By Terry E. Duncan
This ebook presents a accomplished creation to latent variable development 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 truly is designed to exploit 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 e-book, the authors current quite a few LGM strategies which are necessary for lots of various study 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 express variables, progress combination modeling, and pooled interrupted time sequence LGM ways. Following a brand new association, the booklet now covers the improvement of the LGM, by way of chapters on multiple-group matters (analyzing progress in a number of populations, sped up designs, and multi-level longitudinal approaches), after which designated subject matters corresponding to lacking facts types, LGM strength and Monte Carlo estimation, and latent development interplay versions. The version requirements formerly incorporated 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 a variety of social and behavioral researchers attracted to the dimension of swap 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 progress curve modeling or as a complement for a path on multivariate records. A prerequisite of graduate point information is recommended.
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Extra resources for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition
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.
BEGIN DATA. 000 END DATA. MANOVA ALC1 ALC2 ALC3 /TRANSFORM(ALC1 ALC2 ALC3)=POLYNOMIAL /RENAME=CNST LIN QUAD /PRINT=CELLINFO(ALL) ERROR TRANSFORM PARAM(ALL) SIGNIF(MULTIV UNIV) /MATRIX=IN(*) /DESIGN. LGM, REPEATED MEASURES ANOVA, AND MLM 43 for each record. 1. In this example, the variables ALC1, ALC2, and ALC3 represent the repeated measures alcohol-use variable at Times 1, 2, and 3, respectively. 1 presents the transpose of the transformation matrix. Column 1 is the constant effect, column 2 the linear effect, and column 3 the quadratic effect.
1 presents the transpose of the transformation matrix. Column 1 is the constant effect, column 2 the linear effect, and column 3 the quadratic effect. This matrix was used in the MLM and LGM replications to represent the regression coefficients relating the corresponding latent factors to the observed variables. 1. Estimates and tests of significance are shown for the transformed variables. 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.