Blog
About

42
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Balancing Type I error and power in linear mixed models

      ,   , , ,

      Journal of Memory and Language

      Elsevier BV

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references 16

          • Record: found
          • Abstract: not found
          • Article: not found

          Estimating the Dimension of a Model

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Fitting Linear Mixed-Effects Models Usinglme4

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Random effects structure for confirmatory hypothesis testing: Keep it maximal.

              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.
                Bookmark

                Author and article information

                Journal
                Journal of Memory and Language
                Journal of Memory and Language
                Elsevier BV
                0749596X
                June 2017
                June 2017
                : 94
                :
                : 305-315
                Article
                10.1016/j.jml.2017.01.001
                © 2017

                Comments

                Comment on this article