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This chapter explains how to use CFA with longitudinal data. Different ways of rearranging the information with the longitudinal data are introduced. First, the analysis of first differences is demonstrated by simply looking at increases or decreases between two time points. Secondly, CFA and visual shape patterning are explained. Here the shape of the curve are used as categories or patterns. Furthermore, a test of marginal homogeneity is provided which tests the null hypothesis of the homogeneity of marginals in a square contingency table. Moreover, a special type, the discrimination type is described. This type differentiates significantly between two independent samples.
KeywordsVisual Shape Exact Binomial Test Marginal Homogeneity Discrimination Type Bonferroni Adjusted Alpha
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