By Terry E. Duncan
This booklet offers a complete creation to latent variable progress curve modeling (LGM) for reading repeated measures. It offers the statistical foundation for LGM and its a variety of methodological extensions, together with a few 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 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 ebook, the authors current various LGM concepts which are priceless for lots of various learn designs, and various figures supply useful diagrams of the examples.
Updated all through, the second one version good points 3 new chapters—growth modeling with ordered express variables, development blend modeling, and pooled interrupted time sequence LGM methods. Following a brand new association, the publication now covers the advance of the LGM, by means of chapters on multiple-group concerns (analyzing progress in a number of populations, speeded up designs, and multi-level longitudinal approaches), after which distinctive issues reminiscent of lacking facts types, LGM energy and Monte Carlo estimation, and latent progress interplay versions. The version requisites formerly integrated within the appendices are actually to be had at the CD so the reader can extra simply adapt the types to their very own research.
This sensible advisor is perfect for quite a lot of social and behavioral researchers attracted to the size of switch over the years, together with social, developmental, organizational, academic, purchaser, 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 statistics. 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
SUMMARY It is crucial to recognize the limitations of trying to build an adequate model of social behavior with just two points in time. Many important assumptions must be made that can fundamentally affect the conclusions drawn about the nature of change over time on the constructs of interest. Rogosa (1988) clearly demonstrated the hazards involved in using linear panel models when nonlinear growth processes are operative. This assumption cannot be tested with only two assessments. Linear panel models are not optimal for studying linear growth processes, either.
271). Note that the constant (CNST) and linear trend (LIN) are both significant. 854) suggests an increasing rate of change in alcohol use over time. 414 for the quadratic trend). These values are noted here because they are parameters of interest in the subsequent MLM and LGM procedures. 3 presents the within cells correlations that represent the associations among the transformed variables. These can be thought of as associations among the individual differences parameters. 331 Note. Standard deviations are presented on the diagonal.
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. One way of demonstrating that the curve model is nested within the two-factor model is to derive the curve model from the more general model through a series of constraints on the more general model’s parameters.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition by Terry E. Duncan