Utilizing alternate models for analyzing count outcomes
Although ordinary least squares (OLS) regression was once a common tool for modeling discrete count outcomes in criminology and criminal justice, the past several decades have seen an increasing reliance on regression techniques specifically designed for such purposes. Utilizing a practical example...
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| Medienart: | Elektronisch Aufsatz |
| Sprache: | Englisch |
| Veröffentlicht: |
2017
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| In: |
Crime & delinquency
Jahr: 2017, Band: 63, Heft: 1, Seiten: 61-76 |
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Volltext (Resolving-System) |
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MARC
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| 245 | 1 | 0 | |a Utilizing alternate models for analyzing count outcomes |c Jason Rydberg and Danielle Marie Carkin |
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| 520 | |a Although ordinary least squares (OLS) regression was once a common tool for modeling discrete count outcomes in criminology and criminal justice, the past several decades have seen an increasing reliance on regression techniques specifically designed for such purposes. Utilizing a practical example from the 1958 Philadelphia Birth Cohort, this article describes and compares various estimation strategies for modeling such outcome variables, including a discussion of the inappropriateness of OLS for such purposes and specific features of discrete count distributions that complicate statistical inference—overdispersion, non-independence, and excess zeros. Practical advice for selecting an appropriate modeling strategy is offered. | ||
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