By Terry E. Duncan
This quantity provides Latent Variable progress Curve Modeling for reading repeated measures. it really is most likely that almost all readers have already mastered a lot of LGM's underpinnings, in up to repeated measures research of variance (ANOVA) types are precise instances of LGMs that attention basically at the issue potential. against this, a completely increased latent progress curve research takes into consideration either issue capacity 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 sufficient variables, they comprise a median constitution into the version. The e-book gains significant themes--concepts and concerns, and applications--and is designed to exploit the reader's familiarity with ANOVA and traditional tactics 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)
8 shows the construct equations, standard errors, and test statistics for the three latent growth factors. 961, respectively, for the MANOVA). 3 Growth Curve Models Involving Sequelae of Change This section presents analyses of growth as a predictor of subsequent static outcomes. In this example the static criterion is adolescent problem behavior. In testing sequelae of change, the analysis subcommand of SPSS MANOVA also allows the user to switch the roles of the dependent variables and a covariate.
Problem Behavior COVARIATE CNST LIN QUAD B SE t Value Sig. 6 presents the EQS specifications for the sequelae of change model in which the latent growth factors of alcohol are seen to predict problem behavior (V5). Age (V4) is included in the data set, but is not specified in the equations and is trefore not part of the model. 10 presents the measurement equations for this model. The output shows the effects of each of the growth factors on V5, the problem behavior variable, along with standard errors and tests of significance.
As a means of understanding human development, the single longitudinal design has many advantages over comparable cross-sectional methods. Perhaps its greatest advantage is the ability to examine the same person at different times, permitting within-individual analyses of change. Cross-sectional designs, on the other hand, allow only for the study of variations between individuals. Although desirable, true longitudinal designs present some practical problems. Given time constraints, concerns about subject attrition, and the cost of multiple assessments, researchers have long sought alternative approaches that maintain the advantages and minimize the disadvantages of the longitudinal design.
An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology Series) by Terry E. Duncan