this characteristic of quantitative research which refers to its

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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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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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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.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

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

01. Data Collection Methodologies: Sampling Methods

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

Probability sampling

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

There are four main types of probability sampling:

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

Non-probability sampling

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

There are five non-probability sampling models:

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

02. Data collection methodologies: Using surveys & polls

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

Using surveys for primary quantitative research

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

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

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

Use of different question types

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

Survey Distribution and Survey Data Collection

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

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

Survey example

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

Using polls for primary quantitative research

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

C. Data Analysis Techniques

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

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

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

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

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

Secondary Quantitative Research Methods

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

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

The following are five popularly used secondary quantitative research methods:

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

Quantitative Research Examples

Some examples of quantitative research are:

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

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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3.1 What is Quantitative Research?

Quantitative research is a research method that uses numerical data and statistical analysis to study phenomena. 1 Quantitative research plays an important role in scientific inquiry by providing a rigorous, objective, systematic process using numerical data to test relationships and examine cause-and-effect associations between variables. 1, 2 The goal is to make generalisations about a population (extrapolate findings from the sample to the general population). 2 The data and variables are predetermined and measured as consistently and accurately as possible, and statistical analysis is used to evaluate the outcomes. 2 Quantitative research is based on the scientific method, wherein deductive reductionist reasoning is used to formulate hypotheses about a particular phenomenon.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Quantitative research: Definition, characteristics, benefits, limitations, and best practices

Quantitative research characteristics

Benefits and limitations, best practices for quantitative research.

Researchers use different research methods as research is carried out for various purposes. Two main forms of research, qualitative and quantitative, are widely used in different fields. While qualitative research involves using non-numeric data, quantitative research is the opposite and utilizes non-numeric data. Although quantitative research data may not offer deeper insights into the issue, it is the best practice in some instances, especially if you need to collect data from a large sample group. Quantitative research is used in various fields, including sociology, politics, psychology, healthcare, education, economics, and marketing.

Earl R. Babbie notes: "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."

Below are some of the characteristics of quantitative research.

Large sample size

The ability to use larger sample sizes is undoubtedly one of the biggest perks of quantitative research.

Measurability

Due to its quantitative nature, the data gathered through quantitative data collection methods is easily measurable.

Close-ended questions

Quantitative research utilizes close-ended questions, which can be both beneficial and disadvantageous.

Reusability

Since it doesn't involve open-ended questions, quantitative research results can be used in other similar research projects.

Reliability

Quantitative data is considered more reliable since it is usually free of researcher bias.

Generalization

Quantitative research uses larger sample sizes, so it is assumed that it can be generalized easily.

Since quantitative research relies on data that can be measured, there are a lot of benefits offered by quantitative methods.

Quantitative research benefits

• Easier to analyze

Analyzing numeric data is easier; in that context, quantitative research can bring large amounts of data in a short period. There is numerous quantitative data analysis software that lets the researcher analyze the data fast.

• Allows using large sample sizes

Quantitative research involves using close-ended questions or simple "yes and no" questions. Therefore, it is easier to analyze quantitative data. In that sense, it can be distributed to practically as many people as you can. A large sample size usually means more accurate research results.

• More engaging

As quantitative research questions don't feature open-ended questions, participants are more eager to respond to questions. With open-ended qualitative questions, participants sometimes need to write a wall of text, and that is undesirable for many of them. It is easier to choose "yes or no" as it doesn't require much effort. A more engaging research survey means more feedback.  

• Less biased and more accurate

Qualitative research uses open-ended questions, and since the feedback is often open to interpretation, researchers might be biased when analyzing the data. That is not the case with quantitative research, as it involves answers to preset questions. Less biased data means more accurate data.

• Needs less time and effort

In all stages of research, quantitative research requires much less time and effort when compared with qualitative research. With different software, it is possible to create, send and analyze a huge volume of quantitative data in just a few clicks. Unlike qualitative in-depth interviews that usually require participants to be in a specific office, quantitative research isn't geographically bound to any location and can be carried out online.

Quantitative research limitations

• Limited information on the subject - 

Using close-ended questions means there isn't much to interpret. It doesn't allow the researcher to get answers to "why" questions. If you want to get in-depth information on the subject, you need to carry out qualitative research.

• Can be costly

Although it allows the researcher to reach a higher sample size, finding a large number of participants is expensive, considering you have to pay each participant.

• Difficulty in confirming the feedback

Quantitative research doesn't usually involve observing participants or talking with them about their answers; therefore, it is difficult to guess if the data gathered from them is accurate all the time. With qualitative methods, you get a chance to observe participants and ask follow-up questions to confirm their answers.

What kind of research do you need?

It may sound too obvious, but you may want to think about the type of research you need to carry out before you start with one. Sometimes quantitative research is not the best practice for a given subject, and you may need to go with qualitative research.  

Clear research goals

Setting a research goal is the first thing every researcher does before setting out to carry out actual research. The success of the research hugely depends on the clearly defined research goals. In other words, it's a make or break point for most research projects. Having confusing research goals is what usually fails the entire project and results in a loss of time and money.

Use user-friendly structure

When creating your surveys and questionnaires, use a user-friendly layout and keep it simple, so it's more engaging for the users. A lot of software offers simple survey templates that you can use effectively.

Choose the right sample

Although quantitative research allows the research to use large sample sizes, it is essential to choose the right sample group. The sample group you're trying to get feedback from may not represent your target audience. Therefore, think twice before allocating resources to gathering data from them.

Pay attention to questions

Quantitative research uses closed-ended questions, which means you need to be very careful with the questions you choose. One of the benefits of quantitative research is that it gives you the ability to predetermine the questions, so you need to use this chance and think about the best possible questions you may use for a better result. With quantitative research questions, you usually don't get a chance to ask follow-up questions.

Let your bias out of the research

We already mentioned that quantitative research is less biased than qualitative research, but it doesn't mean that it's completely free of bias. In this form of research, bias comes with specifically designed questions. The researcher may frame the questions in a way that the feedback may reflect what the researcher wants. In that sense, it is important to leave all the biased questions out you feel can alter the end result of the research.

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What Are The Characteristics Of Quantitative Research? Characteristics Of Quantitative Research In A Nutshell

The characteristics of quantitative research contribute to methods that use statistics as the basis for making generalizations about something. These generalizations are constructed from data that is used to find patterns and averages and test causal relationships.

To assist in this process, key quantitative research characteristics include:

• The use of measurable variables.
• Standardized research instruments.
• Random sampling of participants.
• Data presentation in tables, graphs, or figures.
• The use of a repeatable method.
• The ability to predict outcomes and causal relationships.
• Close-ended questioning. 

Each characteristic also discriminates quantitative research from qualitative research, which involves the collecting and analyzing of non-numerical data such as text, video, or audio.

With that said, let’s now take a look at each of the characteristics in more detail.

But let’s first look at the importance of quantitative research and when it does matter!

Table of Contents

Importance of quantitative research

In the context of a business that wants to learn more about its market, customers, or competitors, quantitative research is a powerful tool that provides objective, data-based insights, trends, predictions, and patterns.

To clarify the importance of quantitative research as a method, we’ll discuss some of its key benefits to businesses below.

Before a company can develop a marketing strategy or even a single campaign, it must perform research to either confirm or deny a hypothesis it has around an ideal buyer or the target audience.

Before the proliferation of the internet, quantitative data collection was more cumbersome, less exhaustive, and normally occurred face to face.

Today, the ease with which companies can perform quantitative research is impressive – so much so that some would hesitate to even call it research.

Many businesses conduct questionnaires and surveys to have more control over how they test hypotheses, but any business with a Google Analytics account can passively collect data on key metrics such as bounce rate, discovery keywords, and value per visit.

The key thing to remember here is that there is little scope for uncertainty among the research data. Questionnaires ask closed-ended questions with no room for ambiguity and the validity of bounce rate data will never be up for debate.

Objective representation

Fundamentally speaking, quantitative research endeavors to establish the strength or significance of causal relationships.

There is an emphasis on objective measurement based on numerical, statistical, and mathematical data analysis or manipulation.

Quantitative research is also used to produce unbiased, logical, and statistical results that are representative of the population from which the sample is drawn.

In a marketer’s case, the population is usually the target audience of a product or service.

But in any case, organizations are dependent on quantitative data as it provides detailed, accurate, and relevant information on the problem at hand.

When it comes time to either prove or disprove the hypothesis, companies can either move forward with robust data or drop their current line of research and start afresh.

Versatility of quantitative statistical analysis

On the subject of proving a hypothesis are the statistical analyses a business must perform to arrive at the answer.

Fortunately, there are numerous techniques a company can employ depending on the context and the goals of the research. 

These include:

Conjoint analysis

Used to identify the value of attributes that influence purchase decisions, such as cost, benefits, or features.

Unsurprisingly, this analysis is used in product pricing, product launch, and market placement initiatives.

GAP analysis

An analysis that determines the discrepancy that exists between the actual and desired performance of a product or service.

MaxDiff analysis

A simpler version of the conjoint analysis that marketers use to analyze customer preferences related to brand image, preferences, activities, and also product features.

This is also known as “best-worst” scaling.

TURF analysis

TURF, which stands for total unduplicated reach and frequency, is used to ascertain the particular combination of products and services that will yield the highest number of sales.

The use of measurable variables

During quantitative research, data gathering instruments measure various characteristics of a population. 

These characteristics, which are called measurables in a study, may include age, economic status, or the number of dependents.

Standardized research instruments

Standardized and pre-tested data collection instruments include questionnaires, surveys, and polls. Alternatively, existing statistical data may be manipulated using computational techniques to yield new insights.

Standardization of research instruments ensures the data is accurate, valid, and reliable. Instruments should also be tested first to determine if study participant responses satisfy the intent of the research or its objectives.

Random sampling of participants

Quantitative data analysis assumes a normal distribution curve from a large population. 

Random sampling should be used to gather data, a technique in which each sample has an equal probability of being chosen. Randomly chosen samples are unbiased and are important in making statistical inferences and conclusions.

Here are a few random sampling techniques.

True random sampling

Some consider true random sampling to be the gold standard when it comes to probabilistic studies. While it may not be useful in every situation or context, it is one of the most useful for enormous databases.

The method involves assigning numbers to a population of available study participants and then having a random number generator select them. This ensures that each individual in a study pool has an equal chance of being solicited for feedback.

Systematic sampling

Systematic sampling is similar to true random sampling but is more suited to smaller populations. In this technique, the sample is selected by randomly choosing a starting point in the population and then selecting every n th individual after that. 

For example, if a researcher wanted to sample every twentieth person from a list of customers, they would randomly select one customer as the starting point and then sample every twentieth customer thereafter.

Cluster sampling

In cluster sampling, the population is divided into clusters or groups and a random sample of clusters is selected. After which, all members of the selected clusters are included in the sample. 

If a HR team wanted to survey employees of a large organization, they might randomly select several departments as clusters, and then survey all the employees within those departments.

Cluster sampling can also be useful for businesses that have customers or products distributed over wide geographic areas.

To that end, cluster sampling is often used when the population is too large or too dispersed to sample individually. While it may be more efficient to sample clusters, the approach may be less precise if there is variability between them.

Data presentation in tables, graphs, and figures

The results of quantitative research can sometimes be difficult to decipher, particularly for those not involved in the research process.

Tables, graphs, and figures help synthesize the data in a way that is understandable for key stakeholders. They should demonstrate or define relationships, trends, or differences in the data presented.

Take McKinsey Global Institute (MGI), for example, the business and research arm of McKinsey & Company.

Established in 1990, MGI combines the disciplines of economics and management to examine the macroeconomic forces that influence business strategy and public policy. 

Based on this analysis , MGI periodically releases reports covering more than 20 countries and 30 industries around six key themes: natural resources, labor markets, productivity and growth , the evolution of global financial markets, the economic impact of technology and innovation , and urbanization.

MGI’s mission is to “ provide leaders in the commercial, public, and social sectors with the facts and insights on which to base management and policy decisions .” To carry out this mission , McKinsey’s data presentation is key. 

In one article that argued against the deglobalization trend , McKinsey skilfully used graphs and bar charts to synthesize quantitative data related to the global flow of intangibles, services, and students.

The company also used an 80-cell matrix and color-coded scale to show the share of domestic consumption met by inflows for various geographic regions.

The use of a repeatable method

Quantitative research methods should be repeatable.

This means the method can be applied by other researchers in a different context to verify or confirm a particular outcome.

Replicable research outcomes afford researchers greater confidence in the results. Replicability also reduces the chances that the research will be influenced by selection biases and confounding variables.

The ability to predict outcomes and causal relationships

Data analysis can be used to create formulas that predict outcomes and investigate causal relationships.

As hinted at earlier, data are also used to make broad or general inferences about a large population.

Causal relationships, in particular, can be described by so-called “if-then” scenarios, which can be modeled using complex, computer-driven mathematical functions.

Close-ended questioning

Lastly, quantitative research requires that the individuals running the study choose their questions wisely.

Since the study is based on quantitative data, it is imperative close-ended questions are asked.

These are questions that can only be answered by selecting from a limited number of options. 

Questions may be dichotomous, with a simple “yes” or “no” or “true” or “false” answer.

However, many studies also incorporate multiple-choice questions based on a rating scale, Likert scale, checklist, or order ranking system.

Sample size

Sample size is a critical consideration in quantitative research as it impacts the reliability of the results.

In business quantitative research, sample size refers to the number of participants or data points included in a study, and it is vital that the sample size is appropriate for the research questions being addressed.

A sample size that is too small can lead to unreliable conclusions since it will not accurately represent the study population.

Conversely, a sample size that is too large can lead to unnecessary expenses and time constraints.

In general, however, larger sample sizes tend to increase the precision and reliability of study conclusions.

This is because they reduce the impact of random variation and increase the power to detect statistically significant differences or relationships. However, larger sample sizes also require more resources and time to collect and analyze data.

As a consequence, it is important for businesses to select a sample size that balances factors such as the research question, population size, variability of the data, and statistical power.

Four real-world examples of quantitative research

Now that we’ve described some key quantitative research examples, let’s go ahead and look at some real-world examples.

1 – A Quantitative Study of the Impact of Social Media Reviews on Brand Perception

In 2015, Neha Joshi undertook quantitative research as part of her thesis at The City University of New York.

The thesis aimed to determine the impact of social media reviews on brand perception with a particular focus on YouTube and Yelp.

Joshi analyzed the impact of 942 separate YouTube smartphone reviews to develop a statistical model to predict audience response and engagement on any given video.

The wider implications of the study involved using customer reviews as a feedback mechanism to improve brand perception.

2 – A Quantitative Study of Teacher Perceptions of Professional Learning Communities’ Context, Process, and Content

Daniel R. Johnson from Seton Hall University in New Jersey, USA, analyzed the effectiveness of the teacher training model known as Professional Learning Communities (PLC).

Specifically, Johnson wanted to research the impact of the model as perceived by certified educators across three specific areas: content, process, and context.

There was a dire need for this research since there was little quantitative data on an approach that was becoming increasingly popular at the government, state, and district levels.

Data were collected using Standard Inventory Assessment (SAI) surveys which were online, anonymous, and incorporated a Likert scale response system.

3 – A Quantitative Study of Course Grades and Retention Comparing Online and Face-to-Face Classes

This research was performed by Vickie A. Kelly as part of her Doctor of Education in Educational Leadership at Baker University in Kansas, USA.

Kelly wanted to know whether distance education and Internet-driven instruction were as effective a learning tool when compared to traditional face-to-face instruction.

A total of 885 students were selected for the research sample to answer the following two questions:

• Is there a statistically significant difference between the grades of face-to-face students and the grades of online students?
• Is there a statistically significant difference between course content retention in face-to-face students and online students?

In both cases, there was no significant difference, which suggested that distance education as a learning tool was as effective as face-to-face education.

4 – A quantitative research of consumer’s attitude towards food products advertising

At the University of Bucharest, Romania, Mirela-Cristina Voicu wanted to research consumer attitudes toward traditional forms of advertising such as television, radio, and print.

She reasoned that consumer attitudes toward advertising impacted attitudes toward the product or brand itself, with a positive attitude potentially driving purchase intent.

To determine whether there was a link between these factors, 385 consumers in the Bucharest area were interviewed and asked to fill out a questionnaire.

Voicu ensured the sample was representative of the broader population in terms of two variables: age and gender.

The quantitative study results found that 70% of participants considered traditional forms of advertising to be saturated.

In other words, they did not have a positive attitude toward the advertised brand or product.

However, consumer attitudes toward  food advertising  were much more positive, with 61% of participants categorizing their attitudes as either favorable or very favorable in the questionnaire. 

Quantitative vs. Qualitative Research

As the story goes, “data is the new oil,” yes, but what data?

Indeed, while quantitative research can be extremely powerful, it must be balanced with qualitative research .

Several qualitative methods might help enrich the quantitative data.

It’s critical to understand that quantitative data might be very effective in the short term.

Yet, it might not tell us anything in the long term.

For that, we need to use human judgment, intuition, and understanding of context.

In what we can label as second-order thinking .

Only by building qualitative understanding within quantitative methods combined with second-order effect thinking; can you leverage the best of the two worlds!

For instance, take the interesting case of how Amazon has integrated both quantitative and qualitative data into its business strategy .

This is part of Jeff Bezos’ “Day One” Mindset .

That enabled Amazon to understand when it makes sense to leverage quantitative vs. qualitative data .

As  Jeff Bezos explained in 2006:

“ Many of the important decisions we make at Amazon.com can be made with data. There is a right answer or a wrong answer, a better answer or a worse answer, and math tells us which is which. These are our favorite kinds of decisions.”

As our shareholders know, we have made a decision to continuously and significantly lower prices for customers year after year as our efficiency and scale make it possible.

Indeed, this was the core tenet of Amazon’s flywheel .

And Jeff Bezos also explained:

This is an example of a very important decision that cannot be made in a math-based way. In fact, when we lower prices, we go against the math that we can do, which always says that the smart move is to raise prices.

Indeed, as Jeff Bezos further explained:

We have significant data related to price elasticity. With fair accuracy, we can predict that a price reduction of a certain percentage will result in an increase in units sold of a certain percentage. With rare exceptions, the volume increase in the short term is never enough to pay for the price decrease. 

In short, optimization tools leveraging quantitative analysis are quire effective in the short-term and relation to first-order effects activities.

However, in many cases, that doesn’t tell you anything when it comes to its second-order long-term consequences!

Jeff Bezos explained that extremely well:

However, our quantitative understanding of elasticity is short-term. We can estimate what a price reduction will do this week and this quarter. But we cannot numerically estimate the effect that consistently lowering prices will have on our  business  over five years or ten years or more. 

And he introduced the difference between quantitative data vs. human judgment, which is a qualitative measure!

Our judgment is that relentlessly returning efficiency improvements and scale economies to customers in the form of lower prices creates a virtuous cycle that leads over the long term to a much larger dollar amount of free  cash  flow, and thereby to a much more valuable Amazon.com.

He highlighted how long-term, unpredictable and counterintuitive bets were the result of human judgement:

We’ve made similar judgments around Free Super Saver Shipping and Amazon Prime, both of which are expensive in the short term and—we believe—important and valuable in the long term.

Quantitative research examples 

There is a lot of discussion around the ideal length of social media posts online, and much of it is anecdotal or pure conjecture at best.

To cut through the noise and arrive at data-driven conclusions, brand building platform Buffer teamed up with analytics software company SumAll.

In this example, the research involved tabulating and quantifying social media engagement as a factor of post length.

Posts encompassed a variety of social media updates, such as tweets, blog posts, Facebook posts, and headlines. The study determined:

• The optimal width of a paragraph (140 characters).
• The optimal length of a domain name (8 characters).
• The optimal length of a hashtag (6 characters).
• The optimal length of an email subject (28 to 39 characters), and
• The optimal duration of a podcast (22 minutes) and YouTube video (3 minutes).

Where SumAll sourced its quantitative data varied according to the type of social media post.

To determine the optimal width of a paragraph, the company referenced social media guru Derek Halpern who himself analyzed data from two separate academic studies.

To determine the optimal length of an email subject line, SumAll referenced a 2012 study by Mailer Mailer that analyzed 1.2 billion email messages to identify trends.

Tallwave is a customer experience design company that performs quantitative research for clients and identifies potential trends. 

In the wake of COVID-19, the company wanted to know whether consumer trends the pandemic spurred would continue after restrictions were lifted.

These trends included buy online, pick-up in-store (BOPIS), and blended, cook-at-home restaurant meals. 

Tallwave also wanted to learn more about consumer expectations around branded communication.

In a post-pandemic world, were health and safety precautions more important than the inconvenience they caused?

Would customers abandon digital experiences and flock back to brick-and-mortar stores? Indeed, was it wise to continue to invest in infrastructure the customer didn’t want?

To collect quantitative data, Tallwave surveyed 1,010 individuals across the United States aged 24 and over in April 2021.

Consumers were asked various questions on their behaviors, perceptions, and needs pre and post-pandemic. 

The company found that while customer behavior did change as a result of COVID-19, it had not changed to the extent predicted. Some of the key findings include:

• Convenience trumps all – while many brands continued to focus on health and safety, customers still value convenience above all else. Safety-related needs were the next most important for all age brackets (except Gen Z).
• The role of digital experiences – most survey participants who used a company’s digital experience viewed that company more favorably. This proved that in a post-COVID world, the flexibility for consumers to choose their own “adventure” is paramount.
• The accessibility of digital experiences – the survey data also showed that interest in digital experiences declined with age starting with the 45-54 year bracket. Since 66% of those aged 55 and older reported no desire to continue with online experiences after COVID-19, Tallwave argued that increasing digital literacy would drive greater adoption and engagement over the long term.

Additional Case Studies

Examples of Business Scenarios Using Quantitative Research :

• A company launching a new product conducts surveys to identify which age group is most interested in their product.
• A retail store uses conjoint analysis to determine the optimal price point for a new item.
• A beverage company tests various flavors and uses rating scales to determine which new flavor to launch.
• An e-commerce site analyzes click-through rates to optimize the layout of their product pages.
• A startup uses surveys to measure how many consumers are aware of their brand after a marketing campaign.
• A company conducts an online poll to gauge the effectiveness of their recent TV commercial.
• A tech firm analyzes past sales data to predict the number of units they will sell in the next quarter.
• A corporation uses standardized questionnaires to gauge employee satisfaction and identify areas of improvement.
• A manufacturing company analyzes lead times and delivery speeds to optimize their supply chain processes.
• A retail chain reviews sales data to determine the optimal shelf placement for products to maximize sales.
• An airline analyzes frequent flyer data to understand patterns and introduce loyalty rewards.
• A financial institution uses quantitative analysis to predict stock market trends.
• A supermarket uses sales data to understand which products sell best during promotional events.
• A restaurant reviews time-tracking data to optimize shift schedules during peak hours.
• A software company uses surveys to gather feedback on a new feature they’ve introduced.
• Businesses analyze macroeconomic indicators to forecast market conditions.
• Retailers review sales and inventory data to predict restocking needs.
• A hotel chain uses quantitative research to determine the best locations for new hotels based on travel and occupancy data.
• A company reviews market share data to understand their position relative to competitors.
• A service-based company analyzes call center data to reduce wait times and improve customer service.

Key takeaways

• The characteristics of quantitative research contribute to methods that use statistics as the basis for making generalizations about something.
• In a quantitative study, measurable variables are analyzed using standardized research instruments. Importantly, data must be sampled randomly from a large, representative population to avoid biases.
• Quantitative research data should also be presented in tables and graphs to make key findings more digestible for non-technical stakeholders. Methods must also be repeatable in different contexts to ensure greater outcome confidence and validity.

Key Highlights of Quantitative Research Characteristics:

• Quantitative research uses statistics to make generalizations based on measurable variables.
• Standardized research instruments like questionnaires and surveys are used for data collection.
• Random sampling of participants ensures unbiased results from a larger population.
• Data is presented in tables, graphs, or figures for better understanding.
• The research method is repeatable for verification and validity.
• It allows for predicting outcomes and causal relationships.
• Close-ended questioning is used to gather specific and structured responses.

Importance of Quantitative Research:

• Provides objective, data-based insights, trends, predictions, and patterns for businesses.
• Helps in developing marketing strategies and understanding the target audience.
• Focuses on objective measurement and producing unbiased results.
• Offers versatility in statistical analysis techniques for various research goals.

Real-world Examples of Quantitative Research:

• Impact of Social Media Reviews on Brand Perception.
• Teacher Perceptions of Professional Learning Communities.
• Comparison of Course Grades and Retention in Online vs. Face-to-Face Classes.
• Consumer Attitudes Towards Food Product Advertising.

Qualitative vs. Quantitative Research:

• Qualitative research involves non-numerical data and focuses on understanding human behavior and attitudes.
• Quantitative research relies on measurable variables and statistics to make broad inferences.
• The combination of both methods allows for a comprehensive understanding of complex phenomena.

Sample Size Considerations:

• The sample size is critical in quantitative research to ensure reliable results.
• Larger sample sizes increase precision and reduce the impact of random variation.
• Properly balanced sample sizes are essential for valid and statistically significant conclusions.

Main Points

• Involves statistical analysis for making generalizations based on measurable variables.
• Uses standardized research instruments like surveys and questionnaires.
• Requires random sampling for unbiased representation from a larger population.
• Presents data through tables, graphs, or figures for visualization.
• Should follow a repeatable method for validation and reliability.
• Enables prediction of outcomes and identification of causal relationships.
• Utilizes close-ended questions to gather specific responses.
• Offers data-driven insights, patterns, trends, and predictions.
• Informs business strategies, marketing decisions, and audience understanding.
• Provides objective measurement and representation of trends.
• Enables informed decision-making through statistical analysis .
• Examines social media impact on brand perception.
• Investigates teacher perceptions of professional learning communities.
• Compares online and face-to-face class effectiveness.
• Studies consumer attitudes towards food product advertising.
• Qualitative research focuses on understanding human behavior through non-numerical data.
• Quantitative research emphasizes measurable variables and statistical analysis .
• Combining both methods offers a comprehensive understanding of complex phenomena.
• Sample size is crucial for reliable and accurate results.
• Larger samples enhance precision and reduce random variation impact.
• Balanced sample sizes ensure valid and statistically significant findings.

Read Also: Quantitative vs. Qualitative Research .

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Wilson, L.A. (2019). Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_54

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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

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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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Qualitative vs Quantitative Research Methods & Data Analysis

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

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

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

Marketing91

What are the Characteristics of Quantitative Research?

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

No matter whether you belong to a management field or science field, you must have come across quantitative research once in your life.  People quite often come across one or two surveys or questionnaires. The purpose of conducting these surveys is to draw reliable analytical conclusions to understand a particular audience.

Quantitative research is a popular choice of research, especially in industries. As companies want to know about the demand for the product in the market and preference of customers before making huge investments. With the help of quantitative research, one can learn about the wishes and needs of target groups.

Quantitative research is used to get answers in numerical form. The output of quantitative research is usually in the form of graphs, statistical data, tables, and percentages, etc. The purpose of using quantitative research is to make the use of mathematical and statistical models to establish an understanding of the problem of research.

The outcome of quantitative research is analyzed with the help of statistics to get unbiased results. The results of quantitative research can be generalised because it is conducted on a large population size. Different types of quantitative research can be undertaken to obtain the results. Click Here To learn more about the different types of quantitative research.

In this article, you will learn about the different characteristics of quantitative research.

Table of Contents

Characteristics of Quantitative Research

1. Large Sample Size

The first and foremost characteristic of quantitative research is the large sample size to conduct research. Quantitative research is done on a large number of audiences to ensure reliability. The sample size used in quantitative research represents the whole target market.

Because of this characteristic of quantitative research, most organizations prefer to invest in quantitative research rather than investing in qualitative research.

2. Structured Research Methods

Structural research methods like questionnaires, polls, and surveys are used to conduct quantitative research. In-depth information about the preferences of the audience can be drawn using these structured research methods.

Moreover, with the help of the Internet and social media, it is effortless to reach the vast population irrespective of geographical boundaries. You can pay to run your Survey ad on various social media platforms such as Facebook, YouTube, etc. and can collect the opinion of a large population.

In addition to this, these research methods are easy to conduct through offline means. Also, a participant is only required to fill out the questionnaire and is not required to be part of the research process for a long time.

3. Highly Reliable Outcome

The outcome of quantitative research methods is quite reliable, as participants of the research face close-ended questions. Therefore, there are fewer chances of getting vague information or wrong information from the participants.

Quantitative research methods are used for industrial research purposes because of its reliability. Moreover, the Outcome of quantitative research is easy to understand and explain.

The researchers present the outcome of the research to get the approval of the management, and management can understand this information quickly because it is represented in the form of tables and graphs.

4. Reusable Outcome

Another characteristic of quantitative research is that the outcome of quantitative research can be used multiple times. Data collected for one research purpose can be used for the prior study of another research problem.

Sometimes, researchers make the use of the outcome of research for similar research problems, which reduces the expense and time required for conducting research. For example, if you have researched how much money a man will be willing to pay to buy an electronic gadget.

You can use the output of the previous study to get a reliable answer to the research problem of how much a man will be willing to pay to buy an earphone. In this way, the outcome of the previous research problem is used to support the output of new but related research problems.

5. Close-ended questions

Another characteristic of quantitative research is close-ended questions. Close-ended question’s answers are more specific and right to the question than the open-ended questions. Responses to open-ended questions are more detailed and scattered, and it requires real skills to pick out the answers that you need.

Moreover, responses to Close-ended questions are more reliable than the answers to questions of open-ended questions. In addition to this, people also prefer to answer close-ended questions than open-ended questions for various reasons. The followings are a few examples of close-ended questions.

• How often do you shop online?
• How often do you pay for the subscription of a mobile app?
• How much are you ready to pay to buy a women’s health magazine?

The answer to the above questions would be in numerical form, which can be used to derive meaningful conclusions.

6. Numerical Outcome

The outcome of quantitative research is always in numerical form. For example, the result of research can be represented in percentage, range of numbers.

A numerical output is easy to read and understand, and it is easy to deduce a conclusion from the numerical outcome than a detailed result.

7. Generalization of Outcome

The outcome of quantitative research can be generalised easily for the whole population. The reason behind this is that quantitative research is conducted on a large sample of the population.

A decision can be taken for the entire population based on the outcome of the sample population.

8. Prior study

The outcome of quantitative research can be used for a previous study of another research. Many scholars and researchers study and analyze the outcome of previous research to establish their research hypothesis or research problem.

The results of quantitative research are more reliable than qualitative research and can be used easily to deduce conclusions.

Here is a video by Marketing91 on Quantitative Research.

Liked this post? Check out the complete series on Market research

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About Hitesh Bhasin

Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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