149352-Thumbnail Image.png
Description
For this thesis a Monte Carlo simulation was conducted to investigate the robustness of three latent interaction modeling approaches (constrained product indicator, generalized appended product indicator (GAPI), and latent moderated structural equations (LMS)) under high degrees of nonnormality of the

For this thesis a Monte Carlo simulation was conducted to investigate the robustness of three latent interaction modeling approaches (constrained product indicator, generalized appended product indicator (GAPI), and latent moderated structural equations (LMS)) under high degrees of nonnormality of the exogenous indicators, which have not been investigated in previous literature. Results showed that the constrained product indicator and LMS approaches yielded biased estimates of the interaction effect when the exogenous indicators were highly nonnormal. When the violation of nonnormality was not severe (symmetric with excess kurtosis < 1), the LMS approach with ML estimation yielded the most precise latent interaction effect estimates. The LMS approach with ML estimation also had the highest statistical power among the three approaches, given that the actual Type-I error rates of the Wald and likelihood ratio test of interaction effect were acceptable. In highly nonnormal conditions, only the GAPI approach with ML estimation yielded unbiased latent interaction effect estimates, with an acceptable actual Type-I error rate of both the Wald test and likelihood ratio test of interaction effect. No support for the use of the Satorra-Bentler or Yuan-Bentler ML corrections was found across all three methods.
Reuse Permissions


  • Download restricted.

    Details

    Title
    • Robustness of Latent variable interaction methods to nonnormal exogenous indicators
    Contributors
    Date Created
    2010
    Resource Type
  • Text
  • Collections this item is in
    Note
    • thesis
      Partial requirement for: M.A., Arizona State University, 2010
    • bibliography
      Includes bibliographical references (p. 53-57)
    • Field of study: Psychology

    Citation and reuse

    Statement of Responsibility

    Hei Ning Cham

    Machine-readable links