Ordinal Regression (Statistical Associates "Blue Book" Series Book 9)
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Ordinal Regression (Statistical Associates "Blue Book" Series Book 9)
An illustrated tutorial and introduction to ordinal regression analysis using SPSS, SAS, or Stata. Suitable for introductory graduate-level study.
The 2014 edition is a major update to the 2012 edition. Among the new features are these:
* Over double the page length (93 pp. rather than 46) * Fifty percent more figures (52 illustrations) * Now covers SAS and Stata as well as SPSS * Now covers partial proportional odds models(recommended when the parallel lines test fails in ordinary ordinal regression, which it frequently does) * Totally rewritten and reformatted, with new FAQs added * Links to download datasets used in the text. -
Below is the unformatted table of contents.
ORDINAL REGRESSION Overview7 Data examples in this volume8 Key Terms and Concepts9 Location variables and thresholds9 Prediction equations9 Ordinal Regression in SPSS9 Overview9 SPSS inputs10 The main "Ordinal Regression" dialog10 The Ordinal Regression "Location" dialog14 The Ordinal Regression "Options" dialog17 The Ordinal Regression "Scale" dialog19 The Ordinal Regression "Bootstrap" dialog20 The Ordinal Regression "Output" dialog22 SPSS outputs23 Overview23 The parallel lines test24 Tests and effect size measures for model goodness of fit25 Parameter estimates28 Odds ratios31 Other output35 Ordinal Regression in SAS39 Overview39 SAS syntax for ordinal regression39 SAS output for ordinal regression41 The parallel lines test41 Testing the global null hypothesis42 Parameter estimates42 Type 3 Analysis of Effects43 Odds ratio estimates44 R-square44 Association of predicted probabilities and observed responses45 Model fit statistics45 Saving estimates46 Ordinal regression in Stata46 Overview46 Stata input for ordinal regression47 Stata output for ordinal regression47 The parallel lines test47 Overview49 Likelihood ratio test of the model50 Pseudo-R250 Parameter estimates51 Odds ratios51 Model fit statistics52 Saving estimates53 Other Stata statistical output53 Partial proportional odds models54 Overview54 Partial proportional odds models in SAS55 Partial proportional odds models in SAS56 Example56 Overview56 Determining variables to constrain56 The PPO model60 Interpreting PPO results61 Likelihood ratio tests63 Partial proportional odds models in Stata65 Example65 Overview65 Categorical predictor variables66 Determining variables to constrain66 The PPO model69 Interpreting PPO results69 Likelihood ratio tests72 Postestimation74 Assumptions74 Parallel lines assumption74 Adequate cell count76 One ordinal dependent variable78 Data level of predictor variables79 Normal distribution of the dependent variable79 Adequate sample size79 No complete or quasi-complete separation79 Absence of high multicollinearity80 Frequently Asked Questions80 Why not use ordinary least-squares regression instead of ordinal (logit) regression?80 Why not use ANOVA instead of ordinal (logit) regression?80 Why do parameter estimates differ between packages, and what is "parameterization"?81 Does the direction of coding of the ordinal dependent matter?81 How do I save predicted values as variables?82 SPSS82 SAS82 Stata83 What are heteroskedastic ordinal regression models?84 SPSS84 SAS84 Stata84 When should I use a link function other than logit?84 What are ordinal probit models?86 SPSS86 SAS86 Stata86 What are ordinal regression signal-response models (probit link)?87 In Stata's gologit2 partial proportional odds procedure, how are standardized estimates obtained?87 What is the SPSS syntax for ordinal regression models?89 Acknowledgements89 Bibliography90
