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Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study
Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the… Expand
Exploratory Structural Equation Modeling
Exploratory factor analysis (EFA) is a frequently used multivariate analysis technique in statistics. Jennrich and Sampson (1966) solved a significant EFA factor loading matrix rotation problem by… Expand
Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus
This article discusses alternatives to single-step mixture modeling. A 3-step method for latent class predictor variables is studied in several different settings, including latent class analysis,… Expand
Bayesian structural equation modeling: a more flexible representation of substantive theory.
This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate… Expand
Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students' Evaluations of University Teaching
This study is a methodological-substantive synergy, demonstrating the power and flexibility of exploratory structural equation modeling (ESEM) methods that integrate confirmatory and exploratory… Expand
A new look at the big five factor structure through exploratory structural equation modeling.
- H. Marsh, O. Luedtke, +4 authors B. Nagengast
- Psychology, Medicine
- Psychological assessment
- 1 September 2010
NEO instruments are widely used to assess Big Five personality factors, but confirmatory factor analyses (CFAs) conducted at the item level do not support their a priori structure due, in part, to… Expand
The multilevel latent covariate model: a new, more reliable approach to group-level effects in contextual studies.
- O. Luedtke, H. Marsh, A. Robitzsch, U. Trautwein, T. Asparouhov, B. Muthén
- Mathematics, Medicine
- Psychological methods
- 1 September 2008
In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g.,… Expand
Sampling Weights in Latent Variable Modeling
- T. Asparouhov
- Computer Science
- 1 July 2005
This article reviews several basic statistical tools needed for modeling data with sampling weights that are implemented in Mplus Version 3. Expand
Auxiliary Variables in Mixture Modeling : A 3-Step Approach Using Mplus
This paper discusses alternatives to single-step mixture modeling. A 3step method for latent class predictor variables is studied in several different settings including latent class analysis, latent… Expand