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...
| Main Author: | |
|---|---|
| Contributors: | |
| Format: | Electronic Article |
| Language: | English |
| Published: |
2017
|
| In: |
Crime & delinquency
Year: 2017, Volume: 63, Issue: 1, Pages: 61-76 |
| Online Access: |
Volltext (Resolving-System) |
| Journals Online & Print: | Drawer...
|
| Check availability: | HBZ Gateway |
| Keywords: |
MARC
| LEADER | 00000caa a2200000 4500 | ||
|---|---|---|---|
| 001 | 1577205472 | ||
| 003 | DE-627 | ||
| 005 | 20180824114702.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180703s2017 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1177/0011128716678848 |2 doi | |
| 035 | |a (DE-627)1577205472 | ||
| 035 | |a (DE-576)507205472 | ||
| 035 | |a (DE-599)BSZ507205472 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 100 | 1 | |a Rydberg, Jason |0 (DE-588)1211428044 |0 (DE-627)1699326010 |0 (DE-576)341170518 |4 aut | |
| 109 | |a Rydberg, Jason | ||
| 245 | 1 | 0 | |a Utilizing alternate models for analyzing count outcomes |c Jason Rydberg and Danielle Marie Carkin |
| 264 | 1 | |c 2017 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 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. | ||
| 700 | 1 | |a Carkin, Danielle Marie |0 (DE-588)116680982X |0 (DE-627)1030677387 |0 (DE-576)510367267 |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Crime & delinquency |d Thousand Oaks, Calif. [u.a.] : Sage Publications, 1960 |g 63(2017), 1, Seite 61-76 |h Online-Ressource |w (DE-627)306655128 |w (DE-600)1499997-3 |w (DE-576)081985045 |x 1552-387X |7 nnns |
| 773 | 1 | 8 | |g volume:63 |g year:2017 |g number:1 |g pages:61-76 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1177/0011128716678848 |x Resolving-System |3 Volltext |
| 936 | u | w | |d 63 |j 2017 |e 1 |h 61-76 |
| 951 | |a AR | ||
| ELC | |a 1 | ||
| LOK | |0 000 xxxxxcx a22 zn 4500 | ||
| LOK | |0 001 3015805843 | ||
| LOK | |0 003 DE-627 | ||
| LOK | |0 004 1577205472 | ||
| LOK | |0 005 20180703103332 | ||
| LOK | |0 008 180703||||||||||||||||ger||||||| | ||
| LOK | |0 040 |a DE-21-110 |c DE-627 |d DE-21-110 | ||
| LOK | |0 689 |a s |a Quantitative | ||
| LOK | |0 689 |a s |a Count regression models | ||
| LOK | |0 689 |a s |a Zero-inflated models | ||
| LOK | |0 689 |a s |a Hurdle models | ||
| LOK | |0 689 |a s | ||
| LOK | |0 852 |a DE-21-110 | ||
| LOK | |0 852 1 |9 00 | ||
| LOK | |0 935 |a krub | ||
| ORI | |a SA-MARC-krimdoka001.raw | ||