Download Analysis of Ordinal Categorical Data, Second Edition by Alan Agresti(auth.) PDF

By Alan Agresti(auth.)

Statistical science’s first coordinated guide of equipment for reading ordered express information, now absolutely revised and up-to-date, maintains to provide purposes and case reviews in fields as diversified as sociology, public overall healthiness, ecology, advertising and marketing, and pharmacy. Analysis of Ordinal express info, moment Edition presents an creation to uncomplicated descriptive and inferential tools for express information, giving thorough assurance of latest advancements and up to date equipment. exact emphasis is put on interpretation and alertness of equipment together with an built-in comparability of the on hand recommendations for studying ordinal information. Practitioners of information in govt, (particularly pharmaceutical), and academia will wish this new edition.Content:
Chapter 1 advent (pages 1–8):
Chapter 2 Ordinal possibilities, ratings, and Odds Ratios (pages 9–43):
Chapter three Logistic Regression versions utilizing Cumulative Logits (pages 44–87):
Chapter four different Ordinal Logistic Regression versions (pages 88–117):
Chapter five different Ordinal Multinomial reaction versions (pages 118–144):
Chapter 6 Modeling Ordinal organization constitution (pages 145–183):
Chapter 7 Non?Model?Based research of Ordinal organization (pages 184–224):
Chapter eight Matched?Pairs information with Ordered different types (pages 225–261):
Chapter nine Clustered Ordinal Responses: Marginal types (pages 262–280):
Chapter 10 Clustered Ordinal Responses: Random results types (pages 281–314):
Chapter eleven Bayesian Inference for Ordinal reaction facts (pages 315–344):

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Additional info for Analysis of Ordinal Categorical Data, Second Edition

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0. Now, suppose that Z is not actually measured continuously but, rather, with K categories. Consider the value of the global odds ratio for the probabilities in the partial tables, when X and Y are dichotomized at the means of the underlying variables. 12 reports the global odds ratio values for those partial tables. 0 as the number of categories K of the control variable increases. However, with small K there can be substantial bias in approximating the underlying conditional association. We would probably fail to detect the absence of underlying conditional association if we used relatively few control categories or if one control category contained a majority of the observations.

Ordinal models incorporate the c —\ logits into a single model. In the next section we show that this approach results in more parsimonious and simpler-to-interpret models than the fitting of c — 1 separate models, one for each logit. In this chapter we present models for cumulative logits and in Chapter 4 present models for the other ordinal logits. We see that each model has its own ordinal odds ratio for summarizing effects. For example, since the adjacent-categories logits use pairs of adjacent categories, they are naturally summarized using local odds ratios.

Their values describe the relative magnitudes of associations in localized regions of the table. They are called local odds ratios. A second natural family of odds ratios for ordinal variables is 9°. 5) ab) These measures are the regular odds ratios computed for the 2x2 tables obtained from the {r — l)(c — 1) ways of collapsing the row and column classifications into dichotomies. They describe associations that are global in both variables, in the sense that each odds ratio uses all categories of each variable instead of a localized region.

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