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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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期刊全称Analysis of Categorical Data from Historical Perspectives
期刊简称Essays in Honour of
影响因子2023Eric J. Beh,Rosaria Lombardo,Jose G. Clavel
视频video
发行地址Overviews the history of analysis of categorical data through quantification and classification.Summarizes many topics relevant to the analysis of categorical data.Reviews the work of those who have a
学科分类Behaviormetrics: Quantitative Approaches to Human Behavior
图书封面Titlebook: Analysis of Categorical Data from Historical Perspectives; Essays in Honour of  Eric J. Beh,Rosaria Lombardo,Jose G. Clavel Book 2023 The E
影响因子.This collection of essays is in honor of Shizuhiko Nishisato on his 88th birthday and consists of invited contributions only. The book contains essays on the analysis of categorical data, which includes quantification theory, cluster analysis, and other areas of multidimensional data analysis, covering more than half a century of research by the 41 interdisciplinary and international researchers who are contributors. Thus, it offers the wisdom and experience of work past and present and attracts a new generation of researchers to this field. Central to this wisdom and experience is that of Prof. Nishisato, who has spent much of the past 60 years mentoring and providing leadership in the research of quantification theory, especially that of “dual scaling”. The book includes contributions by leading researchers who have worked alongside Prof. Nishisato, published with him, been mentored by him, or whose work has been influenced by the research he has undertaken over his illustrious career. This book inspires researchers young and old as it highlights the significant contributions, past and present, that Prof. Nishisato has made in his field..
Pindex Book 2023
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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
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