Models for Ordered Categorical Variables: An Application
Ordered logit and ordered probit models, commonly used ordered categorical variable models, are used when the dependent variable is categorical and ordered. The validity of the predictions of these models depends on the assumption of parallel slopes. These models can be used if the assumption of...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
ORDT: Organization for Research Development and Training
2024-11-01
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| Series: | Journal of Interdisciplinary Sciences |
| Subjects: | |
| Online Access: | https://journalofinterdisciplinarysciences.com/wp-content/uploads/2024/11/2-Models-for-Ordered-Categorical-Variables-An-Application.pdf |
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| Summary: | Ordered logit and ordered probit models, commonly used ordered categorical variable
models, are used when the dependent variable is categorical and ordered. The validity of the
predictions of these models depends on the assumption of parallel slopes. These models can be used if
the assumption of parallel slopes is met. When this assumption is not met, the generalized ordered
logit/probit model, which is more flexible in terms of assumption, or the multinomial logit model can
be used by ignoring the assumption. In this connection, an exemplary data set was taken, and first of
all, the assumption of parallel slopes was investigated. These models were compared using loglikelihood, Akaike Information Criteria (AIC), and Bayes Information Criteria (BIC) statistics for the
validity of the models. In this way, the lowest log-likelihood, AIC, and BIC values were found for the
generalized logit model. |
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| ISSN: | 2594-3405 |