Titlebook: Analysis of Categorical Data from Historical Perspectives; Essays in Honour of Eric J. Beh,Rosaria Lombardo,Jose G. Clavel Book 2023 The E

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Contrasts for?Neyman’s Modified Chi-Square Statistic in?One-Way Contingency Tables022), however, argued against its use in multiple comparisons on the ground that in this statistic, hypothesised mean and variance-covariance structures of observed frequencies (proportions) are closely linked, so that rejecting the former necessarily implies rejecting the latter as well. To avoid t

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Confounding, a?Nuisance Addressedg. As aggregation, used to isolate an effect of a predictor variable on the dependant variable, leads to confounding if the contingency table is non-orthogonal, the proposed method relies on the loss of information when the correlation between two variables with . and . categories is eliminated by r

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Correcting for?Context Effects in?Ratings transformation is referred to as . and involves the creation of items corresponding to boundaries between the values of the rating scale. The resulting dual-scaling values for these boundaries can be used to quantify differences in respondents’ scale use. In this chapter, we show how a particular m

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Dual Scaling of?Rating Dataowever, the methods differ. To a large extent this is due to differences in pre-processing of the data. In particular, in dual scaling, ratings are either transformed to rank order, or to successive category data before applying a customised dual scaling approach. In correspondence analysis, on the