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Latent Variable Models

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Modern Statistical Methods for HCI

Part of the book series: Human–Computer Interaction Series ((HCIS))

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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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Notes

  1. 1.

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

  2. 2.

    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).

  3. 3.

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

  4. 4.

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

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Author information

Authors and Affiliations

  1. Baylor University, Waco, TX, USA

    A. Alexander Beaujean & Grant B. Morgan

Authors
  1. A. Alexander Beaujean
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  2. Grant B. Morgan
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Correspondence to A. Alexander Beaujean .

Editor information

Editors and Affiliations

  1. Moray School of Education, Edinburgh University, Edinburgh, United Kingdom

    Judy Robertson

  2. Donders Centre for Cognition, Radboud University Nijmegen, Tilburg, The Netherlands

    Maurits Kaptein

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Beaujean, A.A., Morgan, G.B. (2016). Latent Variable Models. In: Robertson, J., Kaptein, M. (eds) Modern Statistical Methods for HCI. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-26633-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-26633-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26631-2

  • Online ISBN: 978-3-319-26633-6

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