The Quasi-Poisson Regression is a generalization of the Poisson regression and is used when modeling an overdispersed count variable.
This article describes how to create a Quasi-Poisson Regression output as shown below. The example below is a Poisson regression that models a survey respondent’s number of fast-food occasions based on characteristics like age, gender, and work status.
- A count Outcome variable with at least three outcomes to be predicted. Ideally, a Numeric variable. When using stacked data the Outcome variable should be a single question in a Multi type structure (eg. Numeric-Multi).
- Continuous, categorical, or binary Predictor variables will be considered as predictors of the outcome variable. When using stacked data the Predictor(s) need to be a single question in a Grid type structure (Binary - Grid).
- Go to Anything > Advanced Analysis > Regression > Quasi-Poisson Regression.
- In the object inspector go to the Inputs tab.
- In the Output menu select the binary variable to be predicted by the predictor variables.
- Select the predictor variable(s) from the Predictor(s) list.
- OPTIONAL: Select the desired Output type:
- Summary: The default; as shown in the example above.
- Detail: Typical R output, some additional information compared to Summary, but without the pretty formatting.
- ANOVA: Analysis of variance table containing the results of Chi-squared likelihood ratio tests for each predictor.
- Relative Importance Analysis: The results of a relative importance analysis.
- Effects Plot Plots the relationship between each of the Predictors and the Outcome.
- OPTIONAL: Select the desired Missing Data treatment. (See Missing Data Options).
- OPTIONAL: Select Variable names to display variable names in the output instead of labels.
- OPTIONAL: Select Correction. Choose between None (the default), False Discovery Rate, Bonferroni.
- OPTIONAL: Specify the Automated outlier removal percentage to remove possible outliers.
- OPTIONAL: Select Stack data to stack the input data prior to analysis. Stacking can be desirable when each individual in the data set has multiple cases and an aggregate model is desired.