Publications

Note that this page has not been updated since 2015. For more recent publications on extensions and applications on latent class and related models, please consult the personal website of Jeroen Vermunt listed below.

Latent Class Modeling

Articles and book chapters

Bakk, Zs., Oberski, D., and Vermunt, J.K. (2016). Relating latent class membership to continuous distal outcomes: improving the LTB approach and a modified three-step implementation. Structural Equation Modeling.

Bakk, Zs., Tekle, F.B., and Vermunt, J.K. (2013). Estimating the association between latent class membership and external variables using bias adjusted three-step approaches. Sociological Methodology, 43, 272-311.

Bakk, Zs., and Vermunt, J.K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling.

Gudicha, D.W., and Vermunt, J.K. (2013). Mixture model clustering with covariates using adjusted three-step approaches. B. Lausen, D. van den Poel, and A. Ultsch (eds), Algorithms from and for Nature and Life; Studies in Classification, Data Analysis, and Knowledge Organization. 87-93. Heidelberg: Springer-Verlag GmbH.

Magidson, J., and Vermunt, J.K. (2002). Latent class models for clustering: a comparison with K-means. Canadian Journal of Marketing Research, 20, 36-43. (pdf)

Magidson, J. and Vermunt, J.K. (2002). Latent class modeling as a probabilistic extension of K-means clustering. Quirk’s Marketing Research Review, March 2002, 20 & 77-80. (pdf)

Magidson, J., and Vermunt, J.K. (2004). Latent class models. In: Kaplan., D (Ed.), The Sage Handbook of Quantitative Methodology for the Social Sciences, Chapter 10, 175-198. Thousand Oaks: Sage Publications. (pdf)

Ross, R.W., Galsky, M.D., Scher, H.I., Magidson, J., Wassmann, K., Lee, G.S., Katz, L., Subudhi, S., Anand, A., Fleisher, M., Kantoff, P., and Oh, W. (2012). A whole-blood RNA transcript-based prognostic model in men with castration-resistant prostate cancer: a prospective study. The Lancet Oncology, 13( 11), 1105–1113.

Saenger, Y.M., Magidson, J., Chi-Hung Liaw, B., de Mol, E., Harcharik, S., Fu, Y., Wassmann, K., Fisher, D., Kirkwood, J., Oh, W.K., and Friedlander, P. (2014). Blood mRNA signature to predict survival in patients with metastatic melanoma treated with tremelimumab. Clinical Cancer Research Journal, 20(24).

van den Bergh, M., Schmittmann, V.D., and Vermunt, J.K. (2017). Building latent class trees, with an application to social capital. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 13(Suppl. 1), 12-22. (pdf)

Vermunt, J.K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18, 450-469.

Vermunt, J.K., van Ginkel, J.R., van der Ark, L.A., and Sijtsma, K. (2008). Multiple imputation of incomplete categorical data using latent class analysis. Sociological Methodology, 33, 369-397. (pdf)

Vermunt, J.K., and Magidson, J. (2007). Latent class analysis with sampling weights: A maximum likelihood approach. Sociological Methods and Research, 36, 87-111. (pdf)

Vermunt, J.K., and Magidson, J. (2003). Latent class models for classification. Computational Statistics and Data Analysis, 41,3-4, 531-537. (pdf)

Vermunt, J.K., and Magidson, J. (2002). Latent class cluster analysis. In: J. A. Hagenaars and A. L. McCutcheon (Eds.), Applied Latent Class Analysis, 89-106. Cambridge: Cambridge University Press. (pdf)

Yardley, D.A., Tripathy, D., Brufsky, A.M., Rugo, H.S., Kaufman, P.A., Mayer, M., Magidson, J., Yoo, B., Quah, C., and Ulcickas Yood, M. (2014). Long-term survivor characteristics in HER2-positive metastatic breast cancer from registHER. British Journal of Cancer, 110(11), 2756-64.

Proceedings and working papers

Magidson, J., and Vermunt, J.K. (2000). Bi-plots and related graphical displays based on latent class factor and cluster models. In: Jansen, W., and Bethlehem, J.G. (Eds.), Proceedings in Computational Statistics 2000, 121-122. Statistics Netherlands. ISSN 0253-018X. (pdf)

Magidson, J., and Vermunt, J.K. (2002). Nontechnical introduction to latent class models. Statistical Innovations White Paper #1. (pdf)

Magidson, J., and Vermunt, J.K. (2006). Use of latent class regression models with a random intercept to remove overall response level effects in rating data. A. Rizzi, and M Vichi (eds.), Proceedings in Computational Statistics , 351-360. Heidelberg: Springer. (pdf)

Popper, R., Kroll, J., and Magidson, J. (2004). Applications of latent class models to food product development: a case study. Sawtooth Software Proceedings, 2004. (pdf)

Vermunt, J.K., and Magidson, J. (2000). Graphical displays for latent class cluster and latent class factor models. In: Blasius, J., Hox, J., de Leuw, E., and Schmith, P. (Eds.), Proceedings of the Fifth International Conference on Logic and Methodology, TT-Publications. (pdf)

Recommended book

Bartholomew, D. J., and Knott, M., and Moustaki, I. (2011) Latent variable models and factor analysis: a unified approach, 3rd ed., John Wiley & Sons, London, UK. ISBN 9780470971925.

Encyclopedia entries

Vermunt, J.K. and Magidson, J. (2004). Latent class analysis. In: M.S. Lewis-Beck, A. Bryman, and T.F. Liao (eds.), The Sage Encyclopedia of Social Sciences Research Methods, 549-553. Thousand Oaks, CA: Sage Publications. (pdf)

Vermunt, J.K. and Magidson, J. (2004). Latent variable. In: M.S. Lewis-Beck, A. Bryman, and T.F. Liao (eds.), The Sage Encyclopedia of Social Sciences Research Methods, 555-556. Thousand Oaks, CA: Sage Publications. (pdf)

Vermunt, J.K. and Magidson, J. (2004). Local independece. In: M.S. Lewis-Beck, A. Bryman, and T.F. Liao (eds.), The Sage Encyclopedia of Social Sciences Research Methods, 580-58. Thousand Oaks, CA: Sage Publications. (pdf)

Vermunt, J.K. and Magidson, J. (2004). Non-parametric random-effects model. In: M.S. Lewis-Beck, A. Bryman, and T.F. Liao (eds.), The Sage Encyclopedia of Social Sciences Research Methods, 732-733. Thousand Oaks, CA: Sage Publications. (pdf)

Vermunt, J.K. and Magidson, J. (2005). Structural equation models: Mixture models. In: B. Everitt and D. Howell, (eds.), Encyclopedia of Statistics in Behavioral Science, 1922–1927. Chichester, UK: Wiley. (pdf)

 

Discrete Factor Models

Articles and book chapters

Magidson, J., and Vermunt, J.K. (2001). Latent class factor and cluster models, bi-plots and related graphical displays. Sociological Methodology, 31, 223-264. (pdf)

Magidson, J., and Vermunt, J.K. (2003). Comparing latent class factor analysis with traditional factor analysis for datamining. In: Bozdogan, H. (Ed), Statistical Datamining & Knowledge Discovery, Chapter 22, 373-383. Boca Raton: Chapman & Hall/CRC. CRC Press. (pdf)

Vermunt, J.K., and Magidson, J. (2004). Factor analysis with categorical indicators: a comparison between traditional and latent class approaches. In: Van der Ark, A., Croon, M.A., and Sijtsma, K. (Eds), New Developments in Categorical Data Analysis for the Social and Behavioral Sciences. Erlbaum. (pdf)

Latent Class Discrete Choice Modeling

Articles and book chapters

Bradlow, E. (2004). Current issues and a “wish list” for conjoint analysis. Applied Stochastic Models in Business and Industry. (pdf)

Comments by Jay Magidson, SI and Jeroen K. Vermunt, Tilburg University. (pdf)

Comments by Jordan J. Louviere, University of Technology, Sydney.(pdf)

Comments by Bryan Orme, Sawtooth Software. (pdf)

Comments by Joffre Swait, Advanis Inc. (pdf)

Rejoinder to reviewer comments by Eric Bradlow, University of Pennsylvania.(pdf)

van den Bergh, van Kollenberg, G. H., & Vermunt, J. K. (2018).  Deciding on the number of classes of a latent class tree. (pdf)

Proceedings and working papers

Magidson, J. & Bennett, G. (2016).  How to develop a MaxDiff typing tool to assign new cases to meaningful segments.  2016 ART Forum. (pdf)

Magidson, J. Thomas, D., Vermunt, J.K. (2009). A new model for the fusion of MaxDiff scaling and ratings data. 2009 Sawtooth Software Proceedings, 83-103. (pdf)

Magidson, J., and Vermunt, J.K. (2007). Removing the scale confound in multinomial logit choice models to obtain better estimates of preference. October 2007 Sawtooth Software Conference Proceedings. (pdf)

Magidson, J., Eagle, T., and Vermunt, J.K. (2005). Using parsimonious conjoint and choice models to improve the accuracy of out-of-sample share predictions. Presented at the 2005 ART Forum. (pdf)

Magidson, J., Eagle, T., and Vermunt, J.K. (2003). New developments in latent class choice models. April 2003 Sawtooth Software Conference Proceedings, 89-112. (pdf)

Multilevel and Longitudinal Modeling

Articles and book chapters

Bennink, M., Croon, M.A., Keuning, J., and Vermunt, J.K. (2014). Measuring student ability, classifying schools, and detecting item-bias at school-level based on student-level dichotomous attainment items. Journal of Educational and Behavioral Statistic, 39(3), 180-202.

Bennink, M., Croon, M.A., & Vermunt, J.K. (2013). Micro–macro multilevel analysis for discrete data: A latent variable approach and an application on personal network data. Sociological Methods and Research, 42(4), 431-457.

Bijmolt, T.H., Paas, L.J., Vermunt , J.K. (2003). Country and consumer segmentation: Multi-level latent class analysis of Financial product ownership. International Journal of Research in Marketing, 21, 323-340. (pdf)

Magidson, J., Vermunt, J.K., Tran, B. (2008). Using a mixture latent Markov model to analyze longitudinal U.S. employment data involving measurement error. In: Shigemasu, K., Okada, A., Imaizum, T., and Hoshino, T. (Eds). New Trends in Psychometrics. Frontiers Science Series No. 55 (FSS-55), 235-242. Universal Academy Press, Inc. (pdf)

Vermunt, J.K. (2003). Multilevel latent class models. Sociological Methodology, 33, 213-239. (pdf)

Vermunt, J.K., and Magidson, J. (2005). Hierarchical mixture models for nested data structures. In: Weihs, C., and Gaul, W., Classification: The Ubiquitous Challenge, 176-183. Heidelberg: Springer. (pdf)

Vermunt, J.K., Tran, B., and Magidson, J. (2008). Latent class models in longitudinal research. In: Menard, S. (ed.), Handbook of Longitudinal Research: Design, Measurement, and Analysis, pp. 373-385. Burlington, MA: Elsevier. (pdf)

Proceedings and working papers

Magidson, J. (2013). Using mixture latent Markov models for analyzing change with longitudinal data. . Presented at Modern Modeling Methods (M3) 2013, University of Connecticut. (pdf)

CHAID (Chi-Squared Automatic Interaction Detection)

Articles and book chapters

Magidson, J. (1993). The Use of the New Ordinal Algorithm in CHAID to Target Profitable Segments. Journal of Database Marketing, London: Henry Stewart Publication.

Magidson, J. (1994). The CHAID Approach to Segmentation Modeling: CHi-squared Automatic Interaction Detection. In: Bagozzi, R. (ed.), Advanced Methods of Marketing Research. Blackwell.

Magidson, J., and Vermunt, J.K. (2005). An extension of the CHAID tree-based segmentation algorithm to multiple dependent variables. In: Weihs, C., and Gaul, W. (eds.),   Classification: The Ubiquitous Challenge, 176-183. Heidelberg: Springer. (pdf)

Correlated Component Regression (CCR)

Articles and book chapters

Magidson, J. (2013). Correlated component regression: Re-thinking regression in the presence of near collinearity. In: New perspectives in partial least squares and related methods. Springer Verlag. (pdf)

Proceedings and working papers

Magidson, J. (2010). Correlated component regression: A prediction/classification methodology for possibly many features. 2010 JSM Proceedings of American Statistical Association, Section on Statistical Learning and Data Mining, 4372-4386. (pdf)

Magidson, J., and Wassmann, K. (2010). The role of proxy genes in predictive models: An application to early detection of prostate cancer. 2010 JSM Proceedings of American Statistical Association, Biometrics Section, 2739-2753. (pdf)

Categorical data

Articles and book chapters

Magidson, J. (1982). Some Common Pitfalls in the Causal Analysis of Categorical Data. Journal of Marketing Research, November issue.

Magidson, J. (1994). Multivariate Statistical Models for Categorical Data. In: Bagozzi, R. (eds), Advanced Methods of Marketing Research, Blackwell.

Magidson, J. (1996). Maximum Likelihood Assessment of Clinical Trials Based on an Ordered Categorical Response. Drug Information Journal, 30 (1).

Reviews of Statistical Innovations Software

LatentGOLD®

Deal, K. (2008). A big step forward in latent class analysis. Marketing Research Magazine, Fall, 48-50. (pdf)

Haughton, D., Legrand, P., Woolford, S, (2009). Review of three latent class cluster analysis packages: LatentGOLD, polka, and MCLUST. The American Statistician, 63(1), 81-91. (pdf)

Kent, P., Jensen, R.k., and Kongsted, A. (2014). A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB. BMC Medical Research Methodology (14), 113. (link)

GOLDMineR®

Deal, K. (2004). Mining for gold gets easier and a lot more fun!. Marketing Research Magazine, Spring, 1-7. (pdf)

SI-CHAID®

Deal, K. (2005). Deeper into trees. Marketing Research Magazine, Summer, 38-40. (pdf)