Terry E. Duncan's An Introduction to Latent Variable Growth Curve Modeling: PDF

By Terry E. Duncan

ISBN-10: 0585176604

ISBN-13: 9780585176604

ISBN-10: 080583060X

ISBN-13: 9780805830606

This quantity offers Latent Variable progress Curve Modeling for examining repeated measures. it's most probably that almost all readers have already mastered lots of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) types are targeted circumstances of LGMs that spotlight simply at the issue skill. against this, a completely multiplied latent progress curve research takes under consideration either issue skill and variances. LGMs also are versions of the traditional linear structural version. as well as utilizing regression coefficients and variances and covariances of the self sustaining variables, they include a median constitution into the version. The ebook positive factors significant themes--concepts and matters, and applications--and is designed to use the reader's familiarity with ANOVA and traditional approaches in introducing LGM strategies and offering functional examples.

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Extra info for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series)

Sample text

See chapter 9 for a detailed discussion of more complex random effects models. LGM provides more flexibility in the measurement of change than the more traditional ANOVA approach, the most notable being LGM's ability to approximate random changes in measurement error. Another is the ability to use variables simultaneously as both independent and dependent variables in the model, allowing for complex representations of growth and correlates of change. Other advantages of the LGM approach, as demonstrated in subsequent chapters, include its capacity to (a) analyze interindividual differences in change simultaneously in cross-domain latent page_48 Page 49 variable associative growth models, (b) include time-varying as well as time-invariant covariates as demonstrated here, (c) develop from the data a common developmental trajectory in overlapping cohorts, and (d) model multilevel longitudinal data.

Cohort-Sequential LGM The following sections provide a test of the cohort-sequential LGM and a comparison of findings from the cohort-sequential LGM to a "true longitudinal" growth curve model using data assessed from a single age cohort. The cohort-sequential technique was used to model the development of adolescent alcohol use from 12 to 16 years of age over a 3-year period and a true longitudinal model was used over a 5-year period. E. C. Duncan, & Hops, 1994) as well as the development of other substances (T.

4 The Full Growth Curve Model Involving Predictors and Sequelae of Change A full model which includes both static predictors of the developmental parameters as well as sequelae of the developmental parameters can not be analyzed in the repeated measures ANOVA format because the transformed variables must be either independent or dependent variables, not both. In contrast, the LGM framework allows for latent and measured variables to be used as both predictors and criteria. The full model is depicted in Fig.

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An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series) by Terry E. Duncan


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