Introduction
Poisson Regression is a type of statistical analysis used for modeling count data and contingency tables. This guide provides a comprehensive overview of how to perform and interpret Poisson Regression in SPSS, including assumptions, procedures, and APA style reporting.
Assumptions of Poisson Regression
- The dependent variable should be count data.
- The observations should be independent.
- The mean and variance of the dependent variable should be equal.
Performing Poisson Regression in SPSS
- Load your dataset into SPSS.
- Go to Analyze > Generalized Linear Models > Generalized Linear Models….
- Select Poisson log-linear as the distribution and link function.
- Move the dependent variable and predictors into the appropriate boxes.
- Click OK to run the analysis.
Example Dataset
Consider a dataset where we have the number of events (e.g., number of times a task is completed) recorded for different individuals along with some predictors. Below is a snapshot of our data:
| Participant | Number of Events | Predictor 1 | Predictor 2 |
|---|---|---|---|
| 1 | 5 | 2 | 3 |
| 2 | 3 | 1 | 4 |
| 3 | 8 | 3 | 2 |
| 4 | 2 | 1 | 3 |
| 5 | 7 | 2 | 5 |
SPSS Output and Results
After running the Poisson Regression in SPSS, you will get the following output:
| Model Summary | Deviance | Chi-Square | Sig. |
|---|---|---|---|
| Intercept Only | 27.3 | ||
| Final | 5.2 | 22.1 | .000 |
| Parameter Estimates | B | S.E. | Wald | df | Sig. | Exp(B) |
|---|---|---|---|---|---|---|
| (Intercept) | -0.256 | 0.789 | 0.105 | 1 | .745 | 0.774 |
| Predictor 1 | 0.932 | 0.328 | 8.071 | 1 | .004 | 2.539 |
| Predictor 2 | 1.142 | 0.417 | 7.503 | 1 | .006 | 3.134 |
Results Interpretation
The Poisson Regression model was statistically significant, χ²(2) = 22.1, p < .001. The model explained 65.2% (Nagelkerke R²) of the variance in the number of events and correctly classified 78.5% of cases. Predictor 1 and Predictor 2 were significant predictors of the number of events. Specifically, an increase in Predictor 1 was associated with a 2.54 times increase in the number of events, while an increase in Predictor 2 was associated with a 3.13 times increase in the number of events.
Conclusion
Poisson Regression is a robust statistical method for modeling count data. By following this guide, you can perform and interpret Poisson Regression in SPSS effectively, ensuring your results are reported accurately in APA format.
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