Advanced Nonlinear Latent Variable Modeling: Distribution Analytic LMS and QML Estimators of Interaction and Quadratic Effects

By Augustin Kelava, Christina S. Werner, K. Schermelleh-Engel, Helfried Moosbrugger, Dieter Zapf, Yue Ma, Heining Cham, L. Aiken, S. West. 2011

178 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Experimental and Cognitive Psychology, Food Science

View Paper: https://www.semanticscholar.org/paper/7abfcec06f07dae6de6a6d23ca16baae4398e662

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In Developmental and Educational Psychology

In Experimental and Cognitive Psychology

In Food Science

Citations in the corpus

A Comparison of Strategies for Forming Product Indicators for Unequal Numbers of Items in Structural Equation Models of Latent Interactions (2013, methodology): “As a distribution analytic approach (Kelava et al., 2011; Marsh et al., 2012), the LMS approach is based on the joint distribution of the indicators that are approximated by a finite mixture distribution, and the expectation maximization (EM) algorithm is applied to maximize the log-likelihood…”
Integrating Moderation and Mediation (2016, methodology): “As explained succinctly in Kelava et al. (2011), LMS is a distribution analytic approach.”
Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study. (2012, unknown): “Consistent with the assumptions of the LMS approach, we treated the indicator variables as continuous (Kelava et al., 2011; Klein & Moosbrugger, 2000).”
PLS' Janus Face – Response to Professor Rigdon's ‘Rethinking Partial Least Squares Modeling: In Praise of Simple Methods’ (2014, methodology): “Eη2η3 = q2q2Eη2η3 (16) Eη(2)1η2 = q 2 1q2Eη 2 1η2 (17) Eη1η 2 2 = q1q 2 2Eη1η 2 2 (18)”
Expectancy-value in mathematics, gender and socioeconomic background as predictors of achievement and aspirations : A multi-cohort study (2015, background): “LMS directly models the implied non-normal distribution of the latent outcome variables and its indicators (Kelava et al., 2011).”
A Nonlinear Structural Equation Mixture Modeling Approach for Nonnormally Distributed Latent Predictor Variables (2014, background): “When this assumption is violated, parameter estimates in the nonlinear structural model will be biased because of additional shared variance between the latent first-order and the product variables that is not accounted for in the model (Kelava et al., 2011; Marsh et al., 2004).”