Quantitative research is a broad term used to refer to various statistical methods and techniques that are used in social sciences, natural sciences, and business research.
Quantitative research generally involves the collection and analysis of numerical data as opposed to qualitative research which focuses on analyzing verbal or textual responses.
Research can be qualitative (where we explore meanings and understandings) or quantitative (where we measure phenomena). In quantitative research, the validity of a measure is checked by statistical methods.
What is Quantitative Research?
Quantitative research refers to a broad range of data-driven methods designed to explore and quantify phenomena in a rigorous, replicable way.
Quantitative researchers use numbers and mathematical operations to describe and analyze the world in order to generate theory and testable hypotheses.
Furthermore, quantitative research is a process that generates data in the form of numbers through the use of surveys or experiments. The numbers generated from these processes can be used to support theories based on previous research.
Quantitative research is a systematic investigation of phenomena that gathers quantifiable data and performs statistical, mathematical, or computational procedures. Sampling methods, online surveys, online polls, questionnaires, and numerical counts generated by these methods are all used to gather quantifiable data.
Example of Quantitative Research
An example of quantitative research is the survey that investigates how long a doctor takes to care for a patient when he or she enters the hospital. It is also possible to use a patient satisfaction survey template to ask questions such as how long did a doctor take to see a patient, how frequently a patient enters the hospital, and so on.
Types of quantitative research
The types of quantitative research are:
Descriptive research
Descriptive research is used to describe a current state or phenomenon. For example, a researcher may want to describe the current state of sexual harassment in the workplace. –
Experimental research
Experimental research is used to test cause-and-effect relationships. For example, a researcher may want to test the effectiveness of a new drug to treat hypertension.
Correlational research
Correlational research is used to examine the relationship between two or more things. For example, a researcher may want to examine the relationship between age and job satisfaction. –
Survey research
Survey research is used to collect data from a large sample of people on a specific topic.
How to conduct quantitative research?
Before conducting a quantitative research, it is important to define the problem, choose a research method, collect and analyze data, and draw conclusions.
Problem definition – Before conducting a quantitative research, it is important to clearly define the problem. It is better to choose a problem that is simple and clearly understandable rather than a complicated one.
Research method selection – After defining the problem, the next important step is to choose a research method. There are various types of quantitative research methods such as descriptive research, experimental research, correlation research, etc.
Data collection – The next step is to collect data. There are various ways to collect data such as surveys, interviews, focus groups, and experiments.
Data analysis – The last step is to analyze the data obtained during the research.
Characteristics of Quantitative Research
Structured tools | A structured tool, such as a survey, poll, or questionnaire, can gather quantitative data. In addition to collecting detailed, actionable data from survey respondents, structured tools offer structure that helps in collecting data in depth. |
Sample size | Since proper sampling methods should be used to fortify the research objective in quantitive research, a large sample size is required. |
Close-ended questions. | Closed-ended questions, are created as per the objective of the research. These questions help collect quantitative data, and hence, are extensively used in quantitative research. |
Quantitative data | Quantitative researchers use numbers to describe and analyze phenomena and generate theory. |
Generalization of results | Results of a quantitative study are generalizable to a larger population because researchers use random samples and surveys to ensure that the sample is representative of the population. |
Theory | Quantitative researchers use data to test theories in order to generate new and more refined theories. |
Data-driven methods | Quantitative researchers generate data from surveys and experiments that can be used to support theories based on previous research. |
Rigor | Quantitative methods are designed to follow rigorous procedures that allow for consistent and accurate findings to be replicated by others. |
Hypothesis | A hypothesis is a statement describing a relationship or effect. It can be tested with data by seeing if the data supports the hypothesis. |
Validity | The validity of a measure is checked by statistical methods to determine if the data makes sense. |
Conclusions | Quantitative researchers use data to draw conclusions and generate theory. |
Pros of quantitative research
Gather precise data
The results will be very accurate because they are based on data that has been gathered, examined, and presented in numerical form. Numbers are reliable. They provide a clear, consistent, and incredibly accurate representation of the study that was really done.
Quick data collection
A population-representative sample of respondents is used in a quantitative study. It would be simple and quick to perform and analyze the findings of a survey or other quantitative research approach on this group of respondents, with minimal time commitment.
More extensive data analysis
This research method offers a broad range of data collection due to the statistics.
Eliminate bias
This research method excludes the possibility of biasing the findings or making personal remarks. The outcomes obtained are numerical and fair enough.
Cons of quantitative research
Rigidity
Researchers must carefully plan and follow a rigid set of procedures when performing a quantitative study
Might not tell the whole story
The variables you collect through quantitative methods, can be superficial or limited. For example, For instance, you can learn very little by just asking individuals how much coffee they consume. It can also be the case that other factors you are surveying affect the responses people give.
Sample sizes can be small
Small sample sizes can limit the impact research has. Asking 10 people about their coffee drinking habits won’t give you a good idea of how coffee consumption plays out across the country, for example.
Data can be over-manipulated.
Sample sizes can be small
It’s possible for the setting of a research study, to be manipulated, and controlled to such an extent that it affects the accuracy of the results or for a range of other, unaccounted-for variables to affect the study.
Conclusion
As mentioned, quantitative and qualitative methods have their own merits and demerits. However, it is important to note that a research method is not a research approach. In other words, a researcher cannot say that he/she uses quantitative or qualitative research methods. Instead, a researcher uses a specific research approach that is based on specific methods.
Thus, it is important to note that quantitative and qualitative research methodologies differ in terms of their goals, procedures, and type of information generated. While both of these research approaches have their own advantages and disadvantages, they are both necessary and important in social sciences, business, and natural sciences.
Other articles
Please read through some of our other articles with examples and explanations if you’d like to learn more.
Statistics
- PLS-SEM model
- Principal Components Analysis
- Multivariate Analysis
- Friedman Test
- Chi-Square Test (Χ²)
- T-test
- SPSS
- Effect Size
- Critical Values in Statistics
- Statistical Analysis
- Calculate the Sample Size for Randomized Controlled Trials
- Covariate in Statistics
- Avoid Common Mistakes in Statistics
- Standard Deviation
- Derivatives & Formulas
- Build a PLS-SEM model using AMOS
- Principal Components Analysis using SPSS
- Statistical Tools
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- One-tailed and Two-tailed Test
- Parametric and Non-Parametric Test
Citation Styles
Comparision
- Independent vs. Dependent Variable – MIM Learnovate
- Research Article and Research Paper
- Proposition and Hypothesis
- Principal Component Analysis and Partial Least Squares
- Academic Research vs Industry Research
- Clinical Research vs Lab Research
- Research Lab and Hospital Lab
- Thesis Statement and Research Question
- Quantitative Researchers vs. Quantitative Traders
- Premise, Hypothesis and Supposition
- Survey Vs Experiment
- Hypothesis and Theory
- Independent vs. Dependent Variable
- APA vs. MLA
- Ghost Authorship vs. Gift Authorship
- Basic and Applied Research
- Cross-Sectional vs Longitudinal Studies
- Survey vs Questionnaire
- Open Ended vs Closed Ended Questions
- Experimental and Non-Experimental Research
- Inductive vs Deductive Approach
- Null and Alternative Hypothesis
- Reliability vs Validity
- Population vs Sample
- Conceptual Framework and Theoretical Framework
- Bibliography and Reference
- Stratified vs Cluster Sampling
- Sampling Error vs Sampling Bias
- Internal Validity vs External Validity
- Full-Scale, Laboratory-Scale and Pilot-Scale Studies
- Plagiarism and Paraphrasing
- Research Methodology Vs. Research Method
- Mediator and Moderator
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- Parametric and Non-Parametric Test
Research
- Table of Contents
- Dissertation Topic
- Synopsis
- Thesis Statement
- Research Proposal
- Research Questions
- Research Problem
- Research Gap
- Types of Research Gaps
- Variables
- Operationalization of Variables
- Literature Review
- Research Hypothesis
- Questionnaire
- Abstract
- Validity
- Reliability
- Measurement of Scale
- Sampling Techniques
- Acknowledgements
- Research Methods
- Quantitative Research
- Qualitative Research
- Case Study Research
- Survey Research
- Conclusive Research
- Descriptive Research
- Cross-Sectional Research
- Theoretical Framework
- Conceptual Framework
- Triangulation
- Grounded Theory
- Quasi-Experimental Design
- Mixed Method
- Correlational Research
- Randomized Controlled Trial
- Stratified Sampling
- Ethnography
- Ghost Authorship
- Secondary Data Collection
- Primary Data Collection
- Ex-Post-Facto
3 Comments
Very educative
Looks very interesting. I would like to learn more
Interested