PPT-The choice between fixed and random effects models: some co

Claire Crawford with Paul Clarke Fiona Steele amp Anna Vignoles Motivation Appropriate modelling of pupil achievement Pupils clustered within schools hierarchical models Two popular choices fixed and random effects. 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:. William Greene. Stern School of Business. New York University. Part 11. Modeling Heterogeneity. Several Types of Heterogeneity. Observational: Observable differences across. choice makers. Choice strategy: How consumers make. 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. Spatio. -Temporal . Dynamic Panel Models with Fixed and Random Effects. Mohammadzadeh, M. . and . Karami. , H.. Tarbiat Modares University, Tehran, . Iran. Rasouli. , H. . Trauma Research Center, . Baqiyatallah. Random Parameters Model. Allow model parameters as well as constants to be random. Allow multiple observations with persistent effects. Allow a hierarchical structure for parameters – not completely random. Professor William Greene. Stern School of Business. Department . of Economics. Econometrics I. Part . 16 – Panel Data. www.oft.gov.uk/shared_oft/reports/Evaluating-OFTs-work/oft1416.pdf. Panel Data Sets. Wellcome Trust Centre for Human Genetics. Synopsis . Comparing non-nested models. Building Models Automatically. Mixed Models. Comparing Non-nested models. There is no equivalent to the partial F test when comparing non-nested models.. Workshop for Eotvos. . Lorand. University. , Budapest 2016. Datasets. Kvam. (2016) . –. Exercise as treatment for depression. Effect size = d. K = 23. Categorical moderator. McLeod (2007) . –. Data . Analysis. Ashley H. Schempf, PhD. MCH Epidemiology Training Course. June 1, 2012. Outline. Clustered Data. Fixed Effects Models. Random Effects Models. GEE . Models. Hybrid Models. Applied Examples. 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 . Used for a variety of purposes, including prediction, data reduction, and causal inference.. From experiments and observational studies.. Slide . 2. Hierarchical Data. Data structures are often hierarchical or “nested”. 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. Dr.. Danny Arends. Northumbria University, Sept 2022. danny.arends@northumbria.ac.uk. About me. QTL mapping in 3 slides. LMM-MQM time series mapping. Linear mixed models & Multiple QTL mapping. More statistical power through modelling of time series .