By Terry E. Duncan
This quantity offers Latent Variable development Curve Modeling for studying repeated measures. it truly is most probably that almost all readers have already mastered a lot of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) types are designated circumstances of LGMs that spotlight purely at the issue ability. by contrast, an absolutely elevated latent development curve research takes into consideration either issue capability and variances. LGMs also are editions of the normal linear structural version. as well as utilizing regression coefficients and variances and covariances of the self sustaining variables, they comprise an average constitution into the version. The ebook positive aspects significant themes--concepts and concerns, and applications--and is designed to use the reader's familiarity with ANOVA and conventional strategies in introducing LGM innovations and featuring functional examples.
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Extra resources for An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series)
Because of the overlap in ages in the cohort-sequential design, a quasi-longitudinal study spanning ages 12 to 16 was created. Moreover, because each age cohort, representing a different pattern of "missingness" due to design, contributed a different section to the overall developmental curve, the complete curve could be constructed using information from all cohorts simultaneously. In creating the full curve, it was assumed that one line could characterize the set of staggered age group curves, and that this line would accurately approximate the developmental trajectory developed from data generated from the 11-year-old cohort within the true longitudinal design.
Because indirect effects are sample statistics, they, like direct effects, have sampling variability. A test of significance for the unstandardized total effect, based on the work of Sobel (1982, 1986, 1987), is implemented in EQS. 989. There is no direct effect of age on subsequent problem behavior, thus the model results in a test with 1 degree of freedom. Because the model no longer provides a perfect fit to the data, a substantive model fit for the hypothesized model is achieved. 11 shows the measurement equations corresponding to the full model.
Page_61 Page 63 Chapter 5 Multivariate Representations of Growth and Development The previous chapters have described how LGMs can be used to model growth as a factor of repeated observations of one variable. Although development in a single behavior is often of interest, in longitudinal studies it can also be important to examine a number of behaviors simultaneously to determine the extent to which their development is interrelated. To this end, a multivariate longitudinal model may be considered.