You are in: Home › Statistics › Publications and Data › Occasional papers › Modelling Census Under-Enumeration › 4. Results ›

Occasional Paper

Modelling Census Under-Enumeration - A Logistic Regression Perspective

4. Results

The results of the logistic analyses are presented below. For each model I have presented the Maximum Likelihood parameter estimates and the odds ratios of these parameter estimates. Like other regression techniques, a p-value less than 0.05 represents a significant result. Additionally, since the relationship between the predictor and outcome variables is not a linear, I have presented each model as a logistic equation as follows. An example of this is evident in Formula 4.1 (19 Kb PDF file). 

Forgetting about the logistic transformation on the left-hand side, the right-hand side has an intercept term β₀ and slope terms β₁ , β₂ , … , β_k – the same as in linear regression.

For reasons of parsimony, early on in the model selection process it was decided not to consider interaction effects between the independent variables. Interaction effects are difficult to interpret, and can introduce additional complexity to the analysis. In some cases although a model with an interaction effect might improve the fit the data, in comparison with a model without, the benefit of the improved model fit is not significant when other factors are taken into account.

The objective of the logistic analyses in this paper was to find the simplest model that uncovered which independent factors were significant predictors of number of people imputed in a ward.

• Deprivation Models
• Hard to Count Index Model
• The Remaining Census Variables
• Further Investigation Into the Assupmtion of Linearity
• Final Modelling Stage
• Odds Ratios
• Assesment of How Well the Models Fit the Data
• Summary

Note:The file(s) listed above can be viewed in Adobe Portable Document Format (pdf)  Download the latest version of Adobe Acrobat Reader free.