Testing and Interpreting Correlation, Moderation and Mediation Effects in Social Science Researches
- September 17, 2020
- Posted by: RSIS
- Categories: IJRISS, Social Science
International Journal of Research and Innovation in Social Science (IJRISS) | Volume IV, Issue VIII, August 2020 | ISSN 2454–6186
T. L. Sajeevanie
Department of Human Resource Management, University of Sri Jayewardenepura, Sri Lanka
Abstract: Most of the researchers in positivist paradigm develop conceptual frameworks to test the research hypotheses. In a quantitative study it is very common to identify the relationship between independent and dependent variables. In addition to that, most of the conceptual models consist of moderating and mediating variables. Hence, it is very critical to test these effects and similarly it is very serious, as to how to interpret, the test results properly. Hence, the objectives of this study are; to explain the correlation, moderating and mediating effects; to explain how to interpret the test results of correlation, moderation and mediating effects in a social science research.
Keywords: Correlation, Moderation, Mediation, Testing, Interpretation
I. INTRODUCTION
Most of the quantitative researchers consider the testing of relationship using correlation analysis, moderating testing and mediating testing. However, some students do not have proper awareness regarding these variables. The main purpose of this article is to explain the correlation, mediation, and mediation effects and discuss the way of interpretation of the analyzed results.
Objectives
1. To explain what is correlation.
2. To identify the moderating and mediating effects.
3. To explain how to interpret the correlation, moderation and mediation analysis results.
II. LITERATURE REVIEW
Correlation
In general correlation means a mutual relationship between two things. Correlation in the broadest sense is a measure of an association between variables. Statistically it can be explained as correlation is a term that is a measure of the strength of a linear relationship between two quantitative variables. This relationship may be of positive or negative correlations. The most common measure of correlation is Pearson’s correlation. The result of this analysis is ‘r’, that is correlation coefficient. The most important factor is correlation coefficient ‘r’, which measures the strength and direction of a linear relationship.
