By Terry E. Duncan
This ebook offers a finished creation to latent variable progress curve modeling (LGM) for reading repeated measures. It provides the statistical foundation for LGM and its a number of methodological extensions, together with a few functional examples of its use. it's designed to exploit the reader’s familiarity with research of variance and structural equation modeling (SEM) in introducing LGM thoughts. pattern facts, syntax, enter and output, are supplied for EQS, Amos, LISREL, and Mplus at the book’s CD. in the course of the e-book, the authors current numerous LGM thoughts which are beneficial for lots of various learn designs, and diverse figures supply precious diagrams of the examples.
Updated all through, the second one version positive factors 3 new chapters—growth modeling with ordered specific variables, development mix modeling, and pooled interrupted time sequence LGM techniques. Following a brand new association, the booklet now covers the improvement of the LGM, by means of chapters on multiple-group matters (analyzing development in a number of populations, sped up designs, and multi-level longitudinal approaches), after which distinct themes equivalent to lacking facts versions, LGM energy and Monte Carlo estimation, and latent development interplay types. 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 drawn to the dimension of swap over the years, together with social, developmental, organizational, academic, customer, 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 path on multivariate statistics. 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
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. This significant variability corresponds to individual variation about the growth means. 6. Although the correlations give some idea as to the relative magnitude of these effects, the analysis does not indicate their level of significance. f. 000 The Latent Growth Curve Model.
316, indicated an adequate fit of the model to the data. Unlike typical applications of the chi-square, in which significant differences are hypothesized, a nonsignificant chi-square value in SEM model-fitting procedures indicates a model that does not differ significantly from the relationships observed in the data and, therefore, provides an adequate fit to the data. Although it is possible to add factors until a satisfactory fit to the data is obtained, LGM is most powerful with a small number of factors describing the data.
MATRIX DATA VARIABLES=ROWTYPE_ ALC1 ALC2 ALC3. 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.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition by Terry E. Duncan