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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    Linear 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.  This 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:    Prerequisites   
  Transformation of dependent variable (Kliegl et al.\, 2010)  Contrast spe
 cification of fixed effects (Kliegl & Vasishth\, 2013)      LMMs (Kliegl e
 t al.\, 2011\; Hohenstein & Kliegl\, 2014\; Masson & Kliegl\, 2013)    Spe
 cification of fixed effects\, variance components\, correlation parameters
   Model selection through model comparison (LRT\, AIC\, BIC)  Confidence i
 ntervals for model parameters      Computation and visualization of LMM pa
 rtial effects with the remef() function (Hohenstein &  Visualization of in
 dividual differences and item differences in (quasi-) experimental effects
  (Kliegl et al.\, 2011)    Intended audience  This workshop does not offer
  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 tradition
 al statistical analyses in this environment. To facilitate preparation of 
 the workshop for organizers\, participants should choose one of the follow
 ing four options during registration.  Option 1  With registration\, submi
 t 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 o
 ne experiment in this study\, (3) an R Script with the code (a) for readin
 g 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 effort
 \; it may not be correct or there may be better or alternative ways to spe
 cify the model. Obviously\, the goal of the workshop is to learn defensibl
 e model specifications and corollary analyses.  Option 2  With registratio
 n\, submit a paper package with simulated data. In this case the PDF shoul
 d simply describe the experimental design. Simulated data and the R script
  for the analyses of the data must be submitted as described in Option 1. 
  Option 3  With registration\, submit a paper package with actual data you
  are currently working on that are unpublished as of this time (with all i
 dentifying information about participants etc. completely removed). In thi
 s case the PDF should simply describe the experimental design. Data and th
 e R script for the analyses of the data must be submitted as described in 
 Option 1.  Option 4  LMM demonstrations during the workshop will be based 
 on analyses reported in the cited paper packages (i.e.\, PDF of paper or p
 reprint plus data and R scripts). They and many other paper packages of LM
 M analyses\, including also tutorial material\, are available at the Potsd
 am Mind Research Repository (PMR2\; http://read.psych.uni-potsdam.de/pmr2/
 ) or at the Mind Research Repository (MRR\; http://openscience.uni-leipzig
 .de/).     Complexity of experiment submitted with registration  Factorial
  design If your experiment is typically analyzed with some form of analysi
 s of variance\, the design must include at least 3 measures per subject (i
 .e.\, one within-subject factor with 3 levels). Preferably\, the design sh
 ould comprise at least a 2 x 2 within-subject factor design (i.e.\, a mini
 mum of 4 measures per subject). The minimum number of subjects should be 3
 0\; preferably around 50. If the experiment contains a second random facto
 r (e.g.\, items)\, there should be at least 20 instances (levels). If your
  data has fewer participants or items than these recommendations\, you can
  still use these data for the workshop\, but should be aware that you may 
 lack statistical power for a serious test of your effects.  Multivariate d
 ata If the experiment includes continuous covariates\, the design should i
 nclude both a within-subject factor (e.g.\, experimental condition) and a 
 continuous within-subject/item (repeated-measures) covariate (e.g.\, log w
 ord frequency for subjects\; language skill of subjects for items). The mi
 nimum number of subjects should be 30\; preferably for such a design the n
 umber of subjects should be around 50. If the experiment contains a second
  random factor (e.g.\, Items)\, there should be at least 20 instances (lev
 els).  Important recommendation If your experiment is much more complex th
 an specified above\, you may want to select a subset of the design for the
  purpose of the workshop and deal with its full complexity afterwards.  Wo
 rkshop-related material  The 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 o
 f such analyses\, including also other tutorial material\, are available a
 t the Potsdam Mind Research Repository (PMR2\; http://read.psych.uni-potsd
 am.de/pmr2/) or at the Mind Research Repository (MRR\; http://openscience.
 uni-leipzig.de/). Probably there will also be a special LMM-workshop websi
 te or moodle account providing access to these paper packages\, background
  reading\, links to related websites\, and workshop slides.  Other partici
 pants / other related topics  There is probably some benefit of the worksh
 op for participants with good knowledge of statistics\, but without enough
  specific knowledge of statistical analyses with R to submit a paper packa
 ge. Typically\, such participants legitimately simply “want to know what t
 his 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 i
 mmediate practical needs of the intended audience\, especially with respec
 t to time spent to analyze one’s data during the workshop. So in this work
 shop the emphasis will be on “how to analyze data with LMMs in R”\, not on
  “what are LMMs”.  Some participants may be interested in other topics. Th
 is LMM workshop will not cover Generalized Linear Mixed Models (i.e.\, ana
 lyses 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 Ge
 neralized 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-contribute/channels/event/linear-mixed-m
 odels-workshop-using-r-software-233937
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