Cited by 1,429Psychometric Methodologies and TestingBayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference

Maximum Likelihood Estimation of Latent Interaction Effects with the LMS Method

Klein, Andreas, Moosbrugger, Helfried·2000·Psychometrika

Abstract

In the context of structural equation modeling, a general interaction model with multiple latent interaction effects is introduced. A stochastic analysis represents the nonnormal distribution of the joint indicator vector as a finite mixture of normal distributions. The Latent Moderated Structural Equations (LMS) approach is a new method developed for the analysis of the general interaction model that utilizes the mixture distribution and provides a ML estimation of model parameters by adapting the EM algorithm. The finite sample properties and the robustness of LMS are discussed. Finally, the applicability of the new method is illustrated by an empirical example.

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Crossref · 10.1007/bf02296338

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Authors
Klein, A., & Moosbrugger, H.
Affiliations
Goethe University Frankfurt (DE)
Corresponding author
Klein, Andreas
Venue
Psychometrika
Published
2000-12
Volume
65
Issue
4
Pages
457-474
ISSN
  • 0033-3123 (ISSN-L)
  • 1860-0980
OpenAlex
W2024507896
Cited by
1,429
References
1
Citations by year
2026: 130 · 2025: 93 · 2024: 82 · 2023: 88 · 2022: 101 · 2021: 112 · 2020: 100 · 2019: 107 · 2018: 84 · 2017: 79 · 2016: 65 · 2015: 64 · 2014: 58 · 2013: 56 · 2012: 48
FWCI
3.65
Type
journal article
Language
en
Open access
closed
Locations
  • Psychometrika (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
LMS algorithm, maximum likelihood estimation, expectation-maximization algorithm, finite sample properties, robustness, structural equation modeling

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