Power of Latent Growth Modeling for Detecting Group Differences in Linear Growth Trajectory Parameters

By Xitao Fan. 2003

128 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b8fa587fca8abd752307febb2ffd4cb5062f8df6

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Citations in the corpus

Evaluating the Power of Latent Growth Curve Models to Detect Individual Differences in Change (2008, background): “Simulation studies of LGCMs have typically focused on other questions, such as detecting mean slope differences in multiple groups (Fan, 2003; Kim, 2005; Muthén & Curran, 1997).”
The Biasing Effects of Unmodeled ARMA Time Series Processes on Latent Growth Curve Model Estimates (2005, background): “, repeated measures analysis of variance), many researchers have argued for its superiority (e.g., Curran, 2000; Duncan, Duncan, Strycker, Li, & Alpert, 1999; Fan, 2003; McArdle & Bell, 2000).”
Planned missing designs to optimize the efficiency of latent growth parameter estimates (2014, background): “Recall that in the three-form missing design, one-third of data were missing. n ¼ 100 is already a small sample size for a latent curveof-factors model (Fan, 2003; Hamilton, Gagne, & Hancock, 2003; Leite, 2007), and it surprised us that even 60% of replications converged with 33% missing data.”
A Comparison of Latent Growth Models for Constructs Measured by Multiple Items (2007, background): “A sample size between 100 and 200 is the minimum recommended by Hamilton, Gagne, and Hancock (2003) and Fan (2003) for parameter estimation with univariate LGM.”
Association of Pre-Service Teachers' Performance, Personality, and Beliefs with Teacher Self-Efficacy at Program Completion. (2012, background): “…consistent findings in the research and measurement literature that measurement error can reduce the relationships between variables (e.g., Fan, 2003; Gulliksen, 1987), the ability to model measurement error in our analyses was an important consideration in selecting an SEM framework for…”
Structural equation modeling: strengths, limitations, and misconceptions. (2005, methodology): “…access to better methods for the treatment of missing data (e.g., Duncan et al. 1999, McArdle & Hamagami 1991, see also the discussion of missing data below); (i) greater statistical power according to the studies that have appeared to date (Duncan et al. 1999, Fan 2003, Muthén & Curran 1997).”