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Jie Fang; Zhonglin Wen; Kit-Tai Hau – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e.,…
Descriptors: Structural Equation Models, Mediation Theory, Data Analysis, Longitudinal Studies
Hancock, Gregory R.; Johnson, Tessa – AERA Online Paper Repository, 2018
Longitudinal models provide researchers with a framework for investigating key aspects of change over time, but rarely is "time" itself modeled as a focal parameter of interest. Rather than treat time as purely an index of measurement occasions, the proposed Time to Criterion (T2C) growth model allows for modeling individual variability…
Descriptors: Statistical Analysis, Longitudinal Studies, Time, Structural Equation Models
Hancock, Gregory R.; Schoonen, Rob – Language Learning, 2015
Although classical statistical techniques have been a valuable tool in second language (L2) research, L2 research questions have started to grow beyond those techniques' capabilities, and indeed are often limited by them. Questions about how complex constructs relate to each other or to constituent subskills, about longitudinal development in…
Descriptors: Structural Equation Models, Language Research, Second Language Learning, Statistical Analysis
Nese, Joseph F. T.; Lai, Cheng-Fei; Anderson, Daniel – Behavioral Research and Teaching, 2013
Longitudinal data analysis in education is the study growth over time. A longitudinal study is one in which repeated observations of the same variables are recorded for the same individuals over a period of time. This type of research is known by many names (e.g., time series analysis or repeated measures design), each of which can imply subtle…
Descriptors: Longitudinal Studies, Data Analysis, Educational Research, Hierarchical Linear Modeling
Grimm, Kevin J. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Latent difference score (LDS) models combine benefits derived from autoregressive and latent growth curve models allowing for time-dependent influences and systematic change. The specification and descriptions of LDS models include an initial level of ability or trait plus an accumulation of changes. A limitation of this specification is that the…
Descriptors: Structural Equation Models, Time, Change, Coding
Peugh, James L.; DiLillo, David; Panuzio, Jillian – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Mixed-dyadic data, collected from distinguishable (nonexchangeable) or indistinguishable (exchangeable) dyads, require statistical analysis techniques that model the variation within dyads and between dyads appropriately. The purpose of this article is to provide a tutorial for performing structural equation modeling analyses of cross-sectional…
Descriptors: Structural Equation Models, Data Analysis, Statistical Analysis, Computer Software
Raykov, Tenko; Zajacova, Anna – Structural Equation Modeling: A Multidisciplinary Journal, 2012
An interval estimation procedure for proportion of explained observed variance in latent curve analysis is discussed, which can be used as an aid in the process of choosing between linear and nonlinear models. The method allows obtaining confidence intervals for the R[squared] indexes associated with repeatedly followed measures in longitudinal…
Descriptors: Longitudinal Studies, Structural Equation Models, Computation, Goodness of Fit
Ghisletta, Paolo; McArdle, John J. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also increased in popularity. At the same time, the R…
Descriptors: Statistical Analysis, Structural Equation Models, Computation, Computer Software
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)
Mayer, Axel; Steyer, Rolf; Mueller, Horst – Structural Equation Modeling: A Multidisciplinary Journal, 2012
We present a 3-step approach to defining latent growth components. In the first step, a measurement model with at least 2 indicators for each time point is formulated to identify measurement error variances and obtain latent variables that are purged from measurement error. In the second step, we use contrast matrices to define the latent growth…
Descriptors: Statistical Analysis, Measurement, Structural Equation Models, Error of Measurement
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
Blozis, Shelley A.; Harring, Jeffrey R.; Mels, Gerhard – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Latent curve models offer a flexible approach to the study of longitudinal data when the form of change in a response is nonlinear. This article considers such models that are conditionally linear with regard to the random coefficients at the 2nd level. This framework allows fixed parameters to enter a model linearly or nonlinearly, and random…
Descriptors: Structural Equation Models, Longitudinal Studies, Guidelines, Computer Software
Grimm, Kevin J.; Ram, Nilam – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Nonlinear growth curves or growth curves that follow a specified nonlinear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this article we describe how a variety of sigmoid curves can be fit using the M"plus" structural modeling program and the nonlinear…
Descriptors: Structural Equation Models, Statistical Analysis, Computer Software, Longitudinal Studies
Flora, David B.; Curran, Patrick J.; Hussong, Andrea M.; Edwards, Michael C. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
A large literature emphasizes the importance of testing for measurement equivalence in scales that may be used as observed variables in structural equation modeling applications. When the same construct is measured across more than one developmental period, as in a longitudinal study, it can be especially critical to establish measurement…
Descriptors: Structural Equation Models, Item Response Theory, Measurement, Scores
Flora, David B. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Piecewise latent trajectory models for longitudinal data are useful in a wide variety of situations, such as when a simple model is needed to describe nonlinear change, or when the purpose of the analysis is to evaluate hypotheses about change occurring during a particular period of time within a model for a longer overall time frame, such as…
Descriptors: Structural Equation Models, Evaluation Methods, Equations (Mathematics), Longitudinal Studies
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