Abstract
Multiple linear regression analysis is widely used among psychological researchers to answer their research question related to causality relationship. Exploring the relative importance of independent variables in explaining the total variation in dependent variable is one of the primary interests upon finding a good fit model from the data. This paper considers two popular methods to obtain relative importance, namely Shapley value regression and relative weight analysis. Both are able to break down the R2 of the full model into individual contribution proportion of each independent variable while accounting for the correlations between independent variables and thus offer easily interpretable effect size measures for regressions. Kaggle’s empirical data from the World Happiness 2019 will illustrate the theoretical concept of methods above.
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