By Terry E. Duncan
This quantity offers Latent Variable development Curve Modeling for studying repeated measures. it's most likely that the majority readers have already mastered a lot of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) types are specific situations of LGMs that attention merely at the issue capacity. against this, a completely elevated latent progress curve research takes into consideration either issue capacity and variances. LGMs also are versions of the normal linear structural version. as well as utilizing regression coefficients and variances and covariances of the autonomous variables, they comprise an average constitution into the version. The e-book positive aspects significant themes--concepts and matters, and applications--and is designed to exploit the reader's familiarity with ANOVA and conventional tactics in introducing LGM concepts and featuring functional examples.
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Extra info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series)
881 page_79 Page 80 assessed over 5 years in the true longitudinal model) to determine whether significant differences were apparent between the true longitudinal estimates and those from the cohort-sequential design. For this model, cross-group equality constraints across the cohorts were placed on latent means, latent variances, latent covariances, and residual errors. 876. 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.
Groups 1, 2, and 3 in the model depicted in Fig. 1 represent a cohort-sequential LGM in which the basic parameters describe a systematic pattern of individual differences in change over time. The same developmental page_76 Page 77 Fig. 1. Representation of the combined cohort-sequential-true longitudinal LGM. model is assumed in each cohort, allowing for tests of hypotheses concerning convergence across separate groups and the feasibility of specifying a common growth trajectory over the 6 years represented by the design.
A variety of growth models can be generalized to the simultaneous analysis of data from multiple populations. To some extent, population differences can be captured in single-population analyses by representing the different groups as dummy vectors used as time-invariant covariates. However, to achieve more generality in modeling as well as specificity in page_53 Page 54 the examination of population differences, it is necessary to use the multiple-population approach. A good first step is to perform separate growth analyses for each group.