Ansari, A., & Jedidi, K. (2000). Bayesian factor analysis for multilevel binary observations.Psychometrika, 65, 475–496. Google Scholar
Arminger, G., & Sobel, M. (1990). Pseudo maximum likelihood estimation of mean- and covariance structures with missing data.Journal of the American Statistical Association, 85, 195–203. Google Scholar
Asparouhov, T., & Muthén, B.O. (2004). Full-information maximum-likelihood estimation of general two-level latent variable models. Draft.
Bollen, K.A. (1989).Structural Equations with Latent Variables. New York: Wiley. Google Scholar
Böckenholt, U. (2001). Hierarchical modeling of paired comparison data.Psychological Methods, 6, 49–66. Google Scholar
Carroll, R.J., Ruppert, D., & Stefanski, L.A. (1995).Measurement Error in Nonlinear Models. London: Chapman & Hall. Google Scholar
Chou, C.-P., Bentler, P.M., & Pentz, M.A. (2000). A two-stage approach to multilevel structural equation models: application to longitudinal data. In T.D. Little, K.U. Schnabel, & J. Baumert (Eds.),Modeling Longitudinal and Multilevel Data (pp. 33–49). Mahwah, NJ: Erlbaum. Google Scholar
Clayton, D. (1988). The analysis of event history data: a review of progress and outstanding problems.Statistics in Medicine, 7, 819–841. Google Scholar
Clogg, C. C. (1995). Latent class models. In G. Arminger, C.C. Clogg, & M.E. Sobel (Eds.),Handbook of Statistical Modeling for the Social and Behavioral Sciences (pp. 311–359). New York: Plenum Press. Google Scholar
Dohoo, I.R., Tillard, E., Stryhn, H., & Faye, B. (2001). The use of multilevel models to evaluate sources of variation in reproductive performance in dairy cattle.Preventive Veterinary Medicine, 50, 127–144. Google Scholar
Fox, J.P. (2001).Multilevel IRT: A Bayesian Perspective on Estimating Parameters and Testing Statistical Hypotheses. Ph.D. thesis, University of Twente, Enschede. Google Scholar
Fox, J.P., & Glas, C. A.W. (2001). Bayesian estimation of a multilevel IRT model using Gibbs sampling.Psychometrika, 66, 271–288. Google Scholar
Fox, J.P., & Glas, C. A.W. (2003). Bayesian modeling of measurement error in predictor variables using item response theory.Psychometrika, 68, 169–191. Google Scholar
Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalised least squares.Biometrika, 73, 43–56. Google Scholar
Goldstein, H. (1995).Multilevel Statistical Models. London: Arnold. Google Scholar
Goldstein, H., & Browne, W. (2002). Multilevel factor analysis modeling using Markov Chain Monte Carlo (MCMC) estimation. In G.A. Marcoulides & I. Moustaki (Eds.),Latent Variable and Latent Structure Models. Mahwah, NJ: Erlbaum. Google Scholar
Goldstein, H., & McDonald, R.P. (1988). A general model for the analysis of multilevel data.Psychometrika, 53, 455–467. Google Scholar
Hagenaars, J.A. (1988). Latent structure models with direct effects between indicators: local dependence models.Sociological Methods & Research, 16, 379–405. Google Scholar
Harper, D. (1972). Local dependence latent structure models.Psychometrika, 37, 53–59. Google Scholar
Heckman, J.J. (1979). Sample selection bias as a specification error.Econometrica, 47, 153–161. Google Scholar
Heckman, J.J., & Singer, B. (1984). A method of minimizing the impact of distributional assumptions in econometric models for duration data.Econometrica, 52, 271–320. Google Scholar
Hox, J. (2002).Multilevel Analysis: Techniques and Applications. Mahwah, NJ: Erlbaum. Google Scholar
Jöreskog, K.G. (1971). Simultaneous factor analysis in several populations.Psychometrika, 36, 409–426. Google Scholar
Jöreskog, K.G. (1973). A general method for estimating a linear structural equation system. In A.S. Goldberger & O.D. Duncan (Eds.),Structural Equation Models in the Social Sciences (pp. 85–112). New York: Seminar. Google Scholar
Jöreskog, K.G. & Goldberger, A.S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable.Journal of the American Statistical Association, 70, 631–639. Google Scholar
Jöreskog, K.G., & Sörbom, D. (1989).LISREL 7: A Guide to the Program and Applications. Chicago, IL: SPSS Publications. Google Scholar
Knott, M., Albanese, M.T., & Galbraith, J.I. (1990). Scoring attitudes to abortion.The Statistician, 40, 217–223. Google Scholar
Laird, N.M. (1978). Nonparametric maximum likelihood estimation of a mixing distribution.Journal of the American Statistical Association, 73, 805–811. Google Scholar
Lee, S.-Y., & Shi, J.-Q. (2001). Maximum likelihood estimation of two-level latent variable models with mixed continuous and polytomous data.Biometrics, 57, 787–794. Google Scholar
Lee, S.-Y., & Tsang, S.-Y. (1999). Constrained maximum likelihood estimation of two-level covariance structure models via EM type algorithms.Psychometrika, 64, 435–450. Google Scholar
Lesaffre, E., & Spiessens, B. (2001). On the effect of the number of quadrature points in a logistic random-effects model: an example.Applied Statistics, 50, 325–335. Google Scholar
Liang, J., & Bentler, P. M. (2003). An EM algorithm for fitting two-level structural equation models.Psychometrika, in press.
Linda, N.Y., Lee, S.-Y., & Poon, W.-Y. (1993). Covariance structure analysis with three level data.Computational Statistics & Data Analysis, 15, 159–178. Google Scholar
Longford, N.T. (1993).Random Coefficient Models. Oxford: Oxford University Press. Google Scholar
Longford, N.T., & Muthén, B.O. (1992). Factor analysis for clustered observations.Psychometrika, 57, 581–597. Google Scholar
McArdle, J.J. (1986). Latent variable growth within behavior genetic models.Behavior Genetics, 16, 163–200. Google Scholar
McDonald, R.P., & Goldstein, H. (1989). Balanced and unbalanced designs for linear structural relations in two-level data.British Journal of Mathematical and Statistical Psychology, 42, 215–232. Google Scholar
Meredith, W. & Tisak, J. (1990). Latent curve analysis.Psychometrika, 55, 107–122. Google Scholar
Muthén, B.O. (1984). A general structural equation model with dichotomous, ordered categorical and continuous latent indicators.Psychometrika, 49, 115–132. Google Scholar
Muthén, B.O. (1985). A method for studying the homogeneity of test items with respect to other relevant variables.Journal of Educational Statistics, 10, 121–132. Google Scholar
Muthén, B.O. (1989). Latent variable modeling in heterogeneous populations.Psychometrika, 54, 557–585. Google Scholar
Muthén, B.O. (1997). Latent variable modeling of longitudinal and multilevel data. In A.E. Raftery (Ed.),Sociological Methodology 1997 (pp. 453–480). Cambridge, MA: Blackwell. Google Scholar
Muthén, B.O. (2002). Beyond SEM: General latent variable modeling.Behaviormetrika, 29, 81–117. Google Scholar
Muthén, L.K., & Muthén, B.O. (1998).Mplus User's Guide. Los Angeles, CA: Muthén & Muthén. Google Scholar
Neale, M.C., & Cardon, L.R. (1992).Methodology for Genetic Studies of Twins and Families. London: Kluwer. Google Scholar
Pickles, A., Pickering, K., Simonoff, E., Meyer, J., Silberg, J., & Maes, H. (1998). Genetic clocks and soft events: A twin model for pubertal development and other recalled sequences of developmental milestones.Behavior Genetics, 28, 243–253. Google Scholar
Plummer, M., & Clayton, D. (1993). Measurement error in dietary assessment: an investigation using covariance structure models. Part II.Statistics in Medicine, 12, 937–948. Google Scholar
Poon, W.-Y., & Lee, S.-Y. (1992). Maximum likelihood and generalized least squares analyses of two-level structural equation models.Statistics and Probability Letters, 14, 25–30. Google Scholar
Rabe-Hesketh, S., & Pickles, A. (1999). Generalised linear latent and mixed models. In H. Friedl, A. Berghold & G. Kauermann (Eds.),Proceedings of the 14th International Workshop on Statistical Modeling (pp. 332–339). Graz, Austria.
Rabe-Hesketh, S., Pickles, A., & Skrondal, A. (2001a). GLLAMM: A general class of multilevel models and a Stata program.Multilevel Modelling Newsletter, 13, 17–23. Google Scholar
Rabe-Hesketh, S., Pickles, A., & Skrondal, A. (2001b).GLLAMM Manual. Tech. rept. 2001/01. Department of Biostatistics and Computing, Institute of Psychiatry, King's College, University of London. Downloadable from http://www.gllamm.org.
Rabe-Hesketh, S., Pickles, A., & Skrondal, A. (2003). Correcting for covariate measurement error in logistic regression using nonparametric maximum likelihood estimation.Statistical Modelling, 3, 215–232. Google Scholar
Rabe-Hesketh, S., Pickles, A., & Taylor, C. (2000). sg129: Generalized linear latent and mixed models.Stata Technical Bulletin, 53, 47–57. Google Scholar
Rabe-Hesketh, S., & Skrondal, A. (2001). Parameterization of multivariate random effects models for categorical data.Biometrics, 57, 1256–1264. Google Scholar
Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2002). Reliable estimation of generalized linear mixed models using adaptive quadrature.The Stata Journal, 2, 1–21. Google Scholar
Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2003). Maximum likelihood estimation of generalized linear models with covariate measurement error.The Stata Journal, 3, 386–411. Google Scholar
Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects.Journal of Econometrics, in press.
Rabe-Hesketh, S., Toulopoulou, T., & Murray, R. (2001). Multilevel modeling of cognitive function in schizophrenic patients and their first degree relatives.Multivariate Behavioral Research, 36, 279–298. Google Scholar
Rabe-Hesketh, S., Yang, S., & Pickles, A. (2001). Multilevel models for censored and latent responses.Statistical Methods in Medical Research, 10, 409–427. Google Scholar
Rasbash, J., Browne, W., Goldstein, H., Yang, M., Plewis, I., Healy, M., Woodhouse, G., Draper, D., Langford, I., & Lewis, T. (2000).A User's Guide to MLwiN (second edition). London: Institute of Education, University of London. Google Scholar
Raudenbush, S.W. (1995). Maximum likelihood estimation for unbalanced multilevel covariance structure models via the EM algorithm.British Journal of Mathematical and Statistical Psychology, 48, 359–370. Google Scholar
Raudenbush, S.W., Bryk, A.S., Cheong, Y.F., & Congdon, R. (2000).HLM 5: Hierarchical Linear and Nonlinear Modeling. Lincolnwood, IL: Scientific Software International. Google Scholar
Raudenbush, S.W., & Bryk, A.S. (2002).Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage. Google Scholar
Raudenbush, S.W., & Sampson, R. (1999a). Assessing direct and indirect effects in multilevel designs with latent variables.Sociological Methods & Research, 28, 123–153. Google Scholar
Raudenbush, S.W., & Sampson, R. (1999b). Ecometrics: toward a science of assessing ecological settings, with application to the systematic social observation of neighborhoods. In P.V. Marsden (Ed.),Sociological Methodology 1999, vol. 29 (pp. 1–41). Oxford: Blackwell. Google Scholar
Rijmen, F., Tuerlinckx, F., De Boeck, P., and Kuppens, P. (2003). A nonlinear mixed model framework for IRT models.Psychological Methods, 8, 185–205. Google Scholar
Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models.Psychometrika, 49, 37–47. Google Scholar
Skrondal, A., & Laake, P. (2001). Regression among factor scores.Psychometrika, 66, 563–576. Google Scholar
Skrondal, A., & Rabe-Hesketh, S. (2003a). Multilevel logistic regression for polytomous data and rankings.Psychometrika, 68, 267–287. Google Scholar
Skrondal, A., & Rabe-Hesketh, S. (2003b). Some applications of generalized linear latent and mixed models in epidemiology: Repeated measures, measurement error and multilevel modeling.Norwegian Journal of Epidemiology, 13, 265–278. Google Scholar
Skrondal, A., & Rabe-Hesketh, S. (2004a).Generalized latent variable modeling: Multilevel, longitudinal and structural equation models. Boca Raton, FL: Chapman & Hall/CRC. Google Scholar
Skrondal, A., & Rabe-Hesketh, S. (2004b). Generalized linear latent and mixed models with composite links and exploded likelihoods. In A. Biggeri, E. Dreassi, C. Lagazio & M. Marchi (Eds.),Proceedings of the 19th International Workshop on Statistical Modeling (pp. 27–39). Florence, Italy: Firenze University Press. Google Scholar
Skrondal, A., Rabe-Hesketh, S., & Pickles, A. (2002). Informative dropout and measurement error in cluster randomised trials. Paper presented at the International Biometric Society Conference 2002, Freiburg.
Social and Community Planning Research (1987).British Social Attitudes Panel Survey, 1983–1986 [Computer file] SN: 2197. Colchester, Essex: The Data Archive [Distributor]. Google Scholar
StataCorp. (2003).Stata Statistical Software: Release 8.0. College Station, TX: Stata Corporation. Google Scholar
Takane, Y. (1987). Analysis of covariance structures and probabilistic binary data.Communication & Cognition, 20, 45–62. Google Scholar
Wiggins, R.D., Ashworth, K., O'Muircheartaigh, C.A., & Galbraith, J.I. (1990). Multilevel analysis of attitudes to aboration.The Statistician, 40, 225–234. Google Scholar
Yang, S., & Pickles, A. (2004). Multilevel latent models for multivariate responses subject to measurement error.Submitted for publication.
Yang, S., Pickles, A., & Taylor, C. (1999). Multilevel latent variable model for analysing two-phase survey data. In H. Friedl, A. Berghold, & G. Kauermann (Eds.)Proceedings of the 14th International Workshop on Statistical Modeling (pp. 402–408). Graz, Austria.