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What Is Quantitative Research? | Definition, Methods & Examples

Published by Ellie Cross at October 24th, 2024 , Revised On October 24, 2024

Quantitative research involves gathering and analysing numerical data to make predictions and describe the relationship between two variables. It deals with more than just numbers and integrates theory, hypothesis , proper methodological approach and statistical analysis to answer research questions.

It is a crucial part of market research that relies on numerical data and other computational techniques to gain insights into consumer preferences and opinions. Unlike qualitative research which uses hard evidence to answer a research question, quantitative research is more data-based and uses objective measurements to answer research questions.

What is Quantitative Research?

The meaning of quantitative research can be understood from the word quantitative, which focuses on data such as numbers that can be easily quantified, compared and analysed. This type of research uses both primary and secondary sources to gain data through closed-question poll results, demographic data and census information.

While quantitative data can be gathered through surveys and other methods, it can also be gained from the research studies of third parties. Additionally, this research is widely used in the fields of psychology, economics, accounting, finance and marketing.

Types Of Quantitative Research

There are several kinds of quantitative research with different methodologies and purposes. Each type has its own characteristics and can be used to calculate data in different ways. Here are numerous types of quantitative research:

Correlational Research

Correlational research defines the possibility of a relation existing between two variables. It identifies a relationship to define patterns and trends between two or more study variables.

Example : Cross-sectional studies and cross-section analysis

• Experimental Research

In experimental research , you investigate if two variables affect each other. This mostly includes studying the effect of independent variables on dependent variables. Experiments are carried out to study the cause and effect between them to establish relationships.

Example: Field and laboratory experiments

• Descriptive Research

In this type of research, a researcher often studies the characteristics of a dataset. This includes applying mean, median and standard deviation equations to understand the data.

Example: Surveys and observational studies

It is necessary to know that both experimental and correlational research use sampling methods to test hypotheses and generate findings that are applicable to a large population.

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Quantitative Research Methods

Quantitative research methods include both primary and secondary research methods to analyse and gather numerical data. Here is an overview of the various methods employed for quantitative research:

Quantitative Research Advantages & Disadvantages

Quantitative research focuses on gathering numerical data to explain a particular topic, subject or research question. Like any other research, there are strengths of qualitative research. However, there are disadvantages as well which should be considered:

Quantitative Research Vs Qualitative Research

There are two types of research, qualitative research and quantitative research . Unlike quantitative research, qualitative research is exploratory and does not use mathematical and statistical methods to analyse the data gathered. The table below outlines the key differences between qualitative and quantitative research:

Quantitative Research Examples

Here are a few examples of quantitative research that can help understand how the studies are carried out:

A fast-food restaurant notices that there has been a significant drop in their sales. To understand the reasons behind this, they conduct a descriptive research study to gather insights. Numerical data is collected through customer surveys with close-ended questions asking about food quality, customer service, and service speed. This helps them pinpoint that the service speed is a major issue.

A smartphone company has launched a new smartphone with added features and benefits. They display a new marketing campaign in their outlets at every mall. To determine the success of their product, they launched a social media survey based on the Likert scale, where the customers have to rate the features and overall campaign on a scale of 1 to 5 reflecting the level of satisfaction or dissatisfaction. These responses help the brand determine customer’s perceptions, opinions and overall experience.

Frequently Asked Questions

What is research design for quantitative study .

A research design for a quantitative study is a framework that highlights how the research will be carried out. It specifies the research question, variables, sampling method to be used, data analysis methods and the ethical considerations to be taken into view.

What is a CASP quantitative research tool?

CASP (Critical Appraisal Skills Programme) is a quantitative research tool that is used to evaluate the effectiveness and quality of quantitative research studies. It checks the authenticity, accuracy and reliability of the data collected and assists policymakers and researchers in ensuring that it meets the highest standards of research.

What are the different kinds of quantitative research?

Quantitative research can be categorised into several types such as:

• Cross-sectional Research
• Correlational Research 
• Comparative Research 
• Longitudinal Research

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Primary research deals with research that is objective and accurate by collecting first-hand data & information on a specific subject.

Qualitative research uses surveys, interviews, observations, and case studies to gather and analyse data to create meaningful conclusions.

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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.

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Home Market Research

Quantitative Research: What It Is, Types & Methods

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

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

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques.

Quantitative research collects statistically significant information from existing and potential customers. It uses sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form . After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly.

Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

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

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

Quantitative Research Characteristics

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

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

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. They are usually divided into two large groups to understand and organize them easily.:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

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

A. Techniques and Types of Primary Studies

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

01. Survey Research

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

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

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

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

Cross-sectional surveys

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

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

Longitudinal surveys

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

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

02. Correlational Research

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

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

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

Example of Correlational Research Questions :

• The relationship between stress and depression: Is there a significant correlation between levels of stress and symptoms of depression among adults?
• The equation between fame and money: What is the relationship between an individual’s level of fame and their financial earnings?
• The relation between activities in a third-grade class and its students: How do classroom activities influence the academic performance and social development of third-grade students?

03. Causal-comparative Research

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

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

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager: How do different types of drug use affect the academic performance and social behavior of teenagers?
• The effect of good education on a freshman: What is the effect of receiving a quality education in high school on the academic success of college freshmen?
• The effect of substantial food provision in the villages of Africa: How does substantial food provision impact the health and economic productivity of villagers in rural African communities?

04. Experimental Research

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

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

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

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
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B. Data Collection Methodologies

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

01. Data Collection Methodologies: Sampling Methods

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

Probability sampling

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

There are four main types of probability sampling:

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

Non-probability sampling

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

There are five non-probability sampling models:

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

02. Data collection methodologies: Using surveys & polls

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

Using surveys for primary quantitative research

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

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

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

Use of different question types

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

Survey Distribution and Survey Data Collection

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

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

Survey example

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

Using polls for primary quantitative research

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

C. Data Analysis Techniques

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

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

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

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

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

Secondary Quantitative Research Methods

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

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

The following are five popularly used secondary quantitative research methods:

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

Quantitative Research Examples

Some examples of quantitative research are:

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

Quantitative Research vs Qualitative Research

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

Quantitative Research

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

Qualitative Research

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

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

What are the Advantages of Quantitative Research?

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

Collect Reliable and Accurate Data:

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

Quick Data Collection:

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

Wider Scope of Data Analysis:

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

Eliminate Bias:

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

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including:

• The collection of reliable and accurate data
• Quick data collection
• A broader scope of data analysis
• The elimination of bias

These advantages makes it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

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

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

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

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

Frequently Asked Questions (FAQs)

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

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

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

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

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

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Quantitative and Qualitative Research

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

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

What is the purpose of quantitative research?

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

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

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

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

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

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What Is Quantitative Research? Types, Characteristics & Methods

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Ever wondered how companies figure out what we want? The answer lies in numbers. Quantitative research is the art of turning numerical data into valuable insights by uncovering hidden trends and patterns.

From understanding customer behaviors to making smarter business decisions, numbers can tell a powerful story.

In this blog, we’ll explore how this research method can help your business and learn about its types, examples, and expert-backed tips.

What Is Quantitative Research?

Quantitative research is a method that uses numbers and statistics to gather precise, measurable data on the research subject. 

Offering numbers and stats-based insights, this research methodology is a crucial part of primary research and helps understand how well an organizational decision will work out. 

This approach mainly uses online surveys, questionnaires , polls , and quizzes to collect measurable and unbiased data from large groups.

For example, qualitative research can let you know whether your product is a hit or miss, but quantitative data allows you to know exactly what percentage of consumers like it, and what numbers think you still need to improve. As a result of this precision , you can make decisions that are likely to be more in tune with the market demands

Quantitative research thus provides crucial, numbers-based insights that help organizations make informed decisions and predict outcomes effectively .

What Is the Purpose of Quantitative Research?

Quantitative research is the backbone of evidence-based decision-making. Its core purpose is to transform raw data into actionable insights. Researchers can uncover hidden patterns, trends, and relationships that might otherwise go unnoticed by employing statistical methods to analyze numerical information.

Here’s an in-depth analysis of its true objectives:

• Prediction: Quantitative research excels at forecasting future trends or outcomes. By identifying historical patterns, researchers can model potential scenarios and make informed predictions.
• Generalization: By studying a representative sample, researchers can draw conclusions about a larger population. This allows for extrapolating findings to broader groups, providing valuable insights for businesses, policymakers, and scientists.
• Testing Hypotheses: Quantitative research is essential for testing theories and hypotheses. By collecting empirical data, researchers can determine if their assumptions are supported by evidence.
• Measuring Outcomes: Evaluating the effectiveness of interventions or programs is a key application of quantitative research. By tracking specific metrics, researchers can assess the impact of initiatives and make necessary adjustments.
• Understanding Relationships: This research method helps uncover correlations and causal relationships between variables. By identifying how factors influence each other, researchers can develop strategies to optimize outcomes.

What Are the Types and Examples of Quantitative Research?

Quantitative research is usually conducted using two methods. They are-

• Primary quantitative research methods
• Secondary quantitative research methods

Let’s discuss each type in detail.

I. Primary Methods

Primary quantitative research is the most popular method of conducting market research. This method differs from others in that the researcher collects data firsthand instead of using data collected from previous research.

There are multiple types of primary quantitative research. They can be distinguished based on three distinctive aspects: Techniques & types of studies, Data collection methodologies, and Data analysis techniques .

A. Techniques & Types of Studies:

1. survey research.

Surveys are the easiest, most common, and one of the most sought-after quantitative research techniques. The main aim of a survey is to widely gather and describe the characteristics of a target population or customers. Surveys are the foremost quantitative method preferred by both small and large organizations.

Surveys can be conducted using various methods, such as online polls, web-based surveys, paper questionnaires, phone calls, or face-to-face interviews. Survey research allows organizations to understand customer opinions, preferences, and behavior, making it crucial for market research and decision-making.

You can watch this quick video to learn more about creating surveys. 

Watch: How to Create a Survey Using ProProfs Survey Maker

Surveys, again, are of two types:

2. Cross-Sectional Surveys

Cross-sectional surveys are used to collect data from a sample of the target population at a specific point in time. Researchers evaluate various variables simultaneously to understand the relationships and patterns within the data.

Cross-sectional surveys are popular in retail, small and medium-sized enterprises (SMEs), and healthcare industries, where they assess customer satisfaction, market trends, and product feedback.

3. Longitudinal Surveys

Longitudinal surveys are conducted over an extended period, observing changes in respondent behavior and thought processes. 

Researchers gather data from the same sample multiple times, enabling them to study trends and developments over time. These surveys are valuable in fields such as medicine, applied sciences, and market trend analysis.

4. Correlational Research:

Correlational research aims to establish relationships between two or more variables. 

Researchers use statistical analysis to identify patterns and trends in the data, but it does not determine causality between the variables. This method helps understand how changes in one variable may impact another.

Examples of correlational research questions include studying the relationship between stress and depression, fame and money, or classroom activities and student performance.

5. Causal-Comparative Research:

Causal-comparative research, or quasi-experimental research, seeks to determine cause-and-effect relationships between variables. 

Researchers analyze how an independent variable influences a dependent variable, but they do not manipulate the independent variable. Instead, they observe and compare different groups to conclude.

Causal-comparative research is useful when it’s not ethical or feasible to conduct true experiments. 

Questions for this type of research include analyzing the effect of training programs on employee performance, studying the influence of customer support on client retention, investigating the impact of supply chain efficiency on cost reduction, etc.

6. Experimental Research:

Experimental research is based on testing theories to validate or disprove them. Researchers conduct experiments and manipulate variables to observe their impact on the outcomes.

This type of research is prevalent in natural and social sciences, and it is a powerful method to establish cause-and-effect relationships. By randomly assigning participants to experimental and control groups, researchers can draw more confident conclusions.

Examples of experimental research include studying the effectiveness of a new drug, the impact of teaching methods on student performance, or the outcomes of a marketing campaign.

B. Data Collection Methodologies

After defining research objectives, the next significant step in primary quantitative research is data collection. This involves using two main methods: sampling and conducting surveys or polls. Quantitative research can also be categorized according to their sampling methods .

In quantitative research, there are two primary sampling methods: Probability and Non-probability sampling .

1. Probability Sampling

In probability sampling, researchers use the concept of probability to create samples from a population. This method ensures that every individual in the target audience has an equal chance of being selected for the sample.

There are four main types of probability sampling:

• Simple random sampling : Here, the elements or participants of a sample are selected randomly, and this technique is used in studies that are conducted over considerably large audiences. It works well for large target populations.
• Stratified random sampling : In this method, the entire population is divided into strata or groups, and the sample members get chosen randomly from these strata only. It is always ensured that different segregated strata do not overlap with each other.
• Cluster sampling : Here, researchers divide the population into clusters, often based on geography or demographics. Then, random clusters are selected for the sample.
• Systematic sampling : In this method, only the starting point of the sample is randomly chosen. All the other participants are chosen using a fixed interval. Researchers calculate this interval by dividing the size of the study population by the target sample size.

2. Non-probability Sampling

Non-probability sampling is a method where the researcher’s knowledge and experience guide the selection of samples. This approach doesn’t give all members of the target population an equal chance of being included in the sample.

There are five non-probability sampling models:

• Convenience sampling : The elements or participants are chosen on the basis of their nearness to the researcher. The people in close proximity can be studied and analyzed easily and quickly, as there is no other selection criterion involved. Researchers simply choose samples based on what is most convenient for them. 
• Consecutive sampling : Similar to convenience sampling, researchers select samples one after another over a significant period. They can opt for a single participant or a group of samples to conduct quantitative research in a consecutive manner for a significant period of time. Once this is over, they can conduct the research from the start.
• Quota sampling : With quota sampling, researchers use their understanding of target traits and personalities to form groups (strata). They then choose samples from each stratum based on their own judgment.
• Snowball sampling : This method is used where the target audiences are difficult to contact and interviewed for data collection. Researchers start with a few participants and then ask them to refer others, creating a snowball effect.
• Judgmental sampling : In judgmental sampling, researchers rely solely on their experience and research skills to handpick samples that they believe will be most relevant to the study.

C. Data Analysis Techniques

Once the raw data is collected, the next step in primary quantitative research is data analysis. This crucial process helps draw inferences from the research. It’s essential to connect the results with the objectives and determine their statistical significance.

To analyze the quantitative data accurately, you’ll need to use specific statistical methods such as:

• SWOT Analysis : This stands for Strengths, Weaknesses, Opportunities, and Threats analysis. Organizations use SWOT analysis to evaluate their performance internally and externally. It helps develop effective improvement strategies.
• Conjoint Analysis : This market research method uncovers how individuals make complex purchasing decisions. It involves considering trade-offs in their daily activities when choosing from a list of product/service options.
• Cross-tabulation : A preliminary statistical market analysis method that reveals relationships, patterns, and trends within various research study parameters.
• TURF Analysis : Short for Totally Unduplicated Reach and Frequency Analysis, this method helps analyze the reach and frequency of favorable communication sources. It provides insights into the potential of a target market.

By using these statistical techniques and inferential statistics methods like confidence intervals and margin of error , you can draw meaningful insights from your primary quantitative research that you can use in making informed decisions.

II. Secondary Methods

Secondary quantitative research, also known as desk research, is a valuable method that uses existing data, called secondary data.

Instead of collecting new data, researchers analyze and combine already available information to enhance their research. This approach involves gathering quantitative data from various sources such as the internet, government databases, libraries, and research reports. 

Secondary quantitative research is crucial in validating data collected through primary quantitative research. It helps reinforce or challenge existing findings.

Here are five commonly used secondary quantitative research methods :

A. Data Available on the Internet:

The Internet has become a vast repository of data, making it easier for researchers to access a wealth of information. Online databases, websites, and research repositories provide valuable quantitative data for researchers to analyze and validate their primary research findings.

B. Government and Non-Government Sources:

Government agencies and non-government organizations often conduct extensive research and publish reports. These reports cover a wide range of topics, providing researchers with reliable and comprehensive data for quantitative analysis.

C. Public Libraries:

While less commonly used in the digital age, public libraries still hold valuable research reports, historical data, and publications that can contribute to quantitative research.

D. Educational Institutions:

Educational institutions frequently conduct research on various subjects. Their research reports and publications can serve as valuable sources of information for researchers, validating and supporting primary quantitative research outcomes.

E. Commercial Information Sources:

Commercial sources such as local newspapers, journals, magazines, and media outlets often publish relevant data on economic trends, market research, and demographic analyses. Researchers can access this data to supplement their own findings and draw better conclusions.

Now, let’s discuss some real-world examples of quantitative research.

Here are two excellent examples of quantitative research methods used by highly distinguished business and consulting organizations. Both examples show how different types of analysis can be performed with qualitative approaches and how the analysis is done once the data is collected.

1. STEP Project Global Consortium / KPMG 2019 Global Family Business Survey

This research utilized quantitative methods to identify ways that kept the family businesses sustainably profitable with time. 

The study also identified the ways in which the family business behavior changed with demographic changes and had “why” and “how” questions. Their qualitative research methods allowed the KPMG team to dig deeper into the mindsets and perspectives of the business owners and uncover unexpected research avenues as well.

It was a joint effort in which STEP Project Global Consortium collected 26 cases, and KPMG collected 11 cases. 

The research reached the stage of data analysis in 2020, and the analysis process spanned over 4 stages. 

The results, which were also the reasons why family businesses tend to lose their strength with time, were found to be:

• Family governance
• Family business legacy

2. EY Seren Teams Research 2020

This is yet another commendable example of qualitative research where the EY Seren Team digs into the unexplored depths of human behavior and how it affected their brand or service expectations. 

The research was done across 200+ sources and involved in-depth virtual interviews with people in their homes, exploring their current needs and wishes. It also involved diary studies across the entire UK customer base to analyze human behavior changes and patterns. 

The study also included interviews with professionals and design leaders from a wide range of industries to explore how COVID-19 transformed their industries. Finally, quantitative surveys were conducted to gain insights into the EY community after every 15 days. 

The insights and results were:

• A culture of fear, daily resilience, and hopes for a better world and a better life – these were the macro trends.
• People felt massive digitization was a resourceful yet demanding aspect as they had to adapt every day.
• Some people wished to have a new world with lots of possibilities, and some were looking for a new purpose.

Advantages of Quantitative Research

Quantitative research data is often standardized and can be easily used to generalize findings for making crucial business decisions and uncover insights to supplement the qualitative research findings. 

Here are some core benefits this research methodology offers.

A. Objectivity and Reliability

• Unbiased Insights: By relying on numerical data, quantitative research minimizes researcher bias, leading to more objective findings.
• Reproducibility: The standardized methods used in quantitative research allow for the replication of studies, enhancing the reliability and credibility of results.

B. Efficiency and Scalability

• Large Sample Sizes: Quantitative methods enable researchers to gather data from large populations, providing a comprehensive understanding of trends and patterns.
• Rapid Data Collection: Efficient data collection techniques, such as online surveys, expedite the research process.
• Cost-Effective: Compared to qualitative research, quantitative methods can be more cost-effective due to their efficiency and scalability.

C. Precision and Generalizability

• Numerical Precision: Quantitative data provides precise measurements and statistical analysis, allowing for accurate comparisons and predictions.
• Generalizability: Findings from large, representative samples can be generalized to the broader population more confidently.

D. Data-Driven Decision-Making

• Informed Choices: Quantitative research offers actionable insights to inform strategic decisions in business, healthcare, policymaking, and other fields.
• Performance Measurement: By tracking key performance indicators (KPIs), organizations can measure the effectiveness of their initiatives and make necessary adjustments.

Disadvantages of Quantitative Research

Despite its numerous benefits, quantitative research has some cons as well, such as:

A. Lack of Depth and Context

Quantitative research excels at providing numerical data but falls short when it comes to understanding the underlying reasons for behaviors or opinions. It offers a snapshot of what happened but not necessarily why it happened. Researchers may struggle to delve into the complexities of human experiences and motivations.

B. Overreliance on Structured Data

The rigid nature of quantitative research limits the exploration of unexpected findings. Researchers are confined to pre-determined questions and response options, potentially overlooking valuable insights from open-ended exploration.

C. Potential for Bias

While striving for objectivity, quantitative research is not immune to bias. Factors such as sample selection, question-wording, and data analysis methods can introduce biases that distort the results. Careful planning and execution are essential to mitigate these risks.

E. Difficulty Establishing Causality

Correlation does not equal causation. While quantitative research can identify relationships between variables, establishing definitive cause-and-effect links is challenging. Other factors may influence the observed relationship, making it difficult to isolate the true cause.

F. Resource Intensive

Conducting quantitative research often requires significant time and financial resources. Collecting large datasets, employing statistical analysis, and ensuring data quality can be costly and time-consuming. Additionally, specialized expertise may be needed for data analysis and interpretation.

Characteristics of Quantitative Research

Quantitative research clarifies the fuzziness of research data from qualitative research analysis. With numerical insights, you can formulate a better and more profitable business decision.

Hence, quantitative research is more readily contestable, sharpens intelligent discussion, helps you see the rival hypotheses, and dynamically contributes to the research process.

Let us have a quick look at some of its characteristics.

1. Measurable Variables

The data collection methods in quantitative research are structured and contain items requiring measurable variables, such as age, number of family members, salary range, highest education, etc.

These structured data collection methods comprise polls, surveys, questionnaires, etc., and may have questions like the ones shown in the following image:

As you can see, all the variables are measurable. This ensures the research is in-depth and provides less erroneous data for reliable, actionable insights.

2. Sample Size

No matter what data analysis methods are used for quantitative research, the sample size is kept small enough to represent the target market.

The main aim of the research methodology is to obtain numerical insights, so the sample size should be fairly large. Depending on the survey objective and scope, it might include hundreds of thousands of people.

3. Normal Population Distribution

To maintain the reliability of a quantitative research methodology, we assume that the population distribution curve is normal.

This type of population distribution curve is preferred over a non-normal distribution as the sample size is large, and the characteristics of the sample vary with its size.

This requires adhering to the random sampling principle to avoid the researcher’s bias in interpreting the results. Any bias can ruin the fairness of the entire process and defeat the purpose of the research.

4. Well-Structured Data Representation

Data analysis in quantitative research produces highly structured results and can form well-defined graphical representations. Some common examples include tables, figures, graphs, etc., that combine large blocks of data.

This way, you can discover hidden data trends, relationships, and differences among various measurable variables. This can help researchers understand the survey data and formulate actionable insights for decision-making.

5. Predictable Outcomes

Quantitative data analysis can also be used to estimate and predict outcomes. You can construct if-then scenarios and analyze the data to identify upcoming trends or events.

However, this requires advanced analytics and involves complex mathematical computations. So, it is mostly done via quantitative research tools with advanced analytics capabilities.

When to Use Quantitative Research

Quantitative research is particularly suited for the following scenarios:

1. Testing Hypotheses and Theories

• Hypothesis Verification: When you have a specific prediction about a relationship between variables, quantitative research can be used to test its validity.
• Theory Building: By gathering numerical data, you can identify patterns and trends that contribute to the development of new theories or the refinement of existing ones.

2. Measuring and Comparing

• Market Research: Assessing market size, share, and customer preferences to inform product development or marketing strategies.
• Performance Evaluation: Quantifying the effectiveness of programs, policies, or interventions through metrics and benchmarks.
• Comparative Analysis: Comparing different groups or conditions to identify similarities, differences, and relationships.

3. Generalizing Findings

• Population Representation: When you aim to draw conclusions about a larger population based on a representative sample.
• Predictive Modeling: Building models to forecast future trends or outcomes based on historical data.

4. Identifying Cause-and-Effect Relationships

• Experimental Design: While challenging, quantitative research can be used to establish causal relationships through controlled experiments.
• Correlational Analysis: Identifying patterns and associations between variables, though caution is needed to avoid assuming causation.

In essence, quantitative research is most appropriate when you seek precise, measurable data to answer specific research questions and make informed decisions.

Best Practices for Conducting Quantitative Research

Here are some best practices to keep in mind while conducting quantitative research:

1. Understand Your Research Objectives

There are many ways to collect data via quantitative research methods chosen according to the research objective and scope . These methods allow you to make your own observations regarding any hypotheses—unknown, entirely new, or unexplained. 

Based on that, you can hypothesize proof and build a prediction of outcomes that support the same. You can also create a detailed stepwise plan for data collection, analysis, and testing. 

2. Keep Your Questions Simple

The surveys are meant to reach people en-masse, including a wide demographic range with recipients from all walks of life. Asking simple questions will ensure that they grasp what’s being asked easily.

3. Develop a Solid Research Design

Choose an appropriate research design that aligns with your objectives, whether experimental, quasi-experimental, or correlational . You also need to pay attention to the sample size and sampling technique so that they accurately represent the target population.

4. Use Reliable & Valid Instruments

It’s crucial to select or develop measurement instruments such as questionnaires, scales, or tests that have been validated and are reliable. Before proceeding with the main study, pilot-test these instruments on a small sample to assess their effectiveness and make any necessary improvements.

5. Ensure Data Quality

Double-check data entries and cleaning procedures to eliminate any inconsistencies or missing values that may affect the accuracy of your results. For instance, you might regularly cross-verify data entries to identify and correct any discrepancies.

6. Employ Appropriate Data Analysis Techniques

Select statistical methods that match the nature of your data and research questions. Whether it’s regression analysis, t-tests, ANOVA, or other techniques, using the right approach is important for drawing meaningful conclusions. Utilize software tools like SPSS or R for data analysis to ensure the accuracy and reproducibility of your findings.

7. Interpret Results Objectively

Present your findings clearly and unbiasedly . Avoid making unwarranted causal claims, especially in correlational studies . Instead, focus on describing the relationships and patterns observed in your data.

8. Address Ethical Considerations

Prioritize ethical considerations throughout your research process. Obtain informed consent from participants, ensuring their voluntary participation and confidentiality of data . Comply with ethical guidelines and gain approval from a governing body if necessary.

Enhance Your Quantitative Research With Cutting-Edge Software

While no single research methodology can produce 100% reliable results, you can always opt for a hybrid method by selecting the most relevant methods for your objective.

For the best results, opt for smart, efficient, and scalable research tools that offer delightful reporting and advanced analytics. These tools will make every research initiative a success.

Advanced software tools, such as ProProfs Survey Maker, come with pre-built survey templates and question libraries and allow you to create a high-converting survey in just a few minutes.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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Research Methods Simplified

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Quantitative

Quantitative research - concerned with precise measurement, replicable, controlled and used to predict events. It is a formal, objective, systematic process. N umerical data are used to obtain information about the subject under study.

-uses data that are numeric

- primarily intended to test theories

-it is deductive and outcome orientated

-examples of statistical techniques used for quantitative data analysis are random sampling, regression analysis, factor analysis, correlation, cluster analysis, causal modeling and standardized tests

For comparative information on qualitative v.s. quantitative see: The University of Arkansas University Library Lib Guides

Related Information

Control group - the group of subjects or elements NOT exposed to the experimental treatment in a study where the sample is randomly selected

Experimental group - the group of subjects receiving the experimental treatment, i.e., the independent variable ( controlled measure or cause ) in an experiment.

Independent variable - the variable or measure being manipulated or controlled by the experimenter. The independent variable is assigned to participants by random assignment.

Dependent variable or dependent measure - the factor that the experimenter predicts is affected by the independent variable, i.e., the response, outcome or effect from the participants that the experimenter is measuring.

Four types of Quantitative Research

Descriptive  

1) Descriptive - provides a description and exploration of phenomena in real-life situations and characteristics. Correlational of particular individuals, situations or groups are described.  

Comparative

2) Comparative - a systematic investigation of relationships between two or more variables used to explain the nature of relationships in the world. Correlations may be positive (e.g., if one variable increases, so does the other), or negative (correlation occurs when one variable increases and the other decreases).

Quasi-experimental

3) Quasi-experimental - a study that resembles an experiment but random assignment had no role in determining which participants were placed on a specific level of treatment. Generally would have less validity than experiments.

Experimental (empirical)

4) Experimental (empirical) method- the scientific method used to test an experimental hypothesis or premise. Consists of a control group (not exposed to the experimental treatment, i.e.. is dependent) and the experimental group (is exposed to the treatment, i.e., independent)

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What is quantitative research.

Quantitative methodologies use statistics to analyze numerical data gathered by researchers to answer their research questions. Quantitative methods can be used to answer questions such as:

• What are the relationships between two or more variables? 
• What factors are at play in an environment that might affect the behavior or development of the organisms in that environment?

Quantitative methods can also be used to test hypotheses by conducting quasi-experimental studies or designing experiments.

Independent and Dependent Variables

In quantitative research, a variable is something (an intervention technique, a pharmaceutical, a temperature, etc.) that changes. There are two kinds of variables:  independent variables and dependent variables . In the simplest terms, the independent variable is whatever the researchers are using to attempt to make a change in their dependent variable.

* This is a real, repeatable experiment you can try on your plants.

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

• Köse S., & Murat, M. (2021). Examination of the relationship between smartphone addiction and cyberchondria in adolescents. Archives of Psychiatric Nursing, 35(6): 563-570.
• Pilger et al. (2021). Spiritual well-being, religious/spiritual coping and quality of life among the elderly undergoing hemodialysis: a correlational study. Journal of Religion, Spirituality & Aging, 33(1): 2-15.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal. 

• Lakshmi, E. (2021). Food consumption pattern and body mass index of adolescents: A descriptive study. International Journal of Nutrition, Pharmacology, Neurological Diseases, 11(4), 293–297.
• Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gašević, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems, 127, 194–207.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually. 

• Jinzhang Jia, Yinuo Chen, Guangbo Che, Jinchao Zhu, Fengxiao Wang, & Peng Jia. (2021). Experimental study on the explosion characteristics of hydrogen-methane premixed gas in complex pipe networks. Scientific Reports, 11(1), 1–11.
• Sasaki, R. et al. (2021). Effects of cryotherapy applied at different temperatures on inflammatory pain during the acute phase of arthritis in rats. Physical Therapy, 101(2), 1–9.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y,  and  Z are also playing a role.

• Jafari, A., Alami, A., Charoghchian, E., Delshad Noghabi, A., & Nejatian, M. (2021). The impact of effective communication skills training on the status of marital burnout among married women. BMC Women’s Health, 21(1), 1-10.
• Phillips, S. W., Kim, D.-Y., Sobol, J. J., & Gayadeen, S. M. (2021). Total recall?: A quasi-experimental study of officer’s recollection in shoot - don’t shoot simulators. Police Practice and Research, 22(3), 1229–1240.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses. 

• Harries et al. (2021). Effects of the COVID-19 pandemic on medical students: A multicenter quantitative study. BMC Medical Education, 21(14), 1-8.
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Organizing Your Social Sciences Research Paper

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

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

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

Characteristics of Quantitative Research

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

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

Its main characteristics are :

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

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

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

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

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

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

Basic Research Design for Quantitative Studies

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

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

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

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

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

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

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

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

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

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

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

Strengths of Using Quantitative Methods

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

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

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

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

Limitations of Using Quantitative Methods

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

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

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

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

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

SAGE Research Methods Online and Cases

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Qualitative vs Quantitative Research | Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analysing quantitative data

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

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

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

Analysing qualitative data

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

Some common approaches to analysing qualitative data include:

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

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

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

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

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

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

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

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

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

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

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Raimo Streefkerk

Understanding quantitative research evidence

What is quantitative research.

There are two main types of research study: quantitative and qualitative (though some studies use a mixture of these methods).

Quantitative research deals with numbers and measurement and will usually use statistical analysis to draw conclusions. There are two main types of quantitative research: Randomised Controlled Trials (RCTs) and various kinds of trials using observational data (sometimes called population or cohort studies). The section below explains the differences between these two types of study, and their strengths and weaknesses.

The results of quantitative research are often quoted as the Relative Risk which means how much more common it is for a problem to occur in one group than another. In some types of research, particularly population studies (see below) you will see a similar measure called the Odds Ratio. However, for someone trying to decide about a treatment or medical intervention it is often more useful to look at the Absolute Risk . This is the chance of a problem occurring, usually expressed as one in 100, one in 1000 etc.

Qualitative research involves exploring people’s opinions, experiences and preferences in depth, usually through interviews, group discussions or questionnaires.

This article is mainly about the use and limitations of different types of quantitative research study. For more about understanding the research relating to pregnancy and birth see our book AIMS Guide to Safety In Childbirth (principal author Gemma McKenzie)

What are the limitations of quantitative research?

Most of the evidence that is used to make recommendations about maternity care comes from quantitative research. This gives us evidence to compare the outcomes from different medical interventions or approaches to maternity care. Unfortunately, evidence is often not clear-cut or may be lacking altogether, and sometimes the research that has been done is of poor quality.

In thinking about what research evidence can tell us, it’s also important to keep the following limitations in mind.

Definition of the study group

In any kind of research, grouping people together according to one characteristic, such as age, BMI or the fact that they conceived through IVF, ignores the fact that there could be important differences between the individuals in a group. For example, pregnancy and birth outcomes might be very different for someone over 40 who has a healthy lifestyle, is completely well and has had a straightforward pregnancy, compared to one who has existing health problems, or who smokes or drinks heavily, yet recommendations may be made based purely on their age.

Publication and reporting bias

Selective reporting, in other words the failure to publish the results of some studies, may be due to Journals only accepting articles about research which have interesting findings or those which support the status quo.

Another issue is what authors choose to report from their research. They may decide not to publish at all, or only report the findings they wanted to prove, or only report some of the outcomes that they measured. Sometimes, if the study did not show the results the researchers were hoping it would, they will pick on some other finding to report, even if that wasn’t an original aim of the study, or claim that there was “a trend towards” a finding even if it was not statistically significant (see below). It is often necessary to dig into the detail of a study to see whether the headline results are supported by the evidence or not.

Short term outcomes

Quantitative research usually only looks at outcomes that can be measured in the short term. Occasionally there will be follow-up studies that seek to understand the long-term consequences of a medical intervention, but these are the exception. This means that we often don’t know about all the risks, as those that arise after the study ends will not have been recorded.

Focus on selected benefits and risks

Studies are only able to focus on a small number of outcomes, so may not be able to provide information about other risks and benefits which you would want to know about when making a decision. This is partly because of practical considerations, but also because if you include a very large number of outcomes in a study it makes the conclusions less reliable, as it much more likely that something will be found that is a chance finding, but not a real effect.

Lack of views of study participants

Only rarely do the researchers carrying out a clinical study ask how the people receiving the care felt about it. Even when they do, the information is usually limited to something that can be quantified, such as asking ‘how satisfied’ they were with the care.

What is a Randomised controlled trial?

The kind of research that is usually considered to be the ’gold standard’ is a Randomised Controlled Trial (RCT). This is where a group of people is randomly divided into two or more groups, each of which receives a different treatment or type of care. The fact that the allocation into groups is random helps to ensure that the groups contain a similar mix of people. That way any difference in outcomes is likely to be due to the treatment or care received, rather than to the groups including more or fewer people with certain characteristics.

RCTs can work very well if it is a case of comparing something like the effectiveness of two drugs but are more problematic when researching something as complex as pregnancy and labour. There are also limitations on how reliable the findings of any RCT can be, as discussed below.

As a result of these problems it is often the case that too few large and well-conducted RCTs have been done to allow any meaningful conclusions to be drawn about what care is best. In some cases, no good studies have been done at all.

The findings of even the best RCT will be limited to answering a specific question about a particular treatment for a particular group of people (and often in a particular healthcare setting such as a hospital). It won’t be able to tell us everything that we might want to know about that treatment for other people or in other settings, or where additional factors are involved.

Blinding of participants and researchers

Each of the groups in an RCT receives a different treatment, so that the outcomes can be compared. Ideally, such trials would be ’blinded’ which means that neither the person nor those conducting the trial know which group an individual is in.

The problem with RCTs looking at care in pregnancy and labour is that blinding isn’t possible. Knowing which group a pregnant woman or person is in may affect both their level of anxiety (which itself can affect the outcomes) and the behaviour of their doctors and midwives, resulting in unconscious bias. For example, a doctor may believe that waiting for labour to start is riskier than inducing it after a set number of weeks of pregnancy. If they are caring for a mother in the group that waited for labour to begin, the doctor may feel they need to intervene if they notice signs that would not normally cause them concern. This could affect the frequency of unnecessary caesareans and assisted births. This and other issues are discussed in the article “Routine induction of labour at 41 weeks gestation: nonsensus consensus” 1

Other issues of Bias

The results of an RCT can also be misleading if the study was carried out in a way that made it biased. For example, there might be important differences between the groups if

• the way in which people were allocated was not truly random
• a lot more people from one group dropped out during the study for some reason
• if there was a lot of cross-over.

Cross-over is where high numbers of people ended up having the opposite treatment or care to the one they were intended to have. Some cross-over in RCT is expected. For example, in an RCT on planned caesareans it's very likely that some women allocated to a planned caesarean will go into labour before it can be done, and some who are in the planned vaginal birth group will decide to have a caesarean because a concern has arisen before their labour started. For the results to remain valid this cross-over needs to remain low.

In research on induction of labour it is quite common for a high proportion of those allocated to the expectant management (waiting for labour) group to have their labours induced because they have reached a pre-set deadline for the birth to take place or there is a concern over their or their baby's well-being. Similarly, some of those allocated to the induction group may go into labour before the induction is started. This can reduce the reliability of the findings.

Low recruitment rates

It is often not possible t o recruit a large enough sample to be able to measure a difference in outcomes. For example, to detect a difference in very rare occurrences such as stillbirth it has been estimated that it would be necessary to include between 16,000 and 30,000 pregnancies in the trial, and this is not usually possible in practice. 2

What is a meta-analysis?

One way around the problem of recruiting a large enough sample is a ’meta-analysis', a type of review which combines the data from multiple studies and uses statistical methods to analyse it. Effectively, a group of studies are analysed as if they were all part of one big study, but there are problems with this approach.

A meta-analysis can only be as good as the trials that go into it, and the results can vary according to which trials the authors choose to include. It is also difficult for a meta-analysis to compare the results of RCTs if they were carried out in different ways or using different methods, or if some important outcomes were not reported in all the studies.

The authors of such reviews will usually comment on the quality of the studies that they have selected for inclusion, and on the overall quality of the evidence available. The typical rating is High/Moderate/Low/Very Low where ‘High’ means that the authors are very confident of having detected a real effect and ‘Very Low’ means they are not at all confident. 3

One of the best-known sources of meta-analyses for all kinds of medical questions is the global Cochrane network. Their approach is explained here www.cochrane.org/about-us .

What are observational or population studies?

These are studies which observe how the outcomes in real life situations differ between groups defined by one or more characteristics or by a difference in the treatment that they receive. They are also sometimes called cohort studies. There is no random allocation of people to the different groups in this type of study, so they are usually considered to provide poorer quality evidence than an RCT. Nevertheless, they can provide useful information, especially on subjects where large, good-quality RCTs are lacking.

Mostly these are retrospective studies which look back at the records of a population, often over a period of years, and try to identify whether there were certain groups who were more likely than others to experience a given outcome. Alternatively, the investigators may look at how outcomes differed before and after a change in standard care procedures or else compare outcomes according to differences in characteristics such as age or the presence of a health condition.

There are also prospective studies which define the groups to be studied at the start of the research and then follow up what happens to those who fall within these groups, for example, those that do and don’t have their labours induced.

The advantages of this type of study are that they can often involve larger samples than RCTs are able to recruit, and they are looking at what happened in real life. In some cases, especially where it would be impractical or unethical to do an RCT they may provide the only evidence we have.

The disadvantages are that the records that are used are often incomplete, and because there is no randomisation there could be important differences between the groups or in the ways in which their labours were managed which are often not identified but could have a major impact on the results.

What is “Statistical significance” and why does it matter?

Studies can report their findings in different ways, but the authors should always use some sort of statistical analysis to check the probability that their results reflect a real difference. If it is highly unlikely that the finding occurred by chance, this is usually described as being a “statistically significant” result. Note that this is a technical use of the word “significant”. It is not saying anything about the relevance or importance of the finding.

Most medical studies use a significance level of 5%. This means that there’s a 95% chance that the finding is real, but still a 5% chance that it is not real. Another way to look at this is that there is a one in 20 (5%) chance that a result found by a study is not a real effect, so a recommendation that is based on just one result in one study may not be reliable.

It is usual to report the 95% confidence intervals for a research finding. This tells you the range of values within which there is a 95% chance that the true value lies. In other words, if an RCT finds that something is 2.5 times more common in one situation than in another, it might report this Relative Risk (RR) as RR 2.5 (95%CI 1.6 to 3.2). That means that the most likely value for the RR is 2.5, and there is a 95% chance that the true value lies somewhere between 1.6 and 3.2. This means we can be fairly confident that the outcome being measured really is more common in one situation than in the other. We can also be fairly confident that it is between 1.6 and 3.2 times more common. However, there is a 5% chance that the true value is outside these limits, (meaning that there is a 2.5% chance (one in 40) that the RR is less than 1.6, and a 2.5% chance that it is greater than 3.2).

If both the upper and lower confidence intervals are less than one, then this also indicates that the result is statistically significant, but that there is a reduction rather than an increase in the risk.

For a result to be statistically significant both figures must be greater than one or both less than one. This indicates that it is likely that the study has identified a real effect. If the lower number is less than one (which implies that the effect is to reduce the risk) and the upper figure is greater than one (which implies that it increases the risk) the result is not statistically significant. The study does not show the whether the risk is reduced or increased in one situation compared to the other.

When the numbers in the confidence interval are very different (but either both greater than one or both less than one) this is referred to as having a wide confidence interval. This means we can be confident that there is an effect, but not very sure how big the effect is. We can still say what the most likely value is, but the true value could be very different.

Sometimes when a study did not produce the result that was expected (or hoped for) authors may make more of non-significant results than they should. The sort of phrases to watch out for are things like “there was a trend towards x” or “the findings were borderline significant” or “there was an increased/decreased chance of x, but this did not achieve significance”. What all these phrases mean is that we don’t know whether the effect is real or not, and there is a high chance that the effect was not a real, because the finding was not statistically significant.

Critiquing research

Research papers, especially if they are likely to have a major impact on clinical practice, will often be ‘critiqued’ by other experts in the field. This means reviewing the strengths and limitations of the research and deciding whether the conclusions can be relied on. A critique of an RCT will usually include analysis of things like:

• Whether the research question was clearly defined, and appropriate outcomes reported
• How good the random allocation of people to the different groups was, and whether it resulted groups with similar characteristics (e.g. age, education level etc.)
• Whether there were any differences in the care given to each group, apart from the treatment being investigated
• How much cross-over there was between group
• Whether a large proportion of those enrolled in the study dropped out along the way
• The size of effect, statistical significance and confidence intervals for each outcome reported

If you want to get a feel for how reliable a piece of research is, it is worth looking to see whether a critique has been published. Examples of this include “Routine induction of labour at 41 weeks: nonsensus consensus” 1 and “Parsing the ARRIVE Trial: Should First-Time Parents Be Routinely Induced at 39 Weeks?” 4

An example of tools to use if you want to try critiquing different types of research yourself can be found here casp-uk.net/casp-tools-checklists

• Menticoglou S.M. and Hall S.F. “Routine induction of labour at 41 weeks: nonsensus consensus” BJOG 109 (5) May 2002 pp485-491 obgyn. onlinelibrary.wiley.com/doi/abs/10.1111/j.1471-0528.2002.01004.x
• Mandruzzato G. et al “Guidelines for the management of postterm pregnancy.” J Perinat Med. 2010 Mar;38(2):111-9 www.ncbi.nlm.nih.gov/pubmed/20156009/
• Siemieniuk R. and Guyatt G. “What is GRADE?” BMJ Best Practice bestpractice.bmj.com/info/toolkit/learn-ebm/what-is-grade/
• Goer H. “Parsing the ARRIVE Trial: Should First-Time Parents Be Routinely Induced at 39 Weeks?” 2018 www.lamaze.org/Connecting-the-Dots/parsing-the-arrive-trial-should-first-time-parents-be-routinely-induced-at-39-weeks

Written by: Nadia Higson Reviewed by: Debbie Chippington Derrick Reviewed on: 13/12/2023 Next review needed: 13/12/2025

AIMS does not give medical advice. Our website provides evidence-based information to support informed decision-making. The AIMS Helpline volunteers will be happy to provide further information and support. Please email [email protected] or ring 0300 365 0663.

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Reference management. Clean and simple.

Qualitative vs. quantitative research - what’s the difference?

What is quantitative research?

What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.

Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.

Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.

With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?

With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.

In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.

Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.

Some methods to collect this data include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
• Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
• Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
• Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
• Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
• Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.

The easiest way to describe qualitative research is that it answers the question " why ".

Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.

Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.

Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.

We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.

Some of these data collection methods include:

• Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
• Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
• Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
• Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
• Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
• Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.

Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:

• Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
• Qualitative research allows you to understand concepts or experiences.

Let's look at how you'll use these approaches in a research project a bit closer:

• Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
• Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
• Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
• Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.

Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.

This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.

So, to answer the initial question, the approach you use is up to you.  However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.

No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.

Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.

To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.

Considering the above, you can use tools, applications, and programming languages like R to calculate:

• The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
• The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
• Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
• The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.

Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.

Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.

Some of the most common ways to analyze qualitative data include:

• Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️  Learn how to do a content analysis in the guide.
• Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️  Learn how to do a thematic analysis in this guide.
• Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.

We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

3 examples of qualitative research would be:

• Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
• Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.

3 examples of quantitative research include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.

The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".

The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".