An Introduction to Latent Variable Growth Curve Modeling: by Terry E. Duncan

By Terry E. Duncan

This quantity offers Latent Variable development Curve Modeling for reading repeated measures. it truly is most probably that almost all readers have already mastered lots of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) versions are precise circumstances of LGMs that spotlight purely at the issue skill. by contrast, a completely accelerated latent progress curve research takes under consideration either issue ability and variances. LGMs also are variations of the normal linear structural version. as well as utilizing regression coefficients and variances and covariances of the self sustaining variables, they include an average constitution into the version. The ebook positive factors significant themes--concepts and concerns, and applications--and is designed to exploit the reader's familiarity with ANOVA and traditional tactics in introducing LGM options and featuring functional examples.

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Extra resources for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series)

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An examination of the univariate LM statistics for the respecified model revealed that none of the remaining constraints, if released, would significantly improve overall model fit. 2 Alternative Multiple-Sample Analysis of ''Added Growth" LGM As in conventional multiple-population latent variable analyses, the preceding analyses specified a two-factor growth model in both groups, testing for equality of parameters across the two populations. An alternative approach (Muthén & Curran, 1997) is shown in Fig.

2. 3. 3. LM tests were used to determine whether relaxing constraints between the true longitudinal design cohort and any of the other cohorts would significantly enhance model fit (see chap. 4 for more information regarding LM tests). Examination of the univariate and multivariate LM statistics between the cohort-sequential and the true longitudinal models revealed that no cross-group constraints would significantly enhance the model fit if released, a further indication of similarity in the accelerated and true longitudinal designs.

The curve-of-factors LGM explicitly requires a condition of factor pattern invariance where common factor pattern elements must be equal over time. In Fig. 3, tobacco use (V5 to V8) is again used as the scaling reference, this time for the first-order common factors (F1, F2, F3, and F4), and the loadings for alcohol (La) and marijuana use (Lb) are constrained to be equal across time. Detailed rationales for the metric invariance assumptions are provided by Nesselroade (1983) and Meredith and Tisak (1982).

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