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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| Tipo de documento: | Electrónico Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| En: |
Crime & delinquency
Año: 2017, Volumen: 63, Número: 1, Páginas: 61-76 |
| Acceso en línea: |
Volltext (Resolving-System) |
| Journals Online & Print: | Gargar...
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| Verificar disponibilidad: | HBZ Gateway |
| Palabras clave: |
| Sumario: | 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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| ISSN: | 1552-387X |
| DOI: | 10.1177/0011128716678848 |