naa a22 4500 510776981 CHVBK 20180411083233.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.3758/s13428-012-0281-2 doi (NATIONALLICENCE)springer-10.3758/s13428-012-0281-2 Evaluating the robustness of repeated measures analyses: The case of small sample sizes and nonnormal data [Elektronische Daten] [Daniel Oberfeld, Thomas Franke] Repeated measures analyses of variance are the method of choice in many studies from experimental psychology and the neurosciences. Data from these fields are often characterized by small sample sizes, high numbers of factor levels of the within-subjects factor(s), and nonnormally distributed response variables such as response times. For a design with a single within-subjects factor, we investigated Type I error control in univariate tests with corrected degrees of freedom, the multivariate approach, and a mixed-model (multilevel) approach (SAS PROC MIXED) with Kenward-Roger's adjusted degrees of freedom. We simulated multivariate normal and nonnormal distributions with varied population variance-covariance structures (spherical and nonspherical), sample sizes (N), and numbers of factor levels (K). For normally distributed data, as expected, the univariate approach with Huynh-Feldt correction controlled the Type I error rate with only very few exceptions, even if samples sizes as low as three were combined with high numbers of factor levels. The multivariate approach also controlled the Type I error rate, but it requires N ≥ K. PROC MIXED often showed acceptable control of the Type I error rate for normal data, but it also produced several liberal or conservative results. For nonnormal data, all of the procedures showed clear deviations from the nominal Type I error rate in many conditions, even for sample sizes greater than 50. Thus, none of these approaches can be considered robust if the response variable is nonnormally distributed. The results indicate that both the variance heterogeneity and covariance heterogeneity of the population covariance matrices affect the error rates. Psychonomic Society, Inc., 2012 Analysis of variance nationallicence Robustness nationallicence Nonnormality nationallicence Small sample settings nationallicence Repeated measurements nationallicence Correlated data nationallicence Multivariate nationallicence Mixed model analyses nationallicence Multilevel model nationallicence Simulation study nationallicence Type I error rate nationallicence Central limit theorem nationallicence Monte Carlo nationallicence Oberfeld Daniel Department of Psychology, Johannes Gutenberg-Universität, 55099, Mainz, Germany aut Franke Thomas Department of Psychology, Johannes Gutenberg-Universität, 55099, Mainz, Germany aut Behavior Research Methods Springer US; http://www.springer-ny.com 45/3(2013-09-01), 792-812 45:3<792 2013 45 13428 https://doi.org/10.3758/s13428-012-0281-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.3758/s13428-012-0281-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Oberfeld Daniel Department of Psychology, Johannes Gutenberg-Universität, 55099, Mainz, Germany aut NATIONALLICENCE 700 1- Franke Thomas Department of Psychology, Johannes Gutenberg-Universität, 55099, Mainz, Germany aut NATIONALLICENCE 773 0- Behavior Research Methods Springer US; http://www.springer-ny.com 45/3(2013-09-01), 792-812 45:3<792 2013 45 13428 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer