By Terry E. Duncan
This quantity provides Latent Variable development Curve Modeling for examining repeated measures. it's most probably that the majority readers have already mastered lots of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) versions are certain instances of LGMs that spotlight in simple terms at the issue ability. by contrast, a completely increased latent development curve research takes into consideration either issue capability and variances. LGMs also are variations of the normal linear structural version. as well as utilizing regression coefficients and variances and covariances of the autonomous variables, they contain a median constitution into the version. The e-book positive aspects significant themes--concepts and matters, and applications--and is designed to use the reader's familiarity with ANOVA and conventional techniques in introducing LGM recommendations and offering sensible examples.
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Extra resources for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series)
Representation of the factor-of-curves LGM. page_69 Page 70 time for alcohol (La) and for marijuana use (Lb). A more formal discussion of the mathematical representation of the factor-of-curves model is given by McArdle (1988). 2. 07, indicated that a higher order common factor representation of the three substances was tenable. 957, for substance use. 709. 415. The higher order factors accounted for approximately 67%, 43%, and 85% of the variation in the firstorder intercepts for alcohol, tobacco, and marijuana use, respectively.
3 Curve-of-Factors LGM Although the factor-of-curves LGM appeared to provide an adequate fit of the model to the data, the alternative curve-of-factors LGM was also tested. Recall that the curve-of-factors model fits a growth curve to factor scores representing what the three substance use behaviors have in common at each time point. The observed variables at each time point are factor analyzed to compute substance use factor scores for use in modeling growth curves. McArdle (1988) suggested that such nonnested model comparisons form a basic requirement for any serious study of multivariate dynamics.
An obvious advantage of the accelerated design over the single-cohort longitudinal design is the shorter follow-up period. This reduces the problems of cumulative testing effects and attrition, and produces quicker results. Another advantage is that tracking several cohorts, rather than one, allows the researcher to determine whether those trends observed in the repeated observations are corroborated within short time periods for each age cohort. The main disadvantage of the accelerated design, compared to the single-cohort longitudinal design, is that within-individual developmental sequences are followed, and behavioral continuity and prediction are studied, over shorter periods.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series) by Terry E. Duncan