By Terry E. Duncan
This ebook offers a entire advent to latent variable progress curve modeling (LGM) for examining repeated measures. It provides the statistical foundation for LGM and its quite a few methodological extensions, together with a few sensible examples of its use. it truly is designed to exploit the reader’s familiarity with research of variance and structural equation modeling (SEM) in introducing LGM innovations. pattern info, syntax, enter and output, are supplied for EQS, Amos, LISREL, and Mplus at the book’s CD. in the course of the booklet, the authors current various LGM suggestions which are precious for plenty of varied examine designs, and various figures offer useful diagrams of the examples.
Updated all through, the second one version beneficial properties 3 new chapters—growth modeling with ordered express variables, development combination modeling, and pooled interrupted time sequence LGM ways. Following a brand new association, the e-book now covers the advance of the LGM, through chapters on multiple-group concerns (analyzing development in a number of populations, speeded up designs, and multi-level longitudinal approaches), after which distinct themes similar to lacking info versions, LGM strength and Monte Carlo estimation, and latent development interplay versions. The version necessities formerly incorporated within the appendices at the moment are to be had 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 attracted to the size of switch through the years, together with social, developmental, organizational, academic, patron, 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 direction on multivariate information. A prerequisite of graduate point information is recommended.
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Additional info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition
32 CHAPTER 2 The mathematical model for the LGM depicted in Fig. 30 where Y(t, n) = observed score at Time t, Mi(n) = unobserved score for the intercept, Ms(n) = unobserved score for the shape, E(t, n) = unobserved error, and B(t) = basis coefficient for Time t. Observed scores are specified to be a weighted sum of two individual latent variables: Mi(n) is a variable representing individual differences in level of some attribute and is a constant for any individual across time; Ms(n) is a shape variable representing individual differences in the rate of change over time.
3. Representation of a three-factor polynomial LGM. lescents ranging in age from 14 to 17 years. Each participant’s level of alcohol consumption for the preceding 6 months was measured at three approximately equal time intervals over a 2-year period. 1. The input specifications necessary to estimate the model depicted in Fig. 1. These commands and input data test the hypothesized model using the EQS SEM program. 043 /PRINT EFFECTS=YES; /LMTEST /END viations of the observed variables in the input program).
Each measured dependent V variable is a linear combination of the three common factors and one independent E, or error, variable. The first factor, F1, is a linear combination of a constant, V999, representing the mean of the factor (and considered another observed variable in this notation), and an independent residual variable, D1. SPECIFICATION OF THE LGM 29 Similarly, the second factor, F2, is a linear combination of V999 and an independent residual variable, D2. Each freely estimated parameter is designated by an asterisk.