Back to Bayes-ics: Improving Universal Screening Decisions by Quantifying Uncertainty
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| Title: | Back to Bayes-ics: Improving Universal Screening Decisions by Quantifying Uncertainty |
|---|---|
| Language: | English |
| Authors: | Garret J. Hall (ORCID |
| Source: | Assessment for Effective Intervention. 2026 51(2):67-83. |
| Availability: | SAGE Publications and Hammill Institute on Disabilities. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Elementary Education Junior High Schools Middle Schools Secondary Education |
| Descriptors: | Bayesian Statistics, Screening Tests, Academic Ability, At Risk Students, Prediction, Statistical Inference, Probability, Computation, Regression (Statistics), Hierarchical Linear Modeling, Decision Making, Educational Assessment, Elementary School Students, Middle School Students, Achievement Tests |
| Assessment and Survey Identifiers: | Measures of Academic Progress |
| DOI: | 10.1177/15345084251392860 |
| ISSN: | 1534-5084 1938-7458 |
| Abstract: | Universal screeners of academic skills in schools are intended to predict the probability of academic risk in an efficient and economical manner. Recent methods of calculating post-test risk probabilities have been demonstrated to be simple and efficient to calculate, improving data-based decision-making practices in schools. However, these methods do not leverage the full advantages of Bayesian statistical inference, thereby limiting the quantification of uncertainty in the calculation of posterior probabilities of risk. This could produce overly deterministic data-based decisions. Bayesian ordinal regression models (BORMs) are a fully Bayesian extension of existing posterior probability calculations, and they offer multiple potential advantages for enhancing universal screening practices in schools. Through simulations and an applied example using real screening data, we elucidate some of the issues around BORMs in screening, including potential strengths (e.g., multilevel modeling) and barriers to practice (difficulty of interpretation/implementation). We discuss how BORMs can further advance both research and practice of data-based decision-making in universal screening in schools. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1496482 |
| Database: | ERIC |
| Abstract: | Universal screeners of academic skills in schools are intended to predict the probability of academic risk in an efficient and economical manner. Recent methods of calculating post-test risk probabilities have been demonstrated to be simple and efficient to calculate, improving data-based decision-making practices in schools. However, these methods do not leverage the full advantages of Bayesian statistical inference, thereby limiting the quantification of uncertainty in the calculation of posterior probabilities of risk. This could produce overly deterministic data-based decisions. Bayesian ordinal regression models (BORMs) are a fully Bayesian extension of existing posterior probability calculations, and they offer multiple potential advantages for enhancing universal screening practices in schools. Through simulations and an applied example using real screening data, we elucidate some of the issues around BORMs in screening, including potential strengths (e.g., multilevel modeling) and barriers to practice (difficulty of interpretation/implementation). We discuss how BORMs can further advance both research and practice of data-based decision-making in universal screening in schools. |
|---|---|
| ISSN: | 1534-5084 1938-7458 |
| DOI: | 10.1177/15345084251392860 |
