By Terry E. Duncan
This booklet offers a finished creation to latent variable development curve modeling (LGM) for examining repeated measures. It provides the statistical foundation for LGM and its numerous methodological extensions, together with a couple of useful examples of its use. it's designed to exploit the reader’s familiarity with research of variance and structural equation modeling (SEM) in introducing LGM options. pattern facts, syntax, enter and output, are supplied for EQS, Amos, LISREL, and Mplus at the book’s CD. in the course of the e-book, the authors current a number of LGM options which are invaluable for plenty of diversified examine designs, and various figures offer priceless diagrams of the examples.
Updated all through, the second one variation positive factors 3 new chapters—growth modeling with ordered specific variables, progress mix modeling, and pooled interrupted time sequence LGM ways. Following a brand new association, the booklet now covers the advance of the LGM, via chapters on multiple-group matters (analyzing development in a number of populations, sped up designs, and multi-level longitudinal approaches), after which targeted themes similar to lacking facts types, LGM strength and Monte Carlo estimation, and latent development interplay types. The version requirements formerly integrated within the appendices at the moment are on hand at the CD so the reader can extra simply adapt the types to their very own research.
This useful advisor is perfect for quite a lot of social and behavioral researchers drawn to the dimension of swap through the years, together with social, developmental, organizational, academic, customer, character and medical psychologists, sociologists, and quantitative methodologists, in addition to for a textual content on latent variable progress curve modeling or as a complement for a path on multivariate data. A prerequisite of graduate point data is recommended.
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Additional info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition
All paths of the E and D variables on the V and F variables, respectively, are fixed at 1. In the case of the E and D variables, it is conventional to fix the path and estimate the variance. Hence the variances of the E and D variables appear as estimated parameters in the variance section. Here, the variances of the Es are not estimated, but fixed at zero because the model is saturated, lacking degrees of freedom. The factor loadings are fixed at values that represent polynomial contrasts and are used to identify the scale of the F variables.
McArdle and Epstein (1987), is probably more familiar to developmentalists than the two-factor approach outlined earlier. McArdle has termed the univariate single-factor LGM a curve model. McArdle’s model is actually a special case of the two-factor model. Meredith and J. Tisak (1990) indicate that the curve model is nested within the two-factor model and have termed this a monotonic stability model, implying that, although significant changes in mean levels may be occurring, the rank order of the observations stays the same over the repeated measures.
Although the selection of loadings is somewhat arbitrary, it is important to note that the intercept factor is inextricably bound to the time scale. By shifting the factor loadings on the slope factor, the scale of time is altered and this in turn affects the meaning and interpretation of the intercept factor mean and variance. Similarly, the correlation and covariance between intercept and slope factors will vary depending on the choice of factor loadings. The models depicted in Fig. 2 each describe growth over two occasions but vary the centering of time.