Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and
statistical inference. There has been a recent upsurge in the application of latent class …

This paper presents the top 10 data mining algorithms identified by the IEEE International
Conference on Data Mining (ICDM) in December 2006: C4. 5, k-Means, SVM, Apriori, EM …

The present guide provides a practical guide to conducting latent profile analysis (LPA) in
the Mplus software system. This guide is intended for researchers familiar with some latent …

Understanding how teacher stress, burnout, coping, and self-efficacy are interrelated can
inform preventive and intervention efforts to support teachers. In this study, we explored …

Finite mixture models are being used increasingly to model a wide variety of random
phenomena for clustering, classification and density estimation. mclust is a powerful and …

This study examined the stress and coping patterns of middle school teachers. A final
teacher sample of 102 and student sample of 1450 agreed to participate in the study. We …

Presents a useful guide for applications of SEM whilst systematically demonstrating various
SEM models using Mplus Focusing on the conceptual and practical aspects of Structural …

Although prediction of class membership from observed variables in latent class analysis is
well understood, predicting an observed distal outcome from latent class membership is …

Selecting the number of different classes that will be assumed to exist in the population is an
important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) …

We propose a general method for constructing confidence sets and hypothesis tests that
have finite-sample guarantees without regularity conditions. We refer to such procedures as …