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date: 05 April 2020

Abstract and Keywords

In this chapter I first describe core terminology, notation, and related readings for certain core clustering and classification techniques. I then discuss the theoretical underpinnings and practical applications of nonparametric techniques that do not require distributional assumptions on outcome variables followed by parametric/model-based techniques that do require such assumptions. In the former set, I specifically discuss hierarchical clustering techniques and K-means clustering techniques. In the latter set I specifically discuss univariate and multivariate finite mixture models, unrestricted latent class models, and restricted latent class models. I further show how so-called diagnostic classification models are a particularly useful class of restricted latent class models for calibration and scaling purposes in educational and psychological measurement.

Keywords: Clustering, classification, K-means cluster analysis, hierarchical cluster analysis, finite mixture models, unrestricted latent class models, restricted latent class models, diagnostic classification models

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