Easy methods for extracting individual regression slopes: Comparing SPSS, R, and Excel

Abstract

Three different methods for extracting coefficients of linear regression analyses are presented. The focus is on automatic and easy-to-use approaches for common statistical packages: SPSS, R, and MS Excel / Libre Office Calc. Hands-on examples are included for each analysis, followed by a brief description of how a subsequent regression coefficient analysis is performed. An increasingly popular analysis of within-subjects designs revolves around regression coefficients that are estimated individually for each participant. More precisely, a dependent variable (criterion) is regressed on an independent variable (predictor) individually for each participant. The extracted values for slopes and intercept are then compared between conditions or tested against a population value of 0 via standard significance tests such as paired-samples t-tests or repeated-measures analyses of variance (ANOVA). This procedure is commonly known as regression coefficient analysis (RCA; Lorch & Myers, 1990, Method 3). RCA circumvents methodological problems of standard regression analysis which assumes different observations to be independent from each other. This assumption is routinely violated by data from within-subjects designs, but it does not apply to the coefficients that were extracted from individual data sets (cf. Lorch & Myers, 1990). In contrast, RCA only assumes a linear relationship between predictor and criterion for each individual participant and can be used for both, continuous and dichotomous predictors