A method for ordinal outcomes: the ordered stereotype model
Open accessAbstract
Objective : The collection and use of ordinal variables are common in many psychological and psychiatric studies. Although the models for continuous variables have similarities to those for ordinal variables, there are advantages when a model developed for modeling ordinal data is used such as avoiding “floor” and “ceiling” effects and avoiding to assign scores, as it happens in continuous models, which can produce results sensitive to the score assigned. This paper introduces and focuses on the application of the ordered stereotype model, which was developed for modeling ordinal outcomes and is not so popular as other models such as linear regression and proportional odds models. This paper aims to compare the performance of the ordered stereotype model with other more commonly used models among researchers and practitioners. Methods : This article compares the performance of the stereotype model against the proportional odd and linear regression models, with three, four, and five levels of ordinal categories and sample sizes 100, 500, and 1000. This paper also discusses the problem of treating ordinal responses as continuous using a simulation study. The trend odds model is also presented in the application. Results : Three types of models were fitted in one real‐life example, including ordered stereotype, proportional odds, and trend odds models. They reached similar conclusions in terms of the significance of covariates. The simulation study evaluated the performance of the ordered stereotype model under four cases. The performance varies depending on the scenarios. Conclusions : The method presented can be applied to several areas of psychiatry dealing with ordinal outcomes. One of the main advantages of this model is that it breaks with the assumption of levels of the ordinal response are equally spaced, which might be not true.
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The ordered stereotype model estimates the true spacing between points on the ordinal scale based on the data, resulting in a continuous transformed scale suitable for a linear model.
“The OSM estimates the true spacing between the points on the ordinal scale based on the data, thereby resulting in a transformed scale that is continuous and thus suitable for a linear model.”
From What is beautiful is still good: the attractiveness halo effect in the era of beauty filters (2024) · cites this paper as [59]
The Methods section (‘Estimation of non-equal space among ordinal categories’; page not provided) says the model estimates spacing between ordinal categories from the data. The Discussion (page not provided) does not say this produces a continuous scale suitable for linear modelling: it discusses linear models when category scores are known beforehand and warns that fitting linear regression to ordinal responses can be misleading. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCategories with overlapping standard deviation intervals are merged into the same category in the OSM.
“Furthermore, categories with overlapping standard deviation intervals are merged into the same category.”
From What is beautiful is still good: the attractiveness halo effect in the era of beauty filters (2024) · cites this paper as [59]
The Methods section (‘Estimation of non‐equal space among ordinal categories’; page unavailable) says adjacent categories could be collapsed if their score parameters are equal. It says overlapping confidence intervals may provide evidence that categories are not distinguishable; it does not specify standard deviation intervals or say that overlap automatically triggers merging. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe OSM estimates the distance between different categories based on the actual data instead of assuming that all categories are equidistant.
“The ϕk′s are interpreted as scores and help estimate the distance between different categories based on the actual data (ratings in our case) instead of assuming that all categories are equidistant.”
From What is beautiful is still good: the attractiveness halo effect in the era of beauty filters (2024) · cites this paper as [59]
The OSM estimates the underlying scale by computing the expected probabilities of the categories based on potential covariates in the data.
“Thus, the OSMs estimate the underlying scale by computing the expected probabilities of the categories based on potential covariates in the data.”
From What is beautiful is still good: the attractiveness halo effect in the era of beauty filters (2024) · cites this paper as [59]
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- Authors
- Fernandez, D., Liu, I., & Costilla, R.
- Affiliations
- Research and Development Unit, Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu, CIBERSAM Barcelona Spain
- Victoria University of Wellington (NZ)
- The University of Queensland (AU)
- Venue
- International Journal of Methods in Psychiatric Research
- Publisher
- Wiley
- Published
- 2019-09-30
- Volume
- 28
- Issue
- 4
- Pages
- e1801-e1801
- ISSN
- 1049-8931 (ISSN-L)
- 1557-0657
- PMC
- PMC7027430
- OpenAlex
- W2979072339
- Cited by
- 26
- References
- 37
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 4 · 2023: 1 · 2022: 5 · 2021: 3 · 2020: 1 · 2019: 1
- FWCI
- 1.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Methods in Psychiatric Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (publishedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- Funders
- Royal Society of New Zealand (Marsden Fund - E2987-3648)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- ordinal outcomes, proportional odds model, linear regression, floor and ceiling effects, model comparison, psychiatric research
- MeSH terms
- Adult, Mental Disorders, Humans, Psychometrics, Models, Statistical, Young Adult
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