Correlational Research Vs Experimental Research

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Correlational Research Vs Experimental Research

Correlational research and experimental research are two different research approaches used in social sciences and other fields of research.

Correlational Research

Correlational Research is a research approach that examines the relationship between two or more variables. It involves measuring the degree of association or correlation between the variables without manipulating them. The goal of correlational research is to identify whether there is a relationship between the variables and the strength of that relationship. Correlational research is typically conducted through surveys, observational studies, or secondary data analysis.

Experimental Research

Experimental Research, on the other hand, is a research approach that involves the manipulation of one or more variables to observe the effect on another variable. The goal of experimental research is to establish a cause-and-effect relationship between the variables. Experimental research is typically conducted in a controlled environment and involves random assignment of participants to different groups to ensure that the groups are equivalent. The data is collected through measurements and observations, and statistical analysis is used to test the hypotheses.

Difference Between Correlational Research and Experimental Research

Here’s a comparison table that highlights the differences between correlational research and experimental research:

	Correlational Research	Experimental Research
Definition	Examines the relationship between two or more variables without manipulating them	Involves the manipulation of one or more variables to observe the effect on another variable
Goal	To identify the strength and direction of the relationship between variables	To establish a cause-and-effect relationship between variables
Data Collection	Surveys, observational studies, or secondary data analysis	Controlled experiments with random assignment of participants
Data Analysis	Correlation coefficients, regression analysis	Inferential statistics, analysis of variance (ANOVA)
Outcome	Association between variables	Causality between variables
Example	Examining the relationship between smoking and lung cancer	Testing the effect of a new medication on a particular disease