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- Mod 5 - Ord Reg
- 5.1 Introduction
- 5.2 Ordinal Outcomes
- 5.3 Assumptions
- 5.4 Example 1 - Ordinal Regression on SPSS
- 5.5 Teacher Expectations and Tiering
- 5.6 Example 2 - Ordinal Regression for Tiering
- 5.7 Example 3 - Interaction Effects
- 5.8 Example 4 - Including Prior Attainment
- 5.9 Proportional Odds Assumption
- 5.10 Reporting the Results
- 5.11 Conclusions
- 5.12 Other Categorical Models
- Quiz
- Exercise
- References
- Mod 5 - Ord Reg

5.10 Reporting the Results of Ordinal Regression
Perhaps the most noteworthy outcome from the analysis of the example completed above is the finding that Black Caribbean students are under-represented in entry to the higher mathematics test tiers relative to White British students in the ratio 0.66:1, i.e. the odds of being entered to the higher tiers for Black Caribbean students are about two-thirds the odds for White British students. As we saw in Module 4 (Page 4.8
This shows how an ordinal model was built hierarchically over a series of steps, looking first at prior attainment then progressively adding further explanatory variables. Note that the results from an analysis of the summary LSYPE dataset used here will not agree precisely with the analyses presented in the paper because slightly different variables are used, and the data in the paper have been weighted to account for selection and non-response issues and for clustering within schools, but the overall pattern of findings will be similar. The paper also discusses in depth the interpretation of the model findings, and what these mean in relation to policy and practice. Statistical models are only useful if the results and their implications can be communicated clearly to the intended audiences. |