Statistics 196A, Education 401D  Spring 2018
    Multilevel Modeling Using R

David Rogosa Sequoia 224,   rag{AT}stat{DOT}stanford{DOT}edu   Office hours: Class meeting days, 5:30 - 6:30
Course web page:

                To see full course materials from Spring 2017 go here

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  STATS 196A (EDUC 401D): Multilevel Modeling Using R
Multilevel data analysis examples using R. 
Topics include: two-level nested data, growth curve modeling, generalized linear models for counts and categorical data, 
three-level analyses. For more information, see course website:
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Rogosa, D. (PI) 
STATS 196A | 1 units | Class # 20661 | Section 01 | Grading: Satisfactory/No Credit | WKS |  
Wed 3:30 PM - 5:20 PM at Littlefield 104 with Rogosa, D. (PI) 
Instructors: Rogosa, D. (PI) 
Notes: Class meets on  April 11, April 18, April 25, May 2, May 16.
 Course Schedule
Five (2hr) mtgs W 3:30 - 5:20 on April 11, April 18, April 25, May 2, May 16.  Littlefield 104
 Weeks 1 - 4. 
    a. Introduction: Basic analyses for two-level nested data, normal models  (UK Exam data)
    b. Additional two-level (normal) models: experimental designs (Dyestuff), longitudinal data (growth curves, sleepstudy), observational data (High School and Beyond) 
    c. Generalized linear mixed models for counts and categorical outcomes 
    d. Three-level analyses (nested data and longitudinal data)    
    e. Specialized applications (as time permits):  regression diagnostics, power calculations and design, ecological inference, survival analysis, nonlinear functional forms, mediation analysis, propensity scores and matching, imputation, item response theory
 Week 5. Student presentations of multilevel data analyses  
For the 1-unit enrollment in this short course, students are expected to engage in the four presentation class sessions, and for the fifth session each student makes a short (~10 min) presentation of a relevant data analysis they have conducted.

Note to auditors. We will have room for auditors.
The Registrar does have a form (no-fee) for faculty, staff, post-docs: Application for Auditor or Permit to Attend (PTA) Status   

Core Sources
Many of the example presented in this short course are described in Examples from Multilevel Software Comparative Reviews  Douglas Bates.    Code version of MlmSoftRev
R-package containing mlmRev data examples.     Bates talk on mlmRev      U Bristol documentation      Additional examples in core package lme4
Another set of examples: lmer for SAS PROC MIXED Users Douglas Bates Department of Statistics University of Wisconsin Madison    Data sets from SAS System for Mixed Models
Overviews and additional examples from Doug Bates:
lme4: Mixed-effects modeling with R  February 17, 2010 Springer (book chapters). An merged version of Bates book:[broken 2/18] lme4: Mixed-effects modeling with R January 11, 2010
R Journal intro Fitting linear mixed models in R Using the lme4 package Douglas Bates (pp.27-30)
Collection of all Doug Bates lme4 talks      Fitting linear mixed-effects models using lme4, Journal of Statistical Software Douglas Bates Martin Machler Ben Bolker.       Technical topics: Mixed models in R using the lme4 package Part 4: Theory of linear mixed models
HSB and growth curve examples in  John Fox lme tutorial
Another nice lmer exposition with life sciences examples: Mixed-effects models, Remko Duursma, Jeff Powell  Hawkesbury Institute for the Environment, Western Sydney University. September 2016.  HIE Datasets

Current version of R is R version 3.4.3 released on 2017-11-30. For references and software: The R Project for Statistical Computing   Closest download mirror is Berkeley

A recent text (potentially) provides more infrastructure for this short course, but sadly it has many shortcomings. This text has free access at Stanford via      Multilevel Modeling Using R        Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences Published:June 23, 2014 by CRC Press
     Journal of Statistical Software Book Review
Book website, including data