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      Random effects structure for confirmatory hypothesis testing: Keep it maximal

      , , ,
      Journal of Memory and Language
      Elsevier BV

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          Abstract

          Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.

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          Generalized linear mixed models: a practical guide for ecology and evolution.

          How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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            Mixed-effects modeling with crossed random effects for subjects and items

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              MCMC Methods for Multi-Response Generalized Linear Mixed Models: TheMCMCglmmRPackage

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                Author and article information

                Journal
                Journal of Memory and Language
                Journal of Memory and Language
                Elsevier BV
                0749596X
                April 2013
                April 2013
                : 68
                : 3
                : 255-278
                Article
                10.1016/j.jml.2012.11.001
                3881361
                24403724
                e379dd0a-c724-4ce6-bf33-ef8971366746
                © 2013
                History

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