PPT-3. The dreaded maximal random effects

UCL Linguistics workshop on mixedeffects modelling in R 1820 May 2016 Code at httpwwwmypolyuwebhksjpolitUCLRworkshop 2 Reminder what random effects are lmer RT ClassFrequency. William Greene. Stern School of Business. New York University. Part 5. Panel Data Models. Application: Health Care Panel Data. German Health Care Usage Data. , 7,293 Individuals, Varying Numbers of Periods. Advanced . Panel Data Techniques. 2. Advanced Panel Data Topics. Fixed Effects estimation. STATA stuff: . xtreg. Autocorrelation/Cluster correction. But first! Review of . heteroskedasticity. Probably won’t get to details:. Factor in Social . Psychology. :. A New and Comprehensive Solution. to a Pervasive but Largely Ignored Problem . Jacob Westfall. University of Colorado Boulder. Charles M. Judd David A. Kenny. 1. , Patria A Hume. 1. , Steve C Hollings. 1. , . Mike . J. . Hamlin. 2. , Matt . Spencer. 3. –and– . Rita M . Malcata. 1. , . T . Brett . Smith. 4. , Ken L Quarrie. 5. 1. AUT . University, . Auckland, . glmm. & the PIT-trap. Loïc Thibaut, David Warton. EagleHawk. Neck copepods. 1. 2. 3. 4. …. B1. 0. 10. 0. 0. B1. 0. 9. 0. 0. B1.t. 0. 51. 0. 2. B1.t. 2. 48. 0. 2. B2. 0. 6. 14. 0. B2. 1. 0. 7. (. UNIBI -. Bielefeld University). “Analysis and Validation of Data from Multi-Level Anticipatory Complex Systems”. Project FP7 – ICT-318723 MATHEMACS. Topdrim-Mathemacs. joint workshop in Bielefeld. Comparing . t . > 2 Groups - Numeric Responses. Extension of Methods used to Compare 2 Groups. Independent Samples and Paired Data Designs. Normal and non-normal data distributions. Completely Randomized Design (CRD). Gage Repeatability and . Repproducibility. . Experiment for Measuring Drill Holes in Wood Parts. Source: M-H. C. Li and A. Al-. Refaie. (2008). “Improving Wooden Parts’ Quality by Adopting DMAIC Procedure,” . Workshop for Eotvos. . Lorand. University. , Budapest 2016. Datasets. Kvam. (2016) . –. Exercise as treatment for depression. Effect size = d. K = 23. Categorical moderator. McLeod (2007) . –. Hierarchical Linear Modeling . . 1. THESE MODELS INCORPORATE A NESTED DESIGN. 2. THIS ALLOWS FOR RESPONSES TO BE MORE SIMILAR WITHIN A GROUP THAN BETWEEN A GROUP. 3. hlm ALLOWS FOR FIXED EFFECTS, RANDOM EFFECTS, AND VARIANCE COMPONENTS . Jonathan J Shuster, PhD shusterj@ufl.edu. University of Florida. College of Medicine. 1. Pre-Talk Questions. Are you familiar with Meta-Analysis?. Have been the Statistician on a MA?. Have you written one or more methods papers on MA?. Justyna. . Ekert. & Alex Hopkins. Expert: Guillaume . Flandin. Overview. Recap of first-level analysis. What is second-level analysis?. Fixed versus random effects. How do we analyse random effects?. Group Analyses. Tobias Hauser. Max Planck UCL . Centre. . for. Computational . Psychiatry. and . Ageing. Research. Wellcome. Centre for Human Neuroimaging. University College London. With many thanks to G. . Lecture . 3. Fixed and random effects models continued. Overview. Review. Between- and within-individual variation. Types of variables: time-invariant, time-varying and trend. Individual heterogeneity.