definition quantitative research method

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative research is a systematic investigation that primarily focuses on quantifying data, variables, and relationships. It involves the use of statistical, mathematical, and computational techniques to collect and analyze data. Quantitative research is often used to establish patterns, test hypotheses, and make predictions. It is widely applied in fields such as psychology, sociology, economics, health sciences, and education.

Quantitative Research

Quantitative research is a research approach that seeks to quantify data and generalize results from a sample to a larger population. It relies on structured data collection methods and employs statistical analysis to interpret results. This type of research is objective, and findings are typically presented in numerical form, allowing for comparison and generalization.

Key Characteristics of Quantitative Research :

Objective : Focuses on numbers and measurable variables rather than subjective opinions.
Structured : Employs well-defined research questions, hypotheses, and data collection methods.
Statistical : Utilizes statistical tools to analyze data and validate findings.
Replicable : Enables repetition of the study to verify results and increase reliability.

Example : A survey on the correlation between exercise frequency and stress levels among adults, using a Likert scale to measure responses.

Types of Quantitative Research

Quantitative research can be categorized into several types, each serving a specific purpose. The most common types include descriptive , correlational , experimental , and causal-comparative research.

1. Descriptive Research

Definition : Descriptive research describes characteristics or behaviors of a population without examining relationships or causes. It provides a snapshot of current conditions or attitudes.

Purpose : To gather information and create an overview of a particular phenomenon, population, or condition.

Example : A survey describing the demographics and academic performance of students at a university.

2. Correlational Research

Definition : Correlational research examines the relationship between two or more variables but does not imply causation. It analyzes patterns to determine if variables are associated or occur together.

Purpose : To identify associations or trends among variables without establishing cause and effect.

Example : Investigating the relationship between social media use and self-esteem among teenagers.

3. Experimental Research

Definition : Experimental research manipulates one or more independent variables to observe the effect on a dependent variable, establishing cause-and-effect relationships. This type of research involves control and experimental groups.

Purpose : To test hypotheses by isolating and controlling variables to establish causality.

Example : Testing the effect of a new medication on blood pressure by administering it to one group (experimental) and comparing it to a placebo group (control).

4. Causal-Comparative (Ex Post Facto) Research

Definition : Causal-comparative research investigates the cause-effect relationship between variables when experimental manipulation is not possible. It compares groups that differ on a particular variable to determine the effect of that variable.

Purpose : To explore cause-and-effect relationships retrospectively by comparing pre-existing groups.

Example : Studying the impact of different teaching methods on student performance by comparing classes taught with traditional versus technology-assisted instruction.

Quantitative Research Methods

Quantitative research methods focus on systematic data collection and analysis using structured techniques. Common methods include surveys , experiments , and observations .

Definition : Surveys are a popular quantitative method that involves asking participants standardized questions to collect data on their opinions, behaviors, or demographics. Surveys can be conducted via questionnaires, interviews, or online forms.

Purpose : To gather data from a large sample, allowing researchers to make inferences about the larger population.

Example : Conducting a survey to collect customer satisfaction data from a random sample of customers in a retail store.

Advantages :

Cost-effective and time-efficient for large sample sizes.
Provides structured data that is easy to analyze statistically.

Disadvantages :

Limited depth, as responses are often restricted to specific options.
Potential for response bias, where participants may not answer truthfully.

2. Experiments

Definition : Experiments involve manipulating one or more variables in a controlled environment to observe the effect on another variable. Experiments are often conducted in laboratories or controlled settings to maintain precision and limit external influences.

Purpose : To test hypotheses and establish cause-and-effect relationships.

Example : Conducting a laboratory experiment to test the effect of light exposure on sleep patterns.

High level of control over variables.
Establishes causality, which can support theory-building.
Limited external validity, as findings may not always apply outside of the controlled setting.
Ethical considerations may limit experimentation on certain subjects or groups.

3. Observations

Definition : Observational research involves systematically observing and recording behavior or events as they occur naturally, without interference. While often used in qualitative research, structured observational methods can yield quantitative data.

Purpose : To gather real-world data in a non-intrusive manner.

Example : Observing customer behavior in a store to track time spent in different areas and identify shopping patterns.

Provides data on actual behaviors rather than self-reported responses.
Useful for gathering data on situations where surveys or experiments may not be feasible.
Observer bias may affect results.
Can be time-consuming, especially if behaviors are infrequent or complex.

Data Collection Tools in Quantitative Research

Quantitative research relies on various tools to collect and quantify data, including:

Questionnaires : Standardized forms with close-ended questions, often using scales (e.g., Likert scale) for responses.
Tests and Assessments : Used to measure knowledge, skills, or other measurable attributes.
Digital Tracking Tools : Software or digital applications that collect data, such as website traffic metrics or physiological monitoring devices.

Data Analysis in Quantitative Research

Data analysis in quantitative research involves statistical techniques to interpret numerical data and determine relationships or trends. Key techniques include descriptive statistics , inferential statistics , and correlation analysis .

1. Descriptive Statistics

Definition : Descriptive statistics summarize and organize data, providing basic information such as mean, median, mode, standard deviation, and range.

Purpose : To give an overview of the dataset, allowing researchers to understand general trends and distributions.

Example : Calculating the average test scores of students in a school to assess overall performance.

Common Measures :

Mean : Average of all data points.
Median : Middle value of an ordered dataset.
Standard Deviation : Measure of variability around the mean.

2. Inferential Statistics

Definition : Inferential statistics allow researchers to make predictions or inferences about a population based on sample data. Techniques include hypothesis testing, t-tests, ANOVA, and regression analysis.

Purpose : To determine if observed results are statistically significant and can be generalized to a larger population.

Example : Using a t-test to compare average scores between two different teaching methods to see if one is significantly more effective.

Common Tests :

t-Test : Compares the means of two groups to determine if they are statistically different.
ANOVA (Analysis of Variance) : Compares means among three or more groups.
Regression Analysis : Examines the relationship between independent and dependent variables.

3. Correlation Analysis

Definition : Correlation analysis measures the strength and direction of the relationship between two variables. It is used to determine if changes in one variable are associated with changes in another.

Purpose : To identify associations between variables without implying causation.

Example : Calculating the correlation coefficient between screen time and academic performance to determine if there is an association.

Pearson Correlation Coefficient (r) : Measures linear correlation between two continuous variables.
Spearman’s Rank Correlation : Measures correlation between two ranked variables.

Advantages and Disadvantages of Quantitative Research

Objective : Minimizes researcher bias by focusing on numerical data.
Generalizable : Findings from large, random samples can often be applied to a broader population.
Replicable : Structured methods make it possible for other researchers to replicate studies and verify results.

Disadvantages

Limited Depth : Quantitative research often lacks the depth of qualitative insights.
Rigid Structure : Limited flexibility in data collection and analysis.
Potential Bias : Response or sampling biases can affect results, especially in survey-based studies.

Tips for Conducting Effective Quantitative Research

Define Clear Objectives : Develop specific research questions or hypotheses to guide the study.
Choose the Right Method : Select a quantitative method that aligns with the research goals and type of data needed.
Ensure Sample Representativeness : Use appropriate sampling techniques to ensure results can be generalized.
Employ Proper Statistical Tools : Choose analysis techniques that match the nature of the data and research questions.
Interpret Results Accurately : Avoid overgeneralizing findings and consider limitations when interpreting results.

Quantitative research provides a structured, objective approach to investigating research questions, allowing for statistical analysis, pattern recognition, and hypothesis testing. With methods like surveys, experiments, and observational studies, quantitative research offers valuable insights across diverse fields, from social sciences to healthcare. By applying rigorous statistical analysis, researchers can draw meaningful conclusions, contributing to the body of scientific knowledge and helping inform data-driven decisions.

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE Publications.
Punch, K. F. (2014). Introduction to Social Research: Quantitative and Qualitative Approaches (3rd ed.). SAGE Publications.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE Publications.
Trochim, W. M., & Donnelly, J. P. (2008). The Research Methods Knowledge Base (3rd ed.). Cengage Learning.
Babbie, E. R. (2021). The Practice of Social Research (15th ed.). Cengage Learning.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Applied Research – Types, Methods and Examples

Correlational Research – Methods, Types and...

Qualitative Research Methods

Quasi-Experimental Research Design – Types...

Case Study – Methods, Examples and Guide

Qualitative Research – Methods, Analysis Types...

Educational resources and simple solutions for your research journey

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

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !  