By Terry E. Duncan
This quantity provides Latent Variable progress Curve Modeling for studying repeated measures. it's most probably that the majority readers have already mastered lots of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) versions are certain circumstances of LGMs that attention simply at the issue capability. by contrast, a completely increased latent progress curve research takes under consideration either issue potential and variances. LGMs also are editions of the traditional linear structural version. as well as utilizing regression coefficients and variances and covariances of the self sufficient variables, they contain an average constitution into the version. The e-book positive aspects significant themes--concepts and concerns, and applications--and is designed to exploit the reader's familiarity with ANOVA and traditional techniques in introducing LGM innovations and proposing functional examples.
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Extra info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series)
The full model is depicted in Fig. 2. In this model, age (V4) is specified as a covariate and problem behavior (V5) is predicted by the developmental trends in alcohol use. 7 presents the EQS specifications for the full model. The parameters of the model depicted in Fig. 2 are the coefficients in the equations and the variances and covariances of the independent variables, and the means of the independent variables and the intercepts of the dependent variables. Although these parameters are directly specified, they may not be the only aspects of interest F1 V5; V4 F2 V5; and V4 F3 V5.
4 presents the EQS specifications for the predictors of change model in which age (V4) is a predictor of change (F1, F2, and F3) in alcohol use. 000, reflecting the saturated nature of the hypothesized model. 8 shows the construct equations, standard errors, and test statistics for the three latent growth factors. 961, respectively, for the MANOVA). 3 Growth Curve Models Involving Sequelae of Change This section presents analyses of growth as a predictor of subsequent static outcomes. In this example the static criterion is adolescent problem behavior.
2, the added growth factor is specified to capture linear differences between the two groups. In this case, the linear slope factor captures normative growth that is common to both groups. The added growth factor approach provides a test of the difference in growth rate between the two groups without having to resort to the use of LM or other model modification tests. 2 presents the EQS program specifications necessary to estimate the model presented in Fig. 2. 4. Model fitting procedures for the model presented in Fig.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series) by Terry E. Duncan