The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Understanding and interpreting generalized ordered logit models
Abstract
When outcome variables are ordinal rather than continuous, the ordered logit model, aka the proportional odds model (ologit/po), is a popular analytical method. However, generalized ordered logit/partial proportional odds models (gologit/ppo) are often a superior alternative. Gologit/ppo models can be less restrictive than proportional odds models and more parsimonious than methods that ignore the ordering of categories altogether. However, the use of gologit/ppo models has itself been problematic or at least sub-optimal. Researchers typically note that such models fit better but fail to explain why the ordered logit model was inadequate or the substantive insights gained by using the gologit alternative. This paper uses both hypothetical examples and data from the 2012 European Social Survey to address these shortcomings.
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Cites
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Bayesian model selection in social research
Raftery, A. E. (1995) · Sociological Methodology · doi:10.2307/271063 · Cited by 6,823
Regression Models for Categorical Dependent Variables Using Stata
Guzmán, J. (2013) · Puerto Rico health sciences journal · Cited by 5,304
Generalized Linear Models (2nd ed.).
Therneau, T. M., McCullagh, P., & Nelder, J. A. (1993) · Journal of the American Statistical Association · doi:10.2307/2290358 · Cited by 4,907
Regression Models for Categorical Dependent Variables Using Stata
Long, J. S., & Jeremy Freese, A. (2014) · Cited by 4,624
Generalized Ordered Logit/Partial Proportional Odds Models for Ordinal Dependent Variables
Williams, R. (2006) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0600600104 · Cited by 2,118
Self-citationAssessing proportionality in the proportional odds model for ordinal logistic regression.
Brant, R. (1990) · Biometrics · doi:10.2307/2532457 · Cited by 1,864
Regression Models for Categorical Dependent Variables Using Stata, Second Edition
Long, J. S., & Jeremy Freese, A. (2005) · Cited by 1,465
The Social Connectedness of Older Adults: A National Profile
Cornwell, B., Laumann, E. O., & Schumm, L. P. (2008) · American Sociological Review · doi:10.1177/000312240807300201 · Cited by 941
Generalized Linear Models and Extensions
Hardin, J. W., & Hilbe, J. M. (2012) · Cited by 849
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Climate Change Risk Perception in Taiwan: Correlation with Individual and Societal Factors
Sun, Y., & Han, Z. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15010091 · Cited by 91
An Analysis of Large-Scale Forced Migration in Africa
Bayar, M., & Aral, M. M. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16214210 · Cited by 37
Kock, M., Kuppens, P., Van der Gucht, K., & Raes, F. (2021) · Psychologica Belgica · doi:10.5334/pb.1093 · Cited by 13
Risk Screening of Veterans Throughout the Life Course
VanTil, L. D., MacLean, M. B., Coulthard, J., Murray, R., Lourenso, S., Camarda, J., & Lea, T. (2022) · Military Behavioral Health · doi:10.1080/21635781.2021.2007186 · Cited by 6
Metadata
- Authors
- Williams, R.
- Venue
- The Journal of Mathematical Sociology
- Publisher
- Informa UK Limited
- Published
- 2016-01-02
- Volume
- 40
- Issue
- 1
- Pages
- 7-20
- ISSN
- 0022-250X (ISSN-L)
- 1545-5874
- OpenAlex
- W2295905303
- Cited by
- 936
- References
- 24
- FWCI
- 52.26
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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