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2016 | OriginalPaper | Chapter

10. Latent Variable Models

Authors : A. Alexander Beaujean, Grant B. Morgan

Published in: Modern Statistical Methods for HCI

Publisher: Springer International Publishing

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Abstract

Human-computer interaction research increasingly involves investigating psychological phenomena as latent variables. In this chapter, we discuss basic latent variable models using a path model approach. In addition, we given some examples of conducting a latent variable model analysis using the lavaan package in the R statistical programming language.

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previous chapter Bayesian Testing of Constrained Hypotheses

next chapter Using Generalized Linear (Mixed) Models in HCI

Those interested in a matrix-based approach should see Bollen’s (1989) classic text.

We assume that the LV is reflective (i.e., causes the MVs to covary). An alternative would be to have a formative LV, which are thought to result from the MVs’ covariation. For more information on formative LVs, see Bollen and Bauldry (2011).

For a more thorough explanation of the degrees of freedom concept, see Walker (1940).

For examples of fitting LV models in R using a package other than lavaan see Fox et al. (2012).

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Title: Latent Variable Models
Authors: A. Alexander Beaujean
Grant B. Morgan
Publisher: Springer International Publishing
Book: Modern Statistical Methods for HCI
Print ISBN: 978-3-319-26631-2

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

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-26633-6_10