Worked Examples using gllamm

Tutorial

gllamm for complex problems by Stas Kolenikov

Review

• Grilli, L. and Rampichini, C. (2006). A review of random effects modelling using gllamm in Stata. In Software Reviews of Multilevel Analysis Packages. Centre for Multilevel Modelling. University of Bristol, UK.
  
  • Do-files for the examples used in the review (ZIP file, 7KB)
  • Datasets for the examples used in the review (ZIP file, 271KB)

Worked examples for papers

• King, G., Murray, C.J.L., Salomon, J.A. and Tandon, A. (2003). Enhancing the Validity and Cross-Cultural Comparability of Survey Research. American Political Science Review 97, 567-583.
  • Chopit model for anchoring vignettes
  • Estimation using gllamm
  • Data (simulated responses) and do-file
  
  
• Thompson, R. and Baker, R. J. (1981). Composite link functions in generalized linear models. Journal of the Royal Statistical Society, Series C, 30 (2), 125-131.
  • Section 3.1: Blood types example

Worked examples for books

• De Boeck, P. and Wilson, M. (Eds.) (2004). Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach. New York: Springer.
  
  • Chapter 2. Descriptive and Explanatory Item Response Models
  
  
• Hox, J. (2002). Multilevel Analysis Techniques and Applications. Erlbaum.
  Textbook Examples at UCLA Technology Services:
  • Chapter 2: The Basic Two-Level Regression Model: Introduction
  • Chapter 4: Some Important Methodological and Statistical Issues
  • Chapter 5: Analyzing longitudinal data
  
  
• Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models. Chapman & Hall/CRC.
  • Section 9.2: Respiratory infection in children: A random intercept model
    
  • Section 9.4 Arithmetic reasoning: Item response models
    
  • Section 9.5: Nicotine gum and smoking cessation: A meta-analysis
    
  • Section 14.3: Herpes and cervical cancer: A latent class covariate measurement error model for a case-control study
    
  • Section 14.4: Job training and depression: A complier average causal effect model
    
  • Section 14.5: Physician advice and drinking: An endogenous treatment model
    
  • Section 14.6: Treatment of liver chirrosis: A joint survival and marker model
    
  • Click here for other datasets and do-files used in the book.

Stata Journal papers containing examples using gllamm

• Zheng, X. and Rabe-Hesketh, S. (2007). Estimating parameters of dichotomous and ordinal item response models using gllamm. The Stata Journal 7 (3), 313-333.
  Datasets and do-files: Use these commands in Stata:
  net sj 7-3 st0129
net get st0129
• Miranda, A. and Rabe-Hesketh, S. (2006). Maximum likelihood estimation of endogenous switching and sample selection models for binary, count, and ordinal variables. The Stata Journal 6 (3), 285-308.
  Datasets and do-files: Use these commands in Stata:
  net sj 6-3 st0107
net install st0107
• Rabe-Hesketh, S. and Skrondal, A. and Pickles, A. (2003). Maximum likelihood estimation of generalized linear models with covariate measurement error. The Stata Journal 3 (4), 385-410.
  Datasets and do-files: Use these commands in Stata:
  net sj 3-4 st0052
net get st0052
• Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2002). Reliable estimation of generalized linear mixed models using adaptive quadrature. The Stata Journal 2 (1), 1-21.
  Datasets and do-files: Use these commands in Stata:
  net sj 2-1 st0005
net get st0005

Datasets and do-file using gllamm

Papers

• Jeon, M., Rijmen, F. and Rabe-Hesketh (in press). Modeling differential item functioning using a generalization of the multiple-group bifactor model. Journal of Educational and Behavioral Statistics.
  • Data and do-file
• Francavilla, F., Giannelli, G.C. and Grilli, L. (in press). Mothers' employment and their children's schooling: a joint multilevel analysis for India. World Development.
  • gllamm code to fit the model
• Jeon, M. and Rabe-Hesketh, S. (2012). Profile-likelihood approach for estimating generalized linear mixed models with factor structures. Journal of Educational and Behavioral Statistics 37 (4), 518-542.
  • Data and do-file
• Skrondal, A. and Rabe-Hesketh, S. (2009). Prediction in multilevel generalized linear models. Journal of the Royal Statistical Society 172 (3), 659-687.
  • Data and do-file from JRSS-A website
• Rabe-Hesketh, S. Skrondal, A. and Gjessing, H. K. (2008). Biometrical modeling of twin and family data using standard mixed model software. Biometrics 64 (1), 280-288. Local
  • Datasets and do-files
  • See also Slides, do-file and datasets from 2010 German Stata Users Group Meeting
• Bagos, P. G. (2008). A unification of multivariate methods for meta-analysis of genetic association studies. Statistical Applications in Genetics and Molecular Biology 7 (1), Article 31.
  
  • Dataset and do-file from SAGMB web site
• Rabe-Hesketh, S. and Skrondal, A. (2006). Multilevel modeling of complex survey data. Journal of the Royal Statistical Society, Series A 169 (4), 805-827. Local
  • PISA data and do-file from JRSS-A web site
• Skrondal, A. and Rabe-Hesketh, S. (2003). Multilevel logistic regression for polytomous data and rankings. Psychometrika 68, 267-287. Local
  • British Election Panel data and do-file

Chapters

• Rabe-Hesketh, S. and Skrondal, A. (2008). Generalized linear mixed effects models. In G. Fitzmaurice, M. Davidian, G. Verbeke and G. Molenberghs (Eds.). Longitudinal Data Analysis: A Handbook of Modern Statistical Methods. Boca Raton, FL: Chapman & Hall/CRC, Chapter 4, pp. 79-106.
  • Data and do-file from book web site (see chapter 4)
• Rabe-Hesketh, S. and Skrondal, A. (2010). Generalized linear mixed models. Penelope Peterson, Eva Baker, Barry McGaw (Eds.), International Encyclopedia of Education (Third Edition). Oxford: Elsevier, volume 7, pp. 171-177.
  • Data and do-file

Books

• Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models. Chapman & Hall/CRC.
  • Section 13.4: British general elections: Multilevel models for discrete choice and rankings
  • Section 13.6: Consumer preferences for coffee makers: A conjoint choice model
  • Click here for other datasets and do-files used in the book.
  
  
• Dohoo, I., Martin, W. and Stryhn, H. (2003). Veterinary Epidemiological Research. University of Prince Edward Island.
  
  • Do file and data for Chapter 22: Mixed models for discrete data

Talks

• Sophia Rabe-Hesketh (2012). Understanding variability in multilevel models for categorical responses. Keynote lecture (HLM SIG) at the 2012 American Educational Research Association Annual Meeting, Vancouver, Canada.
  • pdf of slides
  • Stata commands
  • PISA data, STAR data
• Sophia Rabe-Hesketh (2010). Biometrical modeling of twin and family data in Stata. German Stata Users Group Meeting. Berlin Graduate School of Social Sciences, Berlin, Germany.
  • Slides, do-file and datasets
• Sophia Rabe-Hesketh (2009) Multilevel modeling of ordinal responses . Mexican Stata Users Group Meeting, Mexico City, Mexico.
  • Slides, do-file and datasets
• Sophia Rabe-Hesketh and Anders Skrondal (2008). Prediction in Multilevel Logistic Regression. West Coast Stata Users Group Meeting, San Francisco, CA
  • Slides, do-file and datasets
• Sophia Rabe-Hesketh et al. (2004). Generalized latent class modeling using gllamm. North American Stata Users Group Meeting, Boston, MA
  • Slides, do-file and datasets