By Alan Agresti(auth.)
Statistical science’s first coordinated guide of equipment for studying ordered express facts, now absolutely revised and up-to-date, maintains to give functions and case stories in fields as different as sociology, public well-being, ecology, advertising, and pharmacy. Analysis of Ordinal specific information, moment Edition offers an advent to easy descriptive and inferential tools for express info, giving thorough assurance of latest advancements and up to date equipment. unique emphasis is put on interpretation and alertness of tools together with an built-in comparability of the to be had options for interpreting ordinal info. Practitioners of facts in executive, (particularly pharmaceutical), and academia will wish this new edition.Content:
Chapter 1 creation (pages 1–8):
Chapter 2 Ordinal percentages, rankings, and Odds Ratios (pages 9–43):
Chapter three Logistic Regression versions utilizing Cumulative Logits (pages 44–87):
Chapter four different Ordinal Logistic Regression types (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 versions (pages 262–280):
Chapter 10 Clustered Ordinal Responses: Random results versions (pages 281–314):
Chapter eleven Bayesian Inference for Ordinal reaction info (pages 315–344):
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Extra info for Analysis of Ordinal Categorical Data, Second Edition
Wald confidence intervals for proportions or parameters based on proportions often perform poorly for small to moderate n. 95 unless n is quite large. This is especially true when ζ takes values near the boundary of the parameter space (such as in estimating a proportion that is near 0 or 1), in which case ζ may have a highly skewed sampling distribution. Then it may not be sensible for ζ to be the midpoint of the confidence interval, an extreme case being when ζ falls at the boundary. Alternative confidence intervals that provide results similar to those of Wald intervals for large n but usually perform better for small to moderate n result from inverting likelihood-ratio or score tests.
Based on simulations, we trust the score interval estimator of the odds ratio more than we do the other methods. With any of the intervals, we infer that the active treatment works better than the control treatment to reduce shoulder pain. 7. Shoulder Tip Pain Scores After Laparoscopic Surgery Pain Score" Treatments 1 2 3 4 5 Active Control 19 7 2 3 1 4 0 3 0 2 Source: Lumley (1996), Table 2. " 1 , low; 5, high. 5 6 For example, in SAS, using the LRCI option in PROC GENMOD. html. 5 ORDINAL PROBABILITIES, SCORES, AND ODDS RATIOS Confidence Intervals for Measures Using P(Y\ > Yz) We now consider the stochastic superiority measure a = P(Y\ >Yi) + \ P(Y\ = Y2) for comparing two groups on an ordinal response.
875). The imprecision reflects the relatively small sample sizes. html has R functions by E. Ryu for confidence intervals for a. 6 33 Small-Sample Interval Estimation for Local Odds Ratios A well-known approach to small-sample inference for some parameters with categorical data eliminates unknown nuisance parameters by conditioning on their sufficient statistics. Statistical inference then uses the conditional distribution, which does not depend on the nuisance parameters. This method can be applied to interval estimation for odds ratios.