By Terry E. Duncan
This quantity offers Latent Variable development Curve Modeling for examining repeated measures. it's most probably that the majority readers have already mastered a lot of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) versions are unique circumstances of LGMs that attention in basic terms at the issue capacity. against this, a completely increased latent development curve research takes into consideration either issue capacity and variances. LGMs also are editions of the normal linear structural version. as well as utilizing regression coefficients and variances and covariances of the self sufficient variables, they include an average constitution into the version. The publication beneficial properties significant themes--concepts and concerns, and applications--and is designed to use the reader's familiarity with ANOVA and conventional methods in introducing LGM thoughts and offering sensible examples.
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Extra info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series)
1. 246, indicated that there were no statistical differences between the two models in terms of overall model fit. An advantage of the added growth model to the more standard multiplesample LGM is that it affords a statistical test of the mean differences between the two groups in a single run without having to resort to the use of LMs or other post hoc procedures. For all factors except the initial status factor, one may specify an added growth factor. For example, one group may have both a linear and quadratic growth factor beyond the intercept or initial status factor and the remaining group may have added factors for both the linear and quadratic trajectories.
1, the LGM includes a test of a linear trend in the data (loadings set at values of 0, 1, and 2 on the slope factor). Model fitting procedures for the model presented in Fig. 000. 2. 416 Mean slope Intercept variance Slope variance Covariance Error variances The significant slope means indicated that linear growth in adolescent alcohol use occurred over the time period measured. In addition, the significant variances indicated that substantial variation existed in individual differences regarding initial status and trajectories of alcohol use.
With enough time points to freely estimate factor loadings beyond the two required for identification of the model, the slope factor is interpreted as a general shape factor. , straight line) growth models the data best, then the shape factor is called a slope factor. To test the adequacy of the hypothesized linear growth form and the possibility of nonlinear growth, alternative two-factor unspecified LGMs were performed.