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Erschienen in:

2016 | OriginalPaper | Buchkapitel

12. Mixture Models: Latent Profile and Latent Class Analysis

verfasst von : Daniel Oberski

Erschienen in: Modern Statistical Methods for HCI

Verlag: Springer International Publishing

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Abstract

Latent class analysis (LCA) and latent profile analysis (LPA) are techniques that aim to recover hidden groups from observed data. They are similar to clustering techniques but more flexible because they are based on an explicit model of the data, and allow you to account for the fact that the recovered groups are uncertain. LCA and LPA are useful when you want to reduce a large number of continuous (LPA) or categorical (LCA) variables to a few subgroups. They can also help experimenters in situations where the treatment effect is different for different people, but we do not know which people. This chapter explains how LPA and LCA work, what assumptions are behind the techniques, and how you can use R to apply them.

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Vorheriges Kapitel Using Generalized Linear (Mixed) Models in HCI

Nächstes Kapitel Fair Statistical Communication in HCI

Confusingly, sometimes latent class analysis is used as a broader term for mixture models.

This is not true, but the rest of the chapter is.

Apparently, Ms. Parveen is 213.4 cm and Mr. Dangi is 48.3 cm.

As can be gleaned from the figures, by “normal curve” I mean the probability density function.

We also need to know the proportion of men/women \(\pi _1^{X}\) but I will ignore that for the moment.

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Titel: Mixture Models: Latent Profile and Latent Class Analysis
verfasst von: Daniel Oberski
Verlag: Springer International Publishing
Buch: Modern Statistical Methods for HCI
Print ISBN: 978-3-319-26631-2

Electronic ISBN: 978-3-319-26633-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-26633-6_12