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Yuan Fang; Lijuan Wang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…
Descriptors: Structural Equation Models, Bayesian Statistics, Monte Carlo Methods, Longitudinal Studies
Xu, Menglin; Logan, Jessica A. R. – Journal of Research on Educational Effectiveness, 2021
Planned missing data designs allow researchers to have highly-powered studies by testing only a fraction of the traditional sample size. In two-method measurement planned missingness designs, researchers assess only part of the sample on a high-quality expensive measure, while the entire sample is given a more inexpensive, but biased measure. The…
Descriptors: Longitudinal Studies, Research Design, Research Problems, Structural Equation Models
Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S. – Psychometrika, 2012
We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also…
Descriptors: Structural Equation Models, Longitudinal Studies, Data Analysis, Reliability
Rueger, Sandra Yu; Jenkins, Lyndsay N. – School Psychology Quarterly, 2014
The purpose of the current study is to investigate the effects of frequency of peer victimization experiences on psychological and academic adjustment during early adolescence, with a focus on testing psychological adjustment as a mediator, as well as differences based on gender and type of victimization. The sample in this short-term longitudinal…
Descriptors: Bullying, Peer Relationship, Victims, Incidence
Reichardt, Charles S. – Multivariate Behavioral Research, 2011
Maxwell, Cole, and Mitchell (2011) demonstrated that simple structural equation models, when used with cross-sectional data, generally produce biased estimates of meditated effects. I extend those results by showing how simple structural equation models can produce biased estimates of meditated effects when used even with longitudinal data. Even…
Descriptors: Structural Equation Models, Statistical Data, Longitudinal Studies, Error of Measurement
Liu, Hui; Powers, Daniel A. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
This article applies growth curve models to longitudinal count data characterized by an excess of zero counts. We discuss a zero-inflated Poisson regression model for longitudinal data in which the impact of covariates on the initial counts and the rate of change in counts over time is the focus of inference. Basic growth curve models using a…
Descriptors: Smoking, Structural Equation Models, Longitudinal Studies, Regression (Statistics)