By Terry E. Duncan
This publication presents a finished creation to latent variable development curve modeling (LGM) for reading repeated measures. It provides 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 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. in the course of the booklet, the authors current a number of LGM ideas which are worthy for plenty of various study designs, and diverse figures supply invaluable diagrams of the examples.
Updated all through, the second one version positive aspects 3 new chapters—growth modeling with ordered specific variables, development blend modeling, and pooled interrupted time sequence LGM ways. Following a brand new association, the booklet now covers the advance of the LGM, by means of chapters on multiple-group concerns (analyzing progress in a number of populations, sped up designs, and multi-level longitudinal approaches), after which targeted themes corresponding to lacking facts versions, LGM energy and Monte Carlo estimation, and latent development interplay versions. The version standards formerly integrated 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 consultant is perfect for a variety of social and behavioral researchers drawn to the dimension of switch over the years, together with social, developmental, organizational, academic, purchaser, character and medical 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 records. A prerequisite of graduate point records is recommended.
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Extra info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition
To illustrate, let B0(t) = [1, 1, 1] and B1(t) = [0, 1, 2] represent the function for the data at Time 1 through Time 3, respectively. In addition, let mean intercept = 2 and mean slope = 1. 32 Y(3) = Mi + 2Ms + E(3). 36 SPECIFICATION OF THE LGM 33 where B(t) = 2 indicates that from Time 1 to the third time point there are 2 units of change. Therefore, B(t) describes change in terms of linear differences from initial status at Time 1. Given that B(t) represents the same relation for all individuals, it is likely that individual differences will not only exist in level, Mi(n), but also in the estimated developmental trajectories, Ms(n) (McArdle & Hamagami, 1991).
The matrix data statement defines variable names and their order in the raw data file. The data can include various vector statistics, such as means and standard deviations, as well as matrices, such as correlations. ’ 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.
Continuous covariates accommodated in an analysis of covariance allow for tests of both continuous predictors of change and change as a predictor. This method does not, however, allow for the simultaneous inclusion of change as both an independent and dependent variable. Hierarchical Linear Modeling. With the inclusion of covariates at the second level, individual deviations from the grand mean can be predicted. 4 can be further extended to account for this variability in the level 2 growth functions.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition by Terry E. Duncan