Testing Main Effects and Interactions in Latent Curve Analysis.

Abstract

A key strength of latent curve analysis (LCA) is the ability to model individual variability in rates of change as a function of 1 or more explanatory variables. The measurement of time plays a critical role because the explanatory variables multiplicatively interact with time in the prediction of the repeated measures. However, this interaction is not typically capitalized on in LCA because the measure of time is rather subtly incorporated via the factor loading matrix. The authors' goal is to demonstrate both analytically and empirically that classic techniques for probing interactions in multiple regression can be generalized to LCA. A worked example is presented, and the use of these techniques is recommended whenever estimating conditional LCAs in practice.

On growth curves and mixture models

“…Researchers familiar with the multilevel model of change would think of individual growth curves that make up the observed pattern and observe how the variability increases with time or age (Hoeksma, 2005). As recently shown by Curran, Bauer, and Willoughby (2004), these differences may have profound effects on the validity of researcher's interpretations.…”

Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques

“…Similarly, the J-N technique may be difficult to apply in models involving three-way interactions, such as where x 1 interacts with the x 2 x 3 product term. For situations such as these that may arise in the multilevel growth model, Curran et al (2004) suggested it may be fruitful to blend the pick-a-point approach and J-N technique. Specifically, the conditional effect of x 1 would be plotted as a function of x 2 and examined through the J-N technique at various selected levels of x 3 .…”

From Pews to Participation: The Effect of Congregation Activity and Context on Bridging Civic Engagement

“…There are various ways to calculate the point at which an interaction term between a continuous variable and a dichotomous variable leads to significant differences between the two groups (for a more complete discussion of this issue, see Aiken and West 1991;Curran, Bauer, and Willoughby 2004). Since the main effects of the dichotomous variables are always showing the difference in the two groups when the other variables in the model have values of zero, an easy shorthand way to test for significant differences at particular points is to center the continuous variables at particular values.…”