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  • Sophia Rabe-Hesketh

    (Graduate School of Education and Graduate Group in Biostatistics, UC Berkeley)

  • Anders Skrondal

    (Biostatistics Group, Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway)

  • Andrew Pickles

    (School of Epidemiology and Health Science and CCSR, The University of Manchester , England)


This manual describes a Stata program gllamm that can estimate Generalized Linear Latent and Mixed Models (GLLAMMs). GLLAMMs are a class of multilevel latent variable models for (multivariate) responses of mixed type including continuous responses, counts, duration/survival data, dichotomous, ordered and unordered categorical responses and rankings. The latent variables (common factors or random effects) can be assumed to be discrete or to have a multivariate normal distribution. Examples of models in this class are multilevel generalized linear models or generalized linear mixed models, multilevel factor or latent trait models, item response models, latent class models and multilevel structural equation models. The program can be downloaded from

Suggested Citation

  • Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "GLLAMM Manual," U.C. Berkeley Division of Biostatistics Working Paper Series 1160, Berkeley Electronic Press.
  • Handle: RePEc:bep:ucbbio:1160

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    References listed on IDEAS

    1. Sophia Rabe-Hesketh & Andrew Pickles & Colin Taylor, 2000. "Generalized linear latent and mixed models," Stata Technical Bulletin, StataCorp LP, vol. 9(53).
    2. Anders Skrondal & Sophia Rabe-Hesketh, 2003. "Multilevel logistic regression for polytomous data and rankings," Psychometrika, Springer;The Psychometric Society, vol. 68(2), pages 267-287, June.
    3. Anders Skrondal & Sophia Rabeā€Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.
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