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

Robyn Carson,Katharina Schwarz,Markus Jancyzk,Roland Pfister

doi:10.20982/tqmp.09.2.p072

Robyn Carson, Katharina Schwarz + Show 2 more

Open Access

https://doi.org/10.20982/tqmp.09.2.p072

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Journal: Tutorials in Quantitative Methods for Psychology	Publication Date: Oct 1, 2013
Citations: 53	License type: cc-by

Abstract

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 (Ahn, Jung, & Kang, 2002; Lorch & Myers, 1990; Myers & Broyles,

Highlights

Three different methods for extracting coefficients of linear regression analyses are presented
2000).* regression coefficient analysis (RCA) offers a flexible alternative to more common analyses of variance (ANOVA) approaches, and it has been applied to numerous different topics, covering as diverse areas as reading, emotion, cognitive control, and numerical cognition
As a hands-on example, we demonstrate how regression slopes can be extracted to probe for spatial-numerical associations in a parity judgment task

Summary

Introduction

Three different methods for extracting coefficients of linear regression analyses are presented. We compare available methods for extracting regression slopes for three widespread statistical packages: SPSS 19, R 2.15 and MS Excel 2010 / LibreOffice 3.6 Calc. An efficient way to extract regression slopes with SPSS involves two separate steps (Figure 2).

Results

Conclusion