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Reference:
Garson, G. D. (2014). Ordinal Regression. Asheboro, NC: Statistical Associates Publishers.
 

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About the author
 
Overview
 
Table of Contents
 
ISBN: 978-1-62638-029-5
ASIN: B0081UJ1O2
 
@c 2014 by G. David Garson and Statistical Associates Publishers. worldwide rights reserved in all languages and on all media. Permission is not granted to copy, distribute, or post e-books or passwords.
 


ORDINAL REGRESSION

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.

    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
    R-square	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
    Pseudo-R2	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 quasi-complete separation	79
    Absence of high multicollinearity	80
    Frequently Asked Questions	80
    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
    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 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
    Acknowledgements	89
    Bibliography	90
    
    Pagecount: 93