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DESCRIPTION:To register for this workshop (open to the academic community h
 aving some experience with R): please go to http://www.crblm.ca/events/lin
 ear_mixed_models_workshop\n\nLinear Mixed Models (LMMs) are increasingly r
 eplacing traditional F1/F2-mixed-model/repeated-measure ANOVAs (rmAOVs\; i
 .e.\, designs with both between- and within-subject/item factors\, e.g.\, 
 Kliegl\, Risse & Laubrock\, 2007) or repeated-measures multiple regression
 s (rmMRs\; e.g.\, Kliegl\, 2007) for statistical inference about experimen
 tal effects on fixation durations or response latencies.\nThis workshop fo
 cuses on analyses of these two types of dependent variable. The goal of th
 is workshop is to teach how LMMs go beyond the usual rmAOV and rmMR analys
 es\, using the lmer()-function of the lme4 package (Version 1.1-4\; Bates\
 , Maechler\, Bolker\, & Walker\, 2014) in the R environment (R Core Team\,
  2013). The workshop is structured into four sections:\n\nPrerequisites\n
 \nTransformation of dependent variable (Kliegl et al.\, 2010)\nContrast sp
 ecification of fixed effects (Kliegl & Vasishth\, 2013)\n\n\nLMMs (Kliegl 
 et al.\, 2011\; Hohenstein & Kliegl\, 2014\; Masson & Kliegl\, 2013)\n\nSp
 ecification of fixed effects\, variance components\, correlation parameter
 s\nModel selection through model comparison (LRT\, AIC\, BIC)\nConfidence 
 intervals for model parameters\n\n\nComputation and visualization of LMM p
 artial effects with the remef() function (Hohenstein &\nVisualization of i
 ndividual differences and item differences in (quasi-) experimental effect
 s (Kliegl et al.\, 2011)\n\nIntended audience\nThis workshop does not offe
 r an introduction to R. The content of the workshop is aimed at scientists
  who already have basic knowledge of R and have been carrying out traditio
 nal statistical analyses in this environment. To facilitate preparation of
  the workshop for organizers\, participants should choose one of the follo
 wing four options during registration.\nOption 1\nWith registration\, subm
 it a paper package (zipped file) consisting of (1) PDF of a publication or
  a PDF of a preprint of an in-press or submitted paper\, (2) the data for 
 one experiment in this study\, (3) an R Script with the code (a) for readi
 ng the data\, (b) for computing the summary statistics (Ms and SDs for the
  design cells)\, (c) for the rmAOV (or rmMR)\, and optionally (d) also for
  an LMM. As to optional (d)\, the LMM code could represent your best effor
 t\; it may not be correct or there may be better or alternative ways to sp
 ecify the model. Obviously\, the goal of the workshop is to learn defensib
 le model specifications and corollary analyses.\nOption 2\nWith registrati
 on\, submit a paper package with simulated data. In this case the PDF shou
 ld simply describe the experimental design. Simulated data and the R scrip
 t for the analyses of the data must be submitted as described in Option 1.
 \nOption 3\nWith registration\, submit a paper package with actual data yo
 u are currently working on that are unpublished as of this time (with all 
 identifying information about participants etc. completely removed). In th
 is case the PDF should simply describe the experimental design. Data and t
 he R script for the analyses of the data must be submitted as described in
  Option 1.\nOption 4\nLMM demonstrations during the workshop will be based
  on analyses reported in the cited paper packages (i.e.\, PDF of paper or 
 preprint plus data and R scripts). They and many other paper packages of L
 MM analyses\, including also tutorial material\, are available at the Pots
 dam Mind Research Repository (PMR2\; http://read.psych.uni-potsdam.de/pmr2
 /) or at the Mind Research Repository (MRR\; http://openscience.uni-leipzi
 g.de/). \n\nComplexity of experiment submitted with registration\nFactoria
 l design If your experiment is typically analyzed with some form of analys
 is of variance\, the design must include at least 3 measures per subject (
 i.e.\, one within-subject factor with 3 levels). Preferably\, the design s
 hould comprise at least a 2 x 2 within-subject factor design (i.e.\, a min
 imum of 4 measures per subject). The minimum number of subjects should be 
 30\; preferably around 50. If the experiment contains a second random fact
 or (e.g.\, items)\, there should be at least 20 instances (levels). If you
 r data has fewer participants or items than these recommendations\, you ca
 n still use these data for the workshop\, but should be aware that you may
  lack statistical power for a serious test of your effects.\nMultivariate 
 data If the experiment includes continuous covariates\, the design should 
 include both a within-subject factor (e.g.\, experimental condition) and a
  continuous within-subject/item (repeated-measures) covariate (e.g.\, log 
 word frequency for subjects\; language skill of subjects for items). The m
 inimum number of subjects should be 30\; preferably for such a design the 
 number of subjects should be around 50. If the experiment contains a secon
 d random factor (e.g.\, Items)\, there should be at least 20 instances (le
 vels).\nImportant recommendation If your experiment is much more complex t
 han specified above\, you may want to select a subset of the design for th
 e purpose of the workshop and deal with its full complexity afterwards.\nW
 orkshop-related material\nThe demonstrations in the workshop will be based
  on the analyses reported in the cited paper packages (i.e.\, PDF of paper
  or preprint plus data and R scripts). They and many other paper packages 
 of such analyses\, including also other tutorial material\, are available 
 at the Potsdam Mind Research Repository (PMR2\; http://read.psych.uni-pots
 dam.de/pmr2/) or at the Mind Research Repository (MRR\; http://openscience
 .uni-leipzig.de/). Probably there will also be a special LMM-workshop webs
 ite or moodle account providing access to these paper packages\, backgroun
 d reading\, links to related websites\, and workshop slides.\nOther partic
 ipants / other related topics\nThere is probably some benefit of the works
 hop for participants with good knowledge of statistics\, but without enoug
 h specific knowledge of statistical analyses with R to submit a paper pack
 age. Typically\, such participants legitimately simply “want to know what 
 this is all about”. As the time for the program as sketched above is tight
 \, there may not be enough time to discuss issues that go much beyond the 
 immediate practical needs of the intended audience\, especially with respe
 ct to time spent to analyze one’s data during the workshop. So in this wor
 kshop the emphasis will be on “how to analyze data with LMMs in R”\, not o
 n “what are LMMs”.\nSome participants may be interested in other topics. T
 his LMM workshop will not cover Generalized Linear Mixed Models (i.e.\, an
 alyses of binary dependent variables such as 0/1 accuracy or 0/1 skipping)
  or other related mixed model analyses such as Nonlinear Mixed Models or G
 eneralized Additive Mixed Models.
DTSTART;VALUE=DATE:20140322
DTEND;VALUE=DATE:20140322
LOCATION:Arts-Ferrier Computer Room\, Arts Building\, CA\, QC\, Montreal\, 
 H3A 0G5\, 853 rue Sherbrooke Ouest
SUMMARY:Linear Mixed Models Workshop\, using R software
URL:/channels/event/linear-mixed-models-workshop-using
 -r-software-233937
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