By Terry E. Duncan
This publication offers a accomplished advent to latent variable progress curve modeling (LGM) for examining repeated measures. It offers the statistical foundation for LGM and its a variety of methodological extensions, together with a couple of sensible examples of its use. it truly is designed to use the reader’s familiarity with research of variance and structural equation modeling (SEM) in introducing LGM recommendations. pattern information, syntax, enter and output, are supplied for EQS, Amos, LISREL, and Mplus at the book’s CD. through the e-book, the authors current a number of LGM suggestions which are invaluable for plenty of diverse learn designs, and diverse figures offer worthwhile diagrams of the examples.
Updated all through, the second one variation beneficial properties 3 new chapters—growth modeling with ordered specific variables, development blend modeling, and pooled interrupted time sequence LGM ways. Following a brand new association, the ebook now covers the improvement of the LGM, by way of chapters on multiple-group matters (analyzing development in a number of populations, sped up designs, and multi-level longitudinal approaches), after which particular issues comparable to lacking facts versions, LGM energy and Monte Carlo estimation, and latent progress interplay versions. The version requirements formerly integrated within the appendices are actually to be had at the CD so the reader can extra simply adapt the versions to their very own research.
This useful consultant is perfect for a variety of social and behavioral researchers drawn to the dimension of swap over the years, together with social, developmental, organizational, academic, client, character and scientific psychologists, sociologists, and quantitative methodologists, in addition to for a textual content on latent variable development curve modeling or as a complement for a direction on multivariate facts. A prerequisite of graduate point records is recommended.
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Additional info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition
6. Although the correlations give some idea as to the relative magnitude of these effects, the analysis does not indicate their level of significance. f. 000 The Latent Growth Curve Model. The diagrammatic representation of the three-factor saturated latent growth curve model is presented in Fig. 1. Specific restrictions must be placed on this model to correspond with the assumptions from the general linear modeling procedure (MANOVA) previously presented. First, the orthogonal polynomial transformation matrix generated from the MANOVA procedure is used to represent the regression coefficients relating the latent factors to the observed variables, rather than the coefficients presented in chapter 2.
REPRESENTING THE SHAPE OF GROWTH OVER TIME Because of limited data for the latent growth curve model for two time points, the factor loadings have been of little interest thus far. However, with three points in time, the factor loadings carry information about the shape of growth over time. Three or more time points provide an opportunity to test for nonlinear trajectories. The most familiar approach to nonlinear trajectories is probably the use of polynomials. The inclusion of quadratic or cubic effects is easily accomplished by including another factor or two.
The model estimates 3 variances, 3 covariances, and 3 means for the constant, linear, and quadratic latent factors, resulting in zero degrees of freedom. In testing a more parsimonious model, such as a two-factor LGM (presented in subsequent sections), sufficient degrees of freedom are available for the evaluation of model fit. 1 shows the construct equations, standard errors, and test statistics for the three latent growth factors. The top row of each equation presents the latent mean estimates, the middle row presents the standard error, and the bottom row shows the test of significance (t value).
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition by Terry E. Duncan