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Voelkle, Manuel C.; Oud, Johan H. L.; Davidov, Eldad; Schmidt, Peter – Psychological Methods, 2012
Panel studies, in which the same subjects are repeatedly observed at multiple time points, are among the most popular longitudinal designs in psychology. Meanwhile, there exists a wide range of different methods to analyze such data, with autoregressive and cross-lagged models being 2 of the most well known representatives. Unfortunately, in these…
Descriptors: Authoritarianism, Intervals, Structural Equation Models, Correlation
Enders, Craig K. – Psychological Methods, 2011
The past decade has seen a noticeable shift in missing data handling techniques that assume a missing at random (MAR) mechanism, where the propensity for missing data on an outcome is related to other analysis variables. Although MAR is often reasonable, there are situations where this assumption is unlikely to hold, leading to biased parameter…
Descriptors: Structural Equation Models, Social Sciences, Data, Attrition (Research Studies)
Muthen, Bengt; Asparouhov, Tihomir – Psychological Methods, 2012
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 zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed…
Descriptors: Factor Analysis, Cognitive Ability, Science Achievement, Structural Equation Models
Muthen, Bengt; Asparouhov, Tihomir; Hunter, Aimee M.; Leuchter, Andrew F. – Psychological Methods, 2011
This article uses a general latent variable framework to study a series of models for nonignorable missingness due to dropout. Nonignorable missing data modeling acknowledges that missingness may depend not only on covariates and observed outcomes at previous time points as with the standard missing at random assumption, but also on latent…
Descriptors: Structural Equation Models, Depression (Psychology), Models, Trend Analysis
Evaluating Model Fit for Growth Curve Models: Integration of Fit Indices from SEM and MLM Frameworks
Wu, Wei; West, Stephen G.; Taylor, Aaron B. – Psychological Methods, 2009
Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation…
Descriptors: Structural Equation Models, Goodness of Fit, Longitudinal Studies, Comparative Analysis
Cole, David A.; Martin, Nina C.; Steiger, James H. – Psychological Methods, 2005
The latent trait-state-error model (TSE) and the latent state-trait model with autoregression (LST-AR) represent creative structural equation methods for examining the longitudinal structure of psychological constructs. Application of these models has been somewhat limited by empirical or conceptual problems. In the present study, Monte Carlo…
Descriptors: Structural Equation Models, Computation, Longitudinal Studies, Monte Carlo Methods