract samples from large datasets.

Another way to collect quantitative data is through questionnaires and surveys. Nowadays, it’s easy to create a survey and distribute it online—with tools like Typeform , SurveyMonkey , and Qualtrics , practically anyone can collect quantitative data. Surveys are a useful tool for gathering customer or user feedback, and generally finding out how people feel about certain products or services.

To make sure you gather quantitative data from your surveys, it’s important that you ask respondents to quantify their feelings—for example, asking them to rate their satisfaction on a scale of one to ten.

Open-source datasets online

In addition to analyzing data from internal databases, data analysts might also collect quantitative data from external sources. Again, it all depends on the field you’re working in and what kind of data you need. The internet is full of free and open datasets spanning a range of sectors, from government, business and finance, to science, transport, film, and entertainment—pretty much anything you can think of! We’ve put together a list of places where you can find free datasets here .

6. How is quantitative data analyzed?

A defining characteristic of quantitative data is that it’s suitable for statistical analysis. There are many different methods and techniques used for quantitative data analysis, and how you analyze your data depends on what you hope to find out.

Before we go into some specific methods of analysis, it’s important to distinguish between descriptive and inferential analysis .

What’s the difference between descriptive and inferential analysis of quantitative data?

Descriptive analysis does exactly what it says on the tin; it describes the data. This is useful as it allows you to see, at a glance, what the basic qualities of your data are and what you’re working with. Some commonly used descriptive statistics include the range (the difference between the highest and lowest scores), the minimum and maximum (the lowest and highest scores in a dataset), and frequency (how often a certain value appears in the dataset).

You might also calculate various measures of central tendency in order to gauge the general trend of your data. Measures of central tendency include the mean (the sum of all values divided by the number of values, otherwise known as the average), the median (the middle score when all scores are ordered numerically), and the mode (the most frequently occurring score). Another useful calculation is standard deviation . This tells you how representative of the entire dataset the mean value actually is.

While descriptive statistics give you an initial read on your quantitative data, they don’t allow you to draw definitive conclusions. That’s where inferential analysis comes in. With inferential statistics, you can make inferences and predictions. This allows you to test various hypotheses and to predict future outcomes based on probability theory.

Quantitative data analysis methods

When it comes to deriving insights from your quantitative data, there’s a whole host of techniques at your disposal. Some of the most common (and useful) methods of quantitative data analysis include:

• Regression analysis: This is used to estimate the relationship between a set of variables, and to see if there’s any kind of correlation between the two. Regression is especially useful for making predictions and forecasting future trends.
• Monte Carlo simulation : The Monte Carlo method is a computerized technique used to generate models of possible outcomes and their probability distributions based on your dataset. It essentially considers a range of possible outcomes and then calculates how likely it is that each particular outcome will occur. It’s used by data analysts to conduct advanced risk analysis, allowing them to accurately predict what might happen in the future.
• Cohort analysis: A cohort is a group of people who share a common attribute or behavior during a given time period—for example, a cohort of students who all started university in 2020, or a cohort of customers who purchased via your app in the month of February. Cohort analysis essentially divides your dataset into cohorts and analyzes how these cohorts behave over time. This is especially useful for identifying patterns in customer behavior and tailoring your products and services accordingly.
• Cluster analysis : This is an exploratory technique used to identify structures within a dataset. The aim of cluster analysis is to sort different data points into groups that are internally homogenous and externally heterogeneous—in other words, data points within a cluster are similar to each other, but dissimilar to data points in other clusters. Clustering is used to see how data is distributed in a given dataset, or as a preprocessing step for other algorithms.
• Time series analysis : This is used to identify trends and cycles over time. Time series data is a sequence of data points which measure the same variable at different points in time, such as weekly sales figures or monthly email sign-ups. By looking at time-related trends, analysts can forecast how the variable of interest may fluctuate in the future. Extremely handy when it comes to making business decisions!

Above is just a very brief introduction to how you might analyze your quantitative data. For a more in-depth look, check out this comprehensive guide to some of the most useful data analysis techniques .

7. What are the advantages and disadvantages of quantitative data?

As with anything, there are both advantages and disadvantages of using quantitative data. So what are they? Let’s take a look.

Advantages of quantitative data

The main advantages of working with quantitative data are as follows:

• Quantitative data is relatively quick and easy to collect , allowing you to gather a large sample size. And, the larger your sample size, the more accurate your conclusions are likely to be.
• Quantitative data is less susceptible to bias. The use of random sampling helps to ensure that a given dataset is as representative as possible, and protects the sample from bias. This is crucial for drawing reliable conclusions.
• Quantitative data is analyzed objectively. Because quantitative data is suitable for statistical analysis, it can be analyzed according to mathematical rules and principles. This greatly reduces the impact of analyst or researcher bias on how the results are interpreted.

Disadvantages of quantitative data

There are two main drawbacks to be aware of when working with quantitative data, especially within a research context:

• Quantitative data can lack context. In some cases, context is key; for example, if you’re conducting a questionnaire to find out how customers feel about a new product. The quantitative data may tell you that 60% of customers are unhappy with the product, but that figure alone will not tell you why. Sometimes, you’ll need to delve deeper to gain valuable insights beyond the numbers.
• There is a risk of bias when using surveys and questionnaires. Again, this point relates more to a research context, but it’s important to bear in mind when creating surveys and questionnaires. The way in which questions are worded can allow researcher bias to seep in, so it’s important to make sure that surveys are devised carefully. You can learn all about how to reduce survey bias in this post .

8. Should I use quantitative or qualitative data in my research?

Okay—so now we know what the difference between quantitative and qualitative data is, as well as other aspects of quantitative data. But when should you make use of quantitative or qualitative research? This answer to this question will depend on the type of project you’re working on—or client you’re working for—specifically. But use these simple criteria as a guide:

• When to use quantitative research: when you want to confirm or test something, like a theory or hypothesis. When the data can be shown clearly in numbers. Think of a city census that shows the whole number of people living there, as well as their ages, incomes, and other useful information that makes up a city’s demographic.
• When to use qualitative research: when you want to understand something—for example, a concept, experience, or opinions. Maybe you’re testing out a run of experiences for your company, and need to gather reviews for a specific time period. This would be an example of qualitative research.
• When to use both quantitative and qualitative research: when you’re taking on a research project that demands both numerical and non-numerical data.

9. What are some common quantitative analysis tools?

The tools used for quantitative data collection and analysis should come as no surprise to the budding data analyst. You may end up using one tool per project, or a combination of tools:

• Microsoft Power BI

10. What is quantitative data? FAQs

Who uses quantitative data.

Quantitative data is used in many fields—not just data analytics (though, you could argue that all of these fields are at least data-analytics-adjacent)! Those working in the fields of economics, epidemiology, psychology, sociology, and health—to name a few—would make great use of quantitative data in their work. You would be less likely to see quantitative data being used in fields such as anthropology and history.

Is quantitative data better than qualitative data?

It would be hard to make a solid argument of which form of data collection is “better”, as it really depends on the type of project you’re working on. However, quantitative research provides more “hard and fast” information that can be used to make informed, objective decisions.

Where is quantitative data used?

Quantitative data is used when a problem needs to be quantified. That is, to answer the questions that start with “how many…” or “how often…”, for example.

What is quantitative data in statistics?

As statistics is an umbrella term of a discipline concerning the collection, organization and analysis of data, it’s only natural that quantitative data falls under that umbrella—the practice of counting and measuring data sets according to a research question or set of research needs.

Can quantitative data be ordinal?

Ordinal data is a type of statistical data where the variables are sorted into ranges, and the distance between the ranges are not known. Think of the pain scale they sometimes use in the hospital, where you judge the level of pain you have on a scale of 1-10, with 1 being low and 10 being the highest. However, you can’t really quantify the difference between 1-10—it’s a matter of how you feel!

By that logic, ordinal data falls under qualitative data, not quantitative. You can learn more about the data levels of measurement in this post .

Is quantitative data objective?

Due to the nature of how quantitative data is produced—that is, using methods that are verifiable and replicable—it is objective.

11. Key takeaways and further reading

In this post, we answered the question: what is quantitative data? We looked at how it differs from qualitative data, and how it’s collected and analyzed. To recap what we’ve learned:

• Quantitative data is data that can be quantified. It can be counted or measured, and given a numerical value.
• Quantitative data lends itself to statistical analysis, while qualitative data is grouped according to themes.
• Quantitative data can be discrete or continuous. Discrete data takes on fixed values (e.g. a person has three children), while continuous data can be infinitely broken down into smaller parts.
• Quantitative data has several advantages: It is relatively quick and easy to collect, and it is analyzed subjectively.

Collecting and analyzing quantitative data is just one aspect of the data analyst’s work. To learn more about what it’s like to work as a data analyst, check out the following guides. And, if you’d like to dabble in some analytics yourself, why not try our free five-day introductory short course ?

• What is data analytics? A beginner’s guide
• A step-by-step guide to the data analysis process
• Where could a career in data analytics take you?

Quantitative Research on Leadership and Business Ethics: Examining the State of the Field and an Agenda for Future Research

• Original Paper
• Published: 29 August 2019
• Volume 168 , pages 109–119, ( 2021 )

Cite this article

• Michael Palanski 1 ,
• Alexander Newman 2 ,
• Hannes Leroy 3 ,
• Celia Moore 4 ,
• Sean Hannah 5 &
• Deanne Den Hartog 6  

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In this article, the co-editors of the Leadership and Ethics: Quantitative Analysis section of the journal outline some of the key issues about conducting quantitative research at the intersection of business, ethics, and leadership. They offer guidance for authors by explaining the types of papers that are often rejected and how to avoid some common pitfalls that lead to rejection. They also offer some ideas for future research by drawing upon the opinions of four noted experts in the field to consider the types of research questions we should be asking, the types of theory we should be building, the types of models we should be testing, and the types of methods we should be using.

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Palanski, M., Newman, A., Leroy, H. et al. Quantitative Research on Leadership and Business Ethics: Examining the State of the Field and an Agenda for Future Research. J Bus Ethics 168 , 109–119 (2021). https://doi.org/10.1007/s10551-019-04267-9

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Quantitative Research Examples for Business Success

Mary Kate Morrow

Straight forward quantitative research examples.

Two well known research methods are the quantitative and qualitative research methodologies. When deciding which research method to use the qualitative vs quantitative framework is commonplace. The two research methods have key differences ranging from data collection to research design techniques. Understanding the differences and similarities between quantitative research and qualitative research is crucial for data collection and data analysis optimization. Quantitative research focuses on quantifiable, numerical data collection and data analysis such as statistical analysis techniques. In fact, statistical, mathematical, and computational techniques are all commonly used on quantitative data collected. Alternatively, qualitative research focuses on non numerical data collection and analysis. Data qualitative research collects could originate from a market research focus group or an online survey administered. Quantitative research is undertaken for a wide variety of reasons. Common reasons range from testing a sample size from a large population to case studies for market research purposes. In fact, quantitative research is conventional in market research efforts. Data collection in quantitative market research can focus on current and potential customers. The research methods and research design quantitative and qualitative research uses oftentimes overlap. For example, case studies can use either quantitative research or qualitative research or mixed methods of both. Research questions for case studies employing qualitative research data collection would likely use open ended inquiries. Alternatively, case studies appropriating quantitative research data collection would likely use closed ended research questions. Deciding which research method is most appropriate depends on the researcher's intention. If mixed methods are utilized it is crucial that researchers can undertake quantitative and quantitative research properly.

Quantitative Research Example 1

For many researchers, it is helpful to analyze specific real world research method examples. One example of quantitative research would be a study regarding how mobile phone usage influences teenagers. The sample size of the survey includes a designated number of females and males. Researchers must take care to make sure that the sample size accurately reflects the larger population studied. Instead of researchers asking open ended questions a numerical data point scale is used for research question answer options. A numerical data point scale commonly utilized in quantitative research processes is the Likert scale. The Likert scale supplies numerical figures as research question answer options that represent associated opinion statements. For example, the number 1 could represent strongly agree while the number 10 could represent strongly disagree. The ability for the opinions of sample sizes to translate directly into numerical data is a powerful quantitative research tool.

Quantitative Research Example 2

Another example of quantitative research could be regarding the number of hours that employees spend at their workplace. Instead of having to go to offices all around the city, a proper sample size suffices. Rather than asking employees directly how much time they spend at their office, alternative data collection methods can be utilized. For example, researchers could note the times that sample size participants arrive at and leave the office. As a result, data collected via observation translates the non numerical research process of observation into a numerical data format. Researchers then use data quantitative research produced to find the average amount of hours an employee spends in the office.

Quantitative Research Example 3

Quantitative research is often undertaken in healthcare organizations. Quantitative data can massively assist hospitals seeking to improve their business processes and customer experience. For example, a hospital may want to know how efficiently the organization is functioning. Research question focus can range from doctor time spent per patient to the frequency of patient visits. Similar to other examples, instead of open ended research questions administered, numerical data points are assigned to research question answers. Numerical data point research question answer options may also include a range of numbers. A doctor time research question answer options may include up to 15 minutes, 15 to 30 minutes, over 30 minutes. For a patient visit frequency question, answer options may include 1 time, 2-5 times, more than 5 times. As a result, research question answers are translated into a quantifiable data format. From there, data collected can be analyzed and then applied to real world solutions. Actionable insights can be used to improve customer experience and business processes significantly.

Quantitative Research Example 4

Many businesses utilize the quantitative research method in order to better understand customer satisfaction. Customer satisfaction surveys are so commonplace that survey templates are widely available. A business professional can then customize the survey template to match the research design and objectives. A customer satisfaction survey seeks to learn more about everything from brand reputation to product or service pricing and quality. Survey data collected can occur through techniques like net promoter score questions and matrix table questions. Overall, the objective of customer satisfaction surveys is to improve the customer experience in a measurable way.

Quantitative Research Example 5

Many different countries all across the world utilize quantitative research methods for census purposes. A census is an official survey of a country's population. While the census aims to gain universal response it is still a sample size unless the entire population is polled. Once quantitative data collection is completed for the census, further subgroups can be classified. For example, classifications of population subgroups can include age ranges or gender assigned at birth.

Key Takeaways of Quantitative Research Examples

• There are different applications for qualitative data and quantitative data collected. Gaining a comprehensive understanding of qualitative data and quantitative data collection methods is massively helpful.
• Real world examples of quantitative research range from a business administered customer satisfaction survey to an employee hour research study.

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Why Does Your Business Need Qualitative And Quantitative Research?

If you own a business, you understand the value of information and data. Almost every firm makes judgments based on data. Some businesses strongly emphasise quantitative research, while others devote their efforts to gathering and interpreting qualitative data analysis.

We can all concur that quantitative market research knowledge can potentially convert your company into a results-driven, strong franchise. Also, if you intend to make it big, you must do your homework before diving in. You can conduct qualitative or quantitative research or both. They each have distinct advantages. The best results are obtained by combining the two.

This post will discuss the qualitative research approach, why it is vital for your business, quantitative market research services, and its significance in business. Also, we will discuss why your business needs both qualitative and quantitative research. Then we’ll tell you about the platform that offers the best qualitative and quantitative market research services. Let’s get this started.

• Qualitative Research

The qualitative research approach focuses on how individuals feel, what they believe, and why they make specific decisions. For example, suppose you are considering altering your branding. In that case, you could conduct qualitative data analysis to understand people’s emotional reactions to your new identity and what they identify it with.

Do you know what qualitative research is? If your answer is a no, it’s a research strategy that produces semi-structured results, guaranteeing that the conversation – one-on-one or in a focus group – stays on topic and relevant to deliver the information you want. There are many qualitative research platforms, but your chosen one matters greatly.

Why Does Your Business Need Qualitative Research?

The benefit of qualitative research or qualitative data analysis is that it gives you a comprehensive image of your consumers. The following are some of the reasons why the qualitative research approach is essential for any business.

Qualitative Research Approach Brings Flexibility

Using qualitative project management or research in all facets of your business’s operation gives you flexibility and fluidity. The main implication of the qualitative research approach is that the qualitative data analysis must be relevant to the subject matter and should give the best possible path ahead.

Suppose the data collection source does not meet the requirements. In that case, the qualitative research company has the liberty and flexibility to alter the source or the data collection method to adhere to and respect the quality elements.

Qualitative Research is Based on Human Perceptions and Experiences

Since the best qualitative research platforms understand the value of qualitative research, the data collected and obtained is heavily dependent on human experiences and observations.

The human mind operates on two main functioning modes, one centred on more facts and numbers and the other on our subconsciousness. The qualitative content analysis considers a person’s gut reactions and instincts since they result from experiences and observations accumulated over time.

Qualitative Data Analysis Has a Predictive Character

The data produced and gathered has a predictive character, which is one of the benefits and merits of the qualitative research approach. The key reason for this is that the qualitative research company focuses on one specific group to gather data on the subject matter and that specific customers share the frequency and are like-minded.

All of this leads to data that is verified on quality parameters. It may be a disadvantage for the research aspect, but the data gathered is viable and useful for the organisation that requires it.

Qualitative Research Breeds Creativity

One of the primary reasons that industry experts and professionals recommend businesses to comprehend and implement a qualitative research approach is that it incorporates the value of creativity in its technique. Since it is based more on emotional responses, experiences, and perceptions, the mode of operation allows consumers to voice their opinions honestly, transparently, and authentically.

They are given the flexibility of creative expression, which aids the research team in producing genuine outputs and final products.

Qualitative Research is a Fluid Process

Another advantage of the qualitative research approach is that it is an open-ended, fluid procedure. The research is not time-limited because it must conform to and meet quality requirements.

It also has an emotional component because it is based on human experiences and perceptions you cannot get in a certain questionnaire or time frame. The replies acquired are mostly influenced by the person’s actions or impacts on their conduct in various life events. Therefore the study must be open-ended in nature.

Qualitative Research Gives Insights and Information About the Business

In today’s highly competitive and ever-changing market, it is critical for businesses to communicate with their clients on a psychological and emotional level. It provides a deeper grasp of the consumers and target audience’s actual wants and developing desires. It assists businesses in curating, designing, planning, and manufacturing product offers that meet the wants and expectations of their customers.

Furthermore, it promotes the creation of industry-related data and insights that benefit the whole industrial domain and its enterprises. As a result, a qualitative research approach must be used to capture and comprehend the prospective customer’s behavioural psychology.

Qualitative Research Saves Cost

One of the most significant benefits of hiring a good qualitative research company for your business is that it saves money without affecting the total marketing and sales budget. You can implement the qualitative research approach with smaller sample numbers compared to other research approaches.

Its result is swift and legitimate, giving the research team confidence to carry forward with the project as good and beneficial data has been supplied to the firm management.

Qualitative Research Provides Extra Material for Your Company’s Marketing and Creative Teams

As previously said, the qualitative research approach has an artistic element that works as an intrinsic component. And when more data is acquired based on real feelings, experiences, and observations while putting human consciousness and psychology at the forefront, the material becomes more authentic and fruitful.

It greatly assists the creative, advertising, and sales departments in planning, designing and implementing genuine and effective brand and promotional campaigns. To summarise, it is a win-win scenario for all parties concerned.

It greatly assists the firm’s management in attracting the target audience and market through unique and out-of-the-box promotional and marketing concepts and campaigns. Plus, all of it assists in boosting the general sales and earnings of the organization achieving all the short and long-term targets.

Example of How Qualitative Research is Conducted

One way the qualitative research approach is conducted is through one-on-one interviews. One-on-one interviews can be conducted over the phone or in person. It is a more personalised method that gives a greater knowledge of the clients without outside influence. Participants are generally more at ease with this method than with any other.

• Quantitative Research

Quantitative market research collects customer data on attitudes, behaviours, views, and other characteristics to support or refute a hypothesis. This is done by gathering numerical data, which is easily measurable to establish statistical significance.

Quantitative research or quantitative data analysis, as it may be called, gathers numerical data through closed-ended questions, such as Likert scales or questionnaire forms. Once you’ve distributed your survey to your intended audience, you can quickly quantify the answers for each answer choice.

The questions you pose must be impartial to gather and interpret respondent data. Numbers are fundamental to quantitative data collection services. It employs statistical analysis and data to spotlight critical information about your company and market.

This sort of data, obtained through multiple-choice surveys, can assist you in gauging interest in your firm and its offers. For example, quantitative research can help answer problems like:

• Is there a demand for your goods and services?
• How well-known is your product or service in the market?
• How many individuals are considering purchasing your goods or service?
• What kind of personalities are your most loyal customers?
• What are their purchasing patterns?
• How are your target market’s demands changing?
• How long are visitors remaining on your business website, and which website are they leaving to?

Most significantly, quantitative market research services are statistically legitimate since they are mathematically founded. This implies you use its results to forecast the future of your business.

Quantitative data analysis and quantitative data collection services may sound stressful, which is why some business owners seek the services of a good quantitative market research agency.

This gives them a chance to focus on the more important aspect of the business while the quantitative market research companies do their job.

Why Your Business Needs Quantitative Market Research Services

Below are some of the importance of quantitative market research services for your business.

Quantitative Data Analysis Services Establishes Connections Between Variables

Quantitative research is a conventional scientific procedure that attempts to establish causal correlations and measure their strength and relevance. It has emphasised quantifiable metrics, and numerical, mathematical, or statistical market research gathered over time using sampling methods or by manipulating existing statistical data.

Good quantitative market research companies emphasise comparing, finding the link between the uncontrolled and outcome variables, and generalising the acquired data over many consumer groups.

Depending on the scenario, the research design might be experimental, demonstrating causality, or descriptive, identifying connections between variables. The quantitative research methods are investigative, and the outcome is objective, statistical, and logical.

The importance of quantitative market research to your business is that it helps you establish a link between variables using a systematic manner on a sample representing the whole consumer base.

Quantitative Market Research Gives Data That is Objective and Dependable

Quantitative research is a way of collecting trustworthy and accurate business outcome data. This is accomplished through analysis and measurement. It can illustrate why the data is obtained as well as how it is statistically treated.

It also deals with study results concerning relationships and reports on unexpected incidents during data collection. The study specifically shows why planned, and real analysis differ from one another. It offers a good notion of how to deal with missing data.

The importance of quantitative data analysis to your organization is that it aids in the objective and dependable acquisition of data, which is then presented in a logical, analytical, and complete manner, highlighting crucial results.

Quantitative Market Research Helps in Measuring The Consumer Experience

The importance of quantitative market research is that it is simple to assess data and demonstrate outcomes using objective data. It gives descriptive data that a quantitative market researcher may split down to discover differences between certain groups, such as age groupings.

Quantitative data analysis forecast is based on numerical facts and is tough to refute or dispute. One of the major advantages of doing quantitative market research is the capacity to transform data into forecasts and quantify customer experience. Businesses rely largely on their consumers and have attempted to focus on service-oriented operations. Customer services and performance development and measurement are critical to them.

Quantitative research is essential for obtaining an accurate depiction of the consumer experience and making significant changes in performance. The significance of quantitative market research and quantitative data analysis in gauging customer experience cannot be overstated.

It uses a large enough sample size to obtain valid, accurate, and trustworthy outcomes in consumer insight. It uses survey-based research to collect input related to population attitudes and thoughts. Quantitative research for marketing assists in addressing critical sections of the customer journey to learn what consumers think about their experience so you can make educated business decisions based on this input.

Another function of quantitative market research is discovering the multiple impediments to sales effectiveness and service excellence. The study provides an opportunity to keep connected with client trends through selected survey forms that are scheduled regularly to obtain a fast response.

Example of How Quantitative Research is Conducted

The most effective quantitative research method is questionnaires and surveys. You can deliver questionnaires and surveys containing a list of acceptable responses to a wide sample group.

The options narrow the field of possible responses, resulting in a more thorough survey. The survey is measured by visually showing the proportion of respondents who picked each response. Quantitative data analysis has gotten much easier with online survey questionnaires.

The simplest answer to whether it is possible to combine qualitative and quantitative research is a resounding YES. Usability testing incorporating qualitative and quantitative data analysis for company improvement may be both complementary and productive.

Various data have different values depending on where you are in your business. The qualitative research approach is useful in the early phases of a firm since it allows you to evaluate choices, identify problems, and make modifications.

Although quantitative market research is beneficial throughout the business development process, it can have a higher impact later on when a product is ready to be published or utilised. You might want to track which customers appreciated it the most and which did not.

While each approach has benefits and drawbacks, you are not compelled to select one. Use both data to understand the “what” and “why” questions. Quantitative market research can only tell you whether the metrics you monitor are rising or dropping, not why. The “why” of a user study may be the most important part, and qualitative data analysis will assist in answering that query.

The ideal usability studies contain quantitative and qualitative data analysis to fully comprehend the client experience, efficiency, and the identification of any faults or business defects.

Where to Get The Best Qualitative And Quantitative Research For Your Business?

There are several quantitative market research companies and qualitative research platforms available. But only a few offer quantitative and qualitative research. One such company, and one we recommend, is Insights Opinion, whose simple platform gives users the statistics as well as the explanation behind the findings.

Their team is well-versed in how you intend to utilise the data and what you ought to know. They adhere to strict quality standards; consequently, their qualitative and quantitative market research methodologies produce reliable findings. Visit the Insights Opinion website now to get one of the best qualitative and quantitative data analysis services.

Customer research methodologies, both qualitative and quantitative, collaborate to completely grasp the usefulness of data and customer preferences and to assist in producing the greatest resources available. The more value you derive from your optimization process, the more you will improve it.

Using the best qualitative and quantitative market research agency is important to build a better business. If you’re ready to improve your business with qualitative and quantitative market research methods, visit Insights Opinion and get started immediately.

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How Qualitative Research Can Improve Your Customer Experience

January 3, 2023

Read on to discover five powerful qualitative research tools that will drive informed and strategic decision making for your brand.

At Kelton (now Material), our goal is to propel your customer relationships from transactional to transformative, whether you’re a world famous brand or a new business. We’ve spent decades helping brands solve major pain points and develop customer centric experiences. Our solutions usually involve a qualitative research component—and for good reason. While quantitative research and metrics will always be important, incorporating in-depth qualitative research can foster empathy for current and potential customers and inspire strategic decision making and forward-thinking business plans.

What is qualitative research?

Qualitative research encompasses a broad range of research techniques to solve critical business challenges and inform new strategies for growth. You’re probably familiar with the basics of qualitative research, but it can be challenging to understand how different methodologies can explain and predict consumer behavior in various target markets.

Qualitative research enables us to gather solid qualitative data to understand the drivers, motivators and factors behind current and potential customers’ purchasing decisions. Brands can then communicate more effectively, shape perceptions, and form powerful brand narratives. We’ve compiled a list of qualitative market research tools to help you create unforgettable customer experiences.

Five Qualitative Market Research Tools for Customer Experience

Focus groups.

Focus groups are a key part of qualitative research: these groups of people are comprised of a sample size of about six to eight respondents with different backgrounds and viewpoints. They usually have one thing in common: they use or don’t use a product, are familiar or unfamiliar with a brand, etc. In these in-depth group discussions, consumers are guided by a moderator. In person focus groups are held in a facility with a one-way mirror for observation, and post pandemic, many now take place online. We sometimes use more creative or immersive settings when the project calls for it. Group discussions encourage open conversation, diversity of opinions, and idea-building inspired by a range of ideas and perspectives.

You can use focus groups and group discussions to test creative or advertising copy concepts, discuss brand perceptions, new products or services, and ideate with customers to optimize portfolio offerings. It is also typical to conduct focus groups in a variety of geographical areas (domestic or international) to enhance the quality of opinions from a range of respondents and facilitate data collection and data analysis.

Co-Creation Sessions

Co-creation sessions are another important facet of qualitative research. They’re different than focus groups and are more focused on the iteration of ideas and concepts. The facilities are locations that foster creativity and inspire differentiated thinking. These sessions can be used to understand new products, their overall look and feel, design functionality, and differentiation between other similar products. There is typically stimulus involved to immerse participants in the product or concept development/iteration process.

Co-creation sessions are an incredibly valuable element of qualitative research, because they allow customers to actively work with the products to identify pain points and suggest additional features. By involving consumers in the creation process, your product inevitably becomes more consumer focused.

SmartCommunities (Online Focus Groups)

An online platform that acts as a prolonged focus group, Material’s SmartCommunities are another great tool in the market research toolkit to encourage further ideation and provide rich insights. A brand can gain more varied audience representation since recruitment isn’t limited by geography, demographics, or socioeconomic factors. There is usually a larger base of participants, which is more cost- and time-effective. These respondents are encouraged to think through their answers to questionnaires, rather than provide opinions on the spot, which can elicit a more accurate account of specific preferences and experiences. Material’s SmartCommunity platform reveals invaluable insights and provides important qualitative data.

Ethnographic Interviews

Ethnographic interviews are one-on-one, in-depth interviews that include a participant and a moderator. This type of research typically occur in a consumer’s home, where the individual is more likely to be comfortable enough to speak openly about their lifestyles, preferences, and beliefs. Ethnographic interviews help companies understand their consumers at more emotional and functional levels, fostering empathy and helping to uncover unmet needs or pain points.

Visiting with a consumer in their home allows us to see how shoppers interact with categories. In this type of research we observe how they navigate their kitchen, closet, or bedroom, for example. What hygiene products, cooking utensils and technology do they use? What do they see as necessary or unnecessary? What are they saving for? What do they see as a waste of money? During this fascinating ethnographic process, we also explore their motivations and discuss what propels their purchasing decisions.

Shop-Alongs

Shop-alongs are an extremely powerful tool that can be used to see the purchase journey come to life in real time. We spend time with participants in a store to observe how they interact with displays, and how they respond to the various products they encounter. We can understand how they shop for specific needs/wants, browse, make decisions, while also understanding the influence of packaging and in-store advertising. Immersive observation allows us to uncover consumer behaviors and motivations that customers don’t even consciously realize, and this information feeds into actionable strategies for brands and products across many categories.

In a nutshell: whether your brand is a startup, small business or a global company, we’re confident that our toolkit of research techniques and cutting edge methodologies will ultimately help you develop powerful marketing strategies, reach your target audience, and change your customer relationships from transactional to transformative.

When you’re ready to partner, contact Kelton (now Material) by filling out this form .

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Qualitative consumer insights, correlation, causation, and qualitative research, 3 secrets to great consumer interviews (tips from a reporter's notebook ).