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Hierarchical Linear Regression

Hierarchical linear regression is an extension of multiple linear regression. It is used when we want to predict a continuous (interval or ratio) variable's value based on the value of one or more predictor variables, which can be any measurement scale (e.g., nominal, ordinal, interval, or ratio) while controlling for one or more variables which can also comprise any measurement scale. In this session, Dr. Taylor discusses (1) the four scales of measurement, (2) describes the conditions for using hierarchical linear regression (with all continuous and predictor and control variables), (3) identifies data assumptions surrounding hierarchical linear regression, (4) shows how to conduct hierarchical linear regression using SPSS, (5) explains SPSS output/results, and (6) shows how to write an APA-compliant results section based on the SPSS output, including appropriate tables and figures.