By Terry E. Duncan
This quantity offers Latent Variable development Curve Modeling for examining repeated measures. it really is most likely that the majority readers have already mastered a lot of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) versions are specified situations of LGMs that attention merely at the issue capacity. against this, an absolutely accelerated latent progress curve research takes under consideration either issue capability and variances. LGMs also are versions 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 booklet beneficial properties significant themes--concepts and concerns, and applications--and is designed to use the reader's familiarity with ANOVA and traditional tactics in introducing LGM strategies and proposing sensible examples.
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Additional resources for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series)
The standard errors and test statistics for V5, the problem behavior variable, are shown here. All three latent growth factors are significant predictors of V5. 146 *V4 F2. 12 shows the construct equations, standard errors, and test statistics for the effect of age on the three latent growth factors. The only significant effects for age F1, V4 F2, and were found for F1 and F3, the constant and quadratic trends, respectively. Parameter estimates for the effect of age on the growth factors (V4 F3) are similar to those reported earlier in the separate growth curve analyses.
Note that there is no single correct way of executing multiple-group analyses. Researchers should choose an approach depending on the aims and hypotheses of the study. A useful alternative to relying on LM or other model modification tests in a multiple-sample LGM involves the use of an added growth factor (Muthén & Curran, 1997). This approach allows the researcher to capture normative growth that is common to both groups as well as differences in growth between groups for all growth factors except the initial status factor.
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.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series) by Terry E. Duncan