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

[1]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[2]  Han L. J. van der Maas,et al.  Science Perspectives on Psychological an Agenda for Purely Confirmatory Research on Behalf Of: Association for Psychological Science , 2022 .

[3]  Dirk P. Janssen,et al.  Twice random, once mixed: Applying mixed models to simultaneously analyze random effects of language and participants , 2011, Behavior Research Methods.

[4]  W. Bruce Croft,et al.  Mixed effect models for genetic and areal dependencies in linguistic typology , 2011 .

[5]  T. Florian Jaeger,et al.  Redundancy and reduction: Speakers manage syntactic information density , 2010, Cognitive Psychology.

[6]  Jarrod D. Hadfield,et al.  MCMC methods for multi-response generalized linear mixed models , 2010 .

[7]  Victor Kuperman,et al.  Processing trade-offs in the reading of Dutch derived words , 2010 .

[8]  Austin F. Frank,et al.  Analyzing linguistic data: a practical introduction to statistics using R , 2010 .

[9]  Joseph Hilbe,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .

[10]  Elisabeth Dévière,et al.  Analyzing linguistic data: a practical introduction to statistics using R , 2009 .

[11]  Mollie E. Brooks,et al.  Generalized linear mixed models: a practical guide for ecology and evolution. , 2009, Trends in ecology & evolution.

[12]  H. Schielzeth,et al.  Conclusions beyond support: overconfident estimates in mixed models , 2008, Behavioral ecology : official journal of the International Society for Behavioral Ecology.

[13]  R. Baayen,et al.  Mixed-effects modeling with crossed random effects for subjects and items , 2008 .

[14]  T. Jaeger,et al.  Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. , 2008, Journal of memory and language.

[15]  D. Mirman,et al.  Statistical and computational models of the visual world paradigm: Growth curves and individual differences. , 2008, Journal of memory and language.

[16]  P. Dixon.  Models of accuracy in repeated-measures designs , 2008 .

[17]  D. Barr.  Analyzing ‘visual world’ eyetracking data using multilevel logistic regression , 2008 .

[18]  H. Bergh,et al.  Examples of Mixed-Effects Modeling with Crossed Random Effects and with Binomial Data. , 2008 .

[19]  James A. Bovaird,et al.  On the use of multilevel modeling as an alternative to items analysis in psycholinguistic research , 2007, Behavior research methods.

[20]  Reinhold Kliegl,et al.  Toward a Perceptual-Span Theory of Distributed Processing in Reading: A Reply to Rayner, Pollatsek, Drieghe, Slattery, and Reichle (2007). , 2007 .

[21]  Risto Lehtonen,et al.  Multilevel Statistical Models , 2005 .

[22]  Hugo Quené,et al.  On multi-level modeling of data from repeated measures designs: a tutorial , 2004, Speech Commun..

[23]  R. Harald Baayen,et al.  Statistics in Psycholinguistics: A critique of some current gold standards , 2004 .

[24]  Per Kragh Andersen,et al.  Regression Modeling Strategies with Applications to Linear Models, Logistic Regression and Survival Analysis. Frank E. Harrell, Jun, Springer‐Verlag, New York, 2001. No. of pages: 568. ISBN 0‐387‐95232‐2 , 2003 .

[25]  Sunil J Rao,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .

[26]  Martyn Plummer,et al.  JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling , 2003 .

[27]  V. Carey,et al.  Mixed-Effects Models in S and S-Plus , 2001 .

[28]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[29]  J. Raaijmakers,et al.  How to deal with "The language-as-fixed-effect fallacy": Common misconceptions and alternative solutions. , 1999 .

[30]  Roel Bosker,et al.  Multilevel analysis : an introduction to basic and advanced multilevel modeling , 1999 .

[31]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[32]  Thomas D. Wickens,et al.  On the choice of design and of test statistic in the analysis of experiments with sampled materials , 1983 .

[33]  John W. Tukey,et al.  We Need Both Exploratory and Confirmatory , 1980 .

[34]  John L. Santa,et al.  Using Quasi F to prevent alpha inflation due to stimulus variation. , 1979 .

[35]  K. Forster,et al.  More on the language-as-fixed-effect fallacy: Monte Carlo estimates of error rates for F1,F2,F′, and min F′ , 1976 .

[36]  J. T. Webster,et al.  A Comparison of Some Approximate F-tests , 1973 .

[37]  H. H. Clark.  The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. , 1973 .

[38]  B. J. Winer,et al.  Statistical Principles in Experimental Design, 2nd Edition. , 1973 .

[39]  Seymour Geisser,et al.  Statistical Principles in Experimental Design , 1963 .

[40]  M. Kendall.  Statistical Methods for Research Workers , 1937, Nature.