By Terry E. Duncan
This ebook offers a finished advent to latent variable development curve modeling (LGM) for studying repeated measures. It offers the statistical foundation for LGM and its a variety of methodological extensions, together with a couple of functional examples of its use. it really is designed to use the reader’s familiarity with research of variance and structural equation modeling (SEM) in introducing LGM recommendations. pattern info, syntax, enter and output, are supplied for EQS, Amos, LISREL, and Mplus at the book’s CD. during the publication, the authors current various LGM thoughts which are priceless for lots of diversified examine designs, and diverse figures supply worthwhile diagrams of the examples.
Updated all through, the second one variation good points 3 new chapters—growth modeling with ordered express variables, progress blend modeling, and pooled interrupted time sequence LGM techniques. Following a brand new association, the e-book now covers the advance of the LGM, by means of chapters on multiple-group concerns (analyzing development in a number of populations, sped up designs, and multi-level longitudinal approaches), after which precise issues similar to lacking facts types, LGM strength and Monte Carlo estimation, and latent progress interplay versions. The version standards formerly integrated within the appendices at the moment are on hand 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 switch over the years, together with social, developmental, organizational, academic, patron, character and scientific 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 facts is recommended.
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Additional resources for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition
When there are enough points in time to freely estimate factor loadings beyond the two required for identification of the model, the slope factor is better interpreted as a general shape factor. , straight line) growth turns out to be a good model for the data, then the shape factor is more appropriately called a slope factor. 4 represents a hypothetical two-factor growth model with an unspecified growth function factor for three evenly spaced time points. For the unspecified two-factor model depicted in Fig.
27 Relating these equations back to Fig. 2a, for any given individual, b1 corresponds to the intercept factor score (F1), b2 corresponds to the slope factor score (F2), and ei corresponds to the time-specific errors of prediction. 29 where L1 = 0 at Time 1 simply starts the curve at this point by rescaling the intercept factor to represent initial status, and L2 = 1 at Time 2 indicates that from Time 1 to Time 2 there is 1 unit of change. Thus, Lt describes a linear relation of change in terms of linear differences from initial status at Time 1.
For example, consider what would happen to the latent growth curve model if all the straight lines in the sample were parallel and flat, but passed through different intercepts. In this case, one might say that strict stability exists, and the slope factor mean and variance would not SPECIFICATION OF THE LGM 35 FIG. 5. Representation of a collection of simple growth curve trajectories. differ significantly from zero. Or consider the case in which all the lines emanate from zero at the intercept with different slopes.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition by Terry E. Duncan