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As scientists seek to explain phenomena, they use empirical measures such as correlation to express the relationships among variables. Correlations can range from 0.0 (no relationship) to 1.0 (a perfect relationship between the variables’ values) with coefficients being either positive or negative. Regression moves beyond correlation by analyzing the variability of the dependent variable based on the information from one or more independent variables, seeking to explain which independent variables best predict the dependent variable.
The regression equation indicates how well we can predict values of the dependent variable by knowing the values of the independent variable or variables. The equation is represented by the regression line, which depicts the relationship between the variables. The sum of squares, which is fundamental to regression analysis, refers to the deviation or variance of a score from the average score of a distribution. The least-squares line depicts the best-fitting regression line or lowest sum of squared distances of all data points.
Simple regression analysis seeks to learn how much one continuous independent variable explains or predicts the dependent variable. Its equation refers to how the dependent variable scores rely on the independent variable scores. Multiple regression analysis estimates the separate and collective contributions of two or more independent variables to explaining the dependent variable (Kerlinger & Pedhazur 1973).
Based on the size of each regression coefficient, researchers can compare the contribution of each independent variable to predicting the dependent variable. Multiple R indicates the strength of relationship. R2 depicts the proportion of explained variance for the predictors, and F is the test of significance. If the predictor variables are intercorrelated, it becomes difficult to assess individual contributions to the equation. Depending on the objective, a researcher might choose to enter all predictors simultaneously. Or, if the researcher’s goal is to test a communication model, he or she might enter the predictor variables in blocks, hierarchically, according to the sequential steps in the model.
Examples from communication research: Ohr and Schrott (2001) found campaign information seeking can be explained in a local German election mainly by social expectations to be politically informed, but also by a personal duty to stay politically informed, a desire to vote, and the entertainment aspect of politics. Slater (2003) found gender, sensation seeking, aggression, and frequency of Internet use contributed to explaining the use of violent media content and violent website content.
- Kerlinger, F. N. & Pedhazur, E. J. (1973). Multiple regression in behavioral research. New York: Holt, Rinehart and Winston.
- Ohr, D. & Schrott, P. R. (2001). Campaigns and information seeking: Evidence from a German state election. European Journal of Communication, 16, 419–449.
- Slater, M. D. (2003). Alienation, aggression, and sensation seeking as predictors of adolescent use of violent film, computer, and website content. Journal of Communication, 53(1), 105–121.