Publication Date
In 2025 | 0 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 10 |
Descriptor
Source
Structural Equation Modeling:… | 10 |
Author
Kwok, Oi-Man | 2 |
Luo, Wen | 2 |
Anthony, James C. | 1 |
Chen, Qi | 1 |
Chung, Hwan | 1 |
Davison, Mark L. | 1 |
Eduardo Estrada | 1 |
Fan, Xitao | 1 |
Jackman, M. Grace-Anne | 1 |
Jeffrey R. Harring | 1 |
Ji Seung Yang | 1 |
More ▼ |
Publication Type
Journal Articles | 10 |
Reports - Research | 10 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Early Childhood Longitudinal… | 1 |
National Longitudinal Survey… | 1 |
What Works Clearinghouse Rating
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
Nuria Real-Brioso; Eduardo Estrada; Pablo F. Cáncer – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Accelerated longitudinal designs (ALDs) provide an opportunity to capture long developmental periods in a shorter time framework using a relatively small number of assessments. Prior literature has investigated whether univariate developmental processes can be characterized with data obtained from ALDs. However, many important questions in…
Descriptors: Longitudinal Studies, Psychology, Cognitive Development, Brain Hemisphere Functions
Xiaying Zheng; Ji Seung Yang; Jeffrey R. Harring – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Measuring change in an educational or psychological construct over time is often achieved by repeatedly administering the same items to the same examinees over time and fitting a second-order latent growth curve model. However, latent growth modeling with full information maximum likelihood (FIML) estimation becomes computationally challenging…
Descriptors: Longitudinal Studies, Data Analysis, Item Response Theory, Structural Equation Models
Chung, Hwan; Anthony, James C. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
This article presents a multiple-group latent class-profile analysis (LCPA) by taking a Bayesian approach in which a Markov chain Monte Carlo simulation is employed to achieve more robust estimates for latent growth patterns. This article describes and addresses a label-switching problem that involves the LCPA likelihood function, which has…
Descriptors: Bayesian Statistics, Statistical Analysis, Markov Processes, Monte Carlo Methods
Kim, Su-Young – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Just as growth mixture models are useful with single-phase longitudinal data, multiphase growth mixture models can be used with multiple-phase longitudinal data. One of the practically important issues in single- and multiphase growth mixture models is the sample size requirements for accurate estimation. In a Monte Carlo simulation study, the…
Descriptors: Structural Equation Models, Sample Size, Computation, Monte Carlo Methods
Kwok, Oi-Man; Luo, Wen; West, Stephen G. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Some nonlinear developmental phenomena can be represented by using a simple piecewise procedure in which 2 linear growth models are joined at a single knot. The major problem of using this piecewise approach is that researchers have to optimally locate the knot (or turning point) where the change in the growth rate occurs. A relatively simple way…
Descriptors: Monte Carlo Methods, Longitudinal Studies, Data, Structural Equation Models
Peugh, James; Fan, Xitao – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Growth mixture modeling (GMM) has become a more popular statistical method for modeling population heterogeneity in longitudinal data, but the performance characteristics of GMM enumeration indexes in correctly identifying heterogeneous growth trajectories are largely unknown. Few empirical studies have addressed this issue. This study considered…
Descriptors: Structural Equation Models, Statistical Analysis, Longitudinal Studies, Evaluation Research
Chen, Qi; Kwok, Oi-Man; Luo, Wen; Willson, Victor L. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Growth mixture modeling (GMM) is a relatively new technique for analyzing longitudinal data. However, when applying GMM, researchers might assume that the higher level (nonrepeated measure) units (e.g., students) are independent from each other even though it might not always be true. This article reports the results of a simulation study…
Descriptors: Longitudinal Studies, Data Analysis, Models, Monte Carlo Methods
Leite, Walter L.; Sandbach, Robert; Jin, Rong; MacInnes, Jann W.; Jackman, M. Grace-Anne – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and…
Descriptors: Structural Equation Models, Probability, Computation, Observation
Shin, Tacksoo; Davison, Mark L.; Long, Jeffrey D. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
The purpose of this study is to investigate the effects of missing data techniques in longitudinal studies under diverse conditions. A Monte Carlo simulation examined the performance of 3 missing data methods in latent growth modeling: listwise deletion (LD), maximum likelihood estimation using the expectation and maximization algorithm with a…
Descriptors: Sample Size, Monte Carlo Methods, Structural Equation Models, Data Collection