A Comparison of Latent Growth Models for Constructs Measured by Multiple Items

65 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Factorial Invariance within Longitudinal Structural Equation Models: Measuring the Same Construct across Time (2010, background): “For further treatments of such models, see McArdle (1988),Chan (1998),Sayer & Cumsille (2001),Ferrer et al. (2008), and Leite (2007).1 If strong factorial invariance does not hold, the first-order construct is not invariant and the second-order growth model has no clear interpretation.”
Testing Measurement Invariance in Longitudinal Data with Ordered-Categorical Measures (2016, unknown): “Drawing on studies of growth models with continuous indicators (see Kim & Willson, 2014a, 2014b; Leite, 2007; Wirth, 2008), approaches can be developed for ordered-categorical data that examine effects of inappropriate measurement invariance con- straints on estimates of key growth parameters and…”
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.”