MULTIPLE REGRESSION ANALYSIS: KEY TO SOCIAL SCIENCE RESEARCH
This is well known fact that the success of social science research heavily depends upon the selection of research tools and its effective utilization. Researchers often come across the situations where they want to study the impact of one variable on the other variable viz. impact of income on expenditure. Although we have freedom to select research tools for multivariate analysis as wide range of research tools are available, multiple regression analysis allows us to determine the effect of more than one independent variable on dependent variable. This term paper talks about the concept of multiple regression analysis, its assumptions, application, and its limitations to the social science research. The paper also briefs about various statistics associated with multiple regression analysis.
Regression analysis is a statistical technique to investigate the relationships between quantitative variables. In some situation, researchers are interested to determine the underlying effect of one variable on another variable viz. effect of income on expenditure or effect of changes in money supply on the rate of inflation. At the same time, the researchers also assess the “statistical significance” of the estimated relationships, that is, the degree of confidence that the true relationship is close to the estimated relationship.
Regression analysis is a powerful statistical technique that identifies the association between two or more quantitative variables: a dependent variable, whose value is to be predicted, and an independent or explanatory variable (or variables), about which significant amount of knowledge is available. This statistical tool is used to develop the equation that represents the relationship between the variables. A simple regression analysis can show that the relation between an independent variable and a dependent variable is linear, using the simple linear regression equation. Multiple regression analysis provides an equation that predicts dependent variable from two or more independent variables. In other words, it can be said that multiple regression involves a single dependent variable and two or more independent variables, while simple regression model involves one dependent variable and one independent variable.
Regression analysis is concerned with the nature as well as the degree of association between variables. Although the independent variables may explain the variation in the dependent variable, it does not necessarily imply causation. At the time of using multiple regression analysis in psychology, many researchers use the term “independent variables” to identify those variables that they think will influence some other “dependent variable”. Research literature also uses the term “predictor variables” for those variables that may be useful in predicting the scores on another variable that we call the “criterion variable”.