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Effective Communication of Statistical Forecasting Results for Non-Technical Audiences

This presentation by Dr. Vernon Gayle and Dr. Paul Lambert explores methodologies for forecasting using statistical models. They emphasize the importance of communicating complex statistical results, particularly to non-technically informed audiences. Various models, such as logit models, are discussed, including their applications in real-world scenarios, like assessing education outcomes based on demographic variables. The talk addresses common pitfalls, such as presenting naive odds, and includes practical strategies for effectively interpreting and conveying statistical findings.

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Effective Communication of Statistical Forecasting Results for Non-Technical Audiences

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Presentation Transcript

  1. Sample enumeration: Forecasting from statistical models Dr Vernon Gayle and Dr Paul Lambert (Stirling University) Tuesday 29th April 2008

  2. Communicating Results (to non-technically informed audiences) • Davies (1992) Sample Enumeration • Payne (1998) Labour Party campaign data • Gayle et al. (2002) • War against the uninformed use of odds (e.g. on breakfast t.v.)

  3. Sample Enumeration Methods In a nutshell… “What if” – what if the gender effect was removed 1. Fit a model (e.g. logit) 2. Focus on a comparison (e.g. boys and girls) 3. Use the fitted model to estimate a fitted value for each individual in the comparison group 4. Sum these fitted values and construct a sample enumerated % for the group

  4. Naïve Odds • Naïvely presenting odds ratios is widespread (e.g. Connolly 2006) • In this model naïvely (after controlling for other factors) Girls have an odds of 1.0 Boys have an odds of .58 We should avoid this where possible!

  5. Logit Model • Example from YCS 11 (these pupils took GCSE in 2001) y=1 5+ GCSE passes (A* - C) X vars gender; family social class (NS-SEC); ethnicity; housing tenure; parental education; parental employment; school type; family type

  6. Naïve Odds • Example from YCS 11 (these pupils took GCSE in 2001) • In this model naïvely (after controlling for other factors) Girls have an odds of 1.0 Boys have an odds of .66 We should avoid this where possible!

  7. Sample Enumeration Results

  8. Pseudo Confidence Interval Bootstrapping to construct a pseudo confidence interval (1000 Replications)

  9. Reference • A technical explanation of the issue is given in Davies, R.B. (1992) ‘Sample Enumeration Methods for Model Interpretation’ in P.G.M. van der Heijden, W. Jansen, B. Francis and G.U.H. Seeber (eds) Statistical Modelling, Elsevier.

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