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Julia-Kim Walther; Martin Hecht; Benjamin Nagengast; Steffen Zitzmann – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A two-level data set can be structured in either long format (LF) or wide format (WF), and both have corresponding SEM approaches for estimating multilevel models. Intuitively, one might expect these approaches to perform similarly. However, the two data formats yield data matrices with different numbers of columns and rows, and their "cols :…
Descriptors: Data, Monte Carlo Methods, Statistical Distributions, Matrices
Enders, Craig K.; Tofighi, Davood – Structural Equation Modeling: A Multidisciplinary Journal, 2008
The purpose of this study was to examine the impact of misspecifying a growth mixture model (GMM) by assuming that Level-1 residual variances are constant across classes, when they do, in fact, vary in each subpopulation. Misspecification produced bias in the within-class growth trajectories and variance components, and estimates were…
Descriptors: Structural Equation Models, Computation, Monte Carlo Methods, Evaluation Methods
Kaplan, David – 1993
The impact of the use of data arising from balanced incomplete block (BIB) spiralled designs on the chi-square goodness-of-fit test in factor analysis is considered. Data from BIB designs posses a unique pattern of missing data that can be characterized as missing completely at random (MCAR). Standard approaches to factor analyzing such data rest…
Descriptors: Chi Square, Computer Simulation, Correlation, Factor Analysis
Furlow, Carolyn F.; Beretvas, S. Natasha – Psychological Methods, 2005
Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for…
Descriptors: Rejection (Psychology), Monte Carlo Methods, Least Squares Statistics, Correlation