Applied Latent Class Analysis

Applied Latent Class Analysis presents new techniques for the analysis of categorical data - one of the fastest growing areas in applied statistics. These techniques handle data where one cannot compute meaningful averages (for example, nation of origin, religion, type of disease, political party identification). The essays, written by leading figures in the field, introduce innovative approaches to these data and illustrate practical applications.

Andries van der Ark, Tilburg University Ulf Boekenholt, University of Illinois, Champaign-Urbana Linda M. Collins, Pennsylvania State University Marcel Croon, Tilburg University C. Mitchell Dayton, University of Maryland, College Park Wayne S. DeSarbo, Pennsylvania State University Brian P. Flaherty, Pennsylvania State University Anton K. Formann, University of Vienna Leo A. Goodman, University of California, Berkeley Jacques A. Hagenaars, Tilburg University Peter G. M. van der Heijden, Utrecht University Thomas Kohlman, University of Luebeck Rolf Langeheine, University of Kiel Allan L. McCutcheon, University of Nebraska, Lincoln George B. Macready, University of Maryland, College Park Jay Magidson, Statistical Innovations, United States Robert D. Mare, University of California, Los Angeles Ab Mooijaart, University of Leiden Frank van der Pol, Central Bureau of Statistics, the Netherlands Tamas Rudas, Eotvos Lorand University Jeroen K. Vermunt, Tilburg University John Robert Warren, University of Washington, Seattle Michel Wedel, University of Groningen Christopher Winship, Harvard University