
ORDINAL REGRESSION
An illustrated tutorial and introduction to ordinal regression analysis using SPSS, SAS, or Stata. Suitable for introductory graduatelevel study.
The 2014 edition is a major update to the 2012 edition. Among the new features are these:
The full content is now available from Statistical Associates Publishers. Click here.
Below is the unformatted table of contents.
ORDINAL REGRESSION Overview 7 Data examples in this volume 8 Key Terms and Concepts 9 Location variables and thresholds 9 Prediction equations 9 Ordinal Regression in SPSS 9 Overview 9 SPSS inputs 10 The main "Ordinal Regression" dialog 10 The Ordinal Regression "Location" dialog 14 The Ordinal Regression "Options" dialog 17 The Ordinal Regression "Scale" dialog 19 The Ordinal Regression "Bootstrap" dialog 20 The Ordinal Regression "Output" dialog 22 SPSS outputs 23 Overview 23 The parallel lines test 24 Tests and effect size measures for model goodness of fit 25 Parameter estimates 28 Odds ratios 31 Other output 35 Ordinal Regression in SAS 39 Overview 39 SAS syntax for ordinal regression 39 SAS output for ordinal regression 41 The parallel lines test 41 Testing the global null hypothesis 42 Parameter estimates 42 Type 3 Analysis of Effects 43 Odds ratio estimates 44 Rsquare 44 Association of predicted probabilities and observed responses 45 Model fit statistics 45 Saving estimates 46 Ordinal regression in Stata 46 Overview 46 Stata input for ordinal regression 47 Stata output for ordinal regression 47 The parallel lines test 47 Overview 49 Likelihood ratio test of the model 50 PseudoR2 50 Parameter estimates 51 Odds ratios 51 Model fit statistics 52 Saving estimates 53 Other Stata statistical output 53 Partial proportional odds models 54 Overview 54 Partial proportional odds models in SAS 55 Partial proportional odds models in SAS 56 Example 56 Overview 56 Determining variables to constrain 56 The PPO model 60 Interpreting PPO results 61 Likelihood ratio tests 63 Partial proportional odds models in Stata 65 Example 65 Overview 65 Categorical predictor variables 66 Determining variables to constrain 66 The PPO model 69 Interpreting PPO results 69 Likelihood ratio tests 72 Postestimation 74 Assumptions 74 Parallel lines assumption 74 Adequate cell count 76 One ordinal dependent variable 78 Data level of predictor variables 79 Normal distribution of the dependent variable 79 Adequate sample size 79 No complete or quasicomplete separation 79 Absence of high multicollinearity 80 Frequently Asked Questions 80 Why not use ordinary leastsquares 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 SPSS 82 SAS 82 Stata 83 What are heteroskedastic ordinal regression models? 84 SPSS 84 SAS 84 Stata 84 When should I use a link function other than logit? 84 What are ordinal probit models? 86 SPSS 86 SAS 86 Stata 86 What are ordinal regression signalresponse 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 Acknowledgements 89 Bibliography 90 Pagecount: 93