By Terry E. Duncan
This ebook presents a finished creation to latent variable progress curve modeling (LGM) for examining repeated measures. It provides the statistical foundation for LGM and its a number of methodological extensions, together with a couple of functional examples of its use. it's designed to use the reader’s familiarity with research of variance and structural equation modeling (SEM) in introducing LGM innovations. pattern facts, syntax, enter and output, are supplied for EQS, Amos, LISREL, and Mplus at the book’s CD. during the e-book, the authors current numerous LGM recommendations which are valuable for plenty of diverse examine designs, and various figures offer worthwhile diagrams of the examples.
Updated all through, the second one version positive aspects 3 new chapters—growth modeling with ordered express variables, progress combination modeling, and pooled interrupted time sequence LGM methods. Following a brand new association, the ebook now covers the improvement of the LGM, by way of chapters on multiple-group concerns (analyzing development in a number of populations, speeded up designs, and multi-level longitudinal approaches), after which targeted themes akin to lacking info versions, LGM strength and Monte Carlo estimation, and latent development interplay types. 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 versions to their very own research.
This sensible advisor is perfect for a variety of social and behavioral researchers drawn to the size of switch over the years, together with social, developmental, organizational, academic, buyer, 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 data. A prerequisite of graduate point statistics is recommended.
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Additional info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition
2004). Because the model is saturated with df = zero, the within-person random error, etj, must be constrained to zero. 1. Note that the data structure includes 1,074 observations (repeated measures) at level 1. These 1,074 observations are nested within 358 individuals at level 2. 4. Significant mean levels were evident for the constant and linear trend. 864 for the constant and linear trend, respectively). 5. As can be seen from the output, significant variability in the constant, linear, and quadratic scores existed.
Fixing L1 = zero and L2 = 1 has the effect of locating the intercept at the initial measurement, V1. 23 cov(F1, F2) = cov(V1, V2) – var(V1). 22 states that the variance of the slope factor is equal to the variance of the difference scores. 23, the intercept variance is the variance of V1. 24, is the covariance between initial status and the difference scores or change. INTERPRETATION OF THE GROWTH FACTORS Even with only two time points, the choice of loadings can affect the interpretation of both the intercept and slope factors.
1, V1 and V2 can be expressed as linear functions of the latent factor scores (symbolized by Fs), the factor loadings, and the latent factor means (symbolized by Ms). The reason for using D as a symbol of the factor variance is that, when a factor is a dependent variable, the D is interpreted as the disturbance variance, that is, the variance not accounted for by the predictors of F. When the factor is an independent variable, the disturbance variance is equal to the entire factor variance. The same reasoning applies for the choice of M as a symbol for the factor mean.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition by Terry E. Duncan