To transform or not to transform: using generalized linear mixed models to analyse reaction time data

By Steson Lo, Sally Andrews. Frontiers in Psychology, 2015

854 citations recorded by the source corpus.

Linear mixed-effect models (LMMs) are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by Speelman and McGann (2013) about the use of mean data, because they do not average across individual responses. However, recent guidelines for using LMM to analyse skewed reaction time (RT) data collected in many cognitive psychological studies recommend the application of non-linear transformations to satisfy assumptions of normality. Uncritical adoption of this recommendation has important theoretical implications which can yield misleading conclusions. For example, Balota et al. (2013) showed that analyses of raw RT produced additive effects of word frequency and stimulus quality on word identification, which conflicted with the interactive effects observed in analyses of transformed RT. Generalized linear mixed-effect models (GLMM) provide a solution to this problem by satisfying normality assumptions without the need for transformation. This allows differences between individuals to be properly assessed, using the metric most appropriate to the researcher's theoretical context. We outline the major theoretical decisions involved in specifying a GLMM, and illustrate them by reanalysing Balota et al.'s datasets. We then consider the broader benefits of using GLMM to investigate individual differences.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ec3b951c7e763c194de5cec60bdc54b11a7c3caf

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Citations in the corpus

Power Analysis and Effect Size in Mixed Effects Models: A Tutorial (2018, methodology): “Others warn that transformations may alter the nature of the data (e.g., a logarithmic transformation may alter the interactions between independent variables; Lo & Andrews, 2015).”
The JASP guidelines for conducting and reporting a Bayesian analysis (2019, background): “The main measure of the study is a reaction time variable, which can for various reasons be non-normally distributed (Lo & Andrews, 2015; but see Schramm & Rouder, 2019).”
Nonlinear Transformations in Organizational Research: Possible Problems and Potential Solutions (2018, background): “The disregard for theory raises concerns for construct validity and hypothesis testing because performing a NLT creates a new variable, the scores on which may not operationalize the latent variable intended by the researcher (Lo & Andrews, 2015).”