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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 0000-0002-8285-3239), Emma Doyle
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
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  Data: 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
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  Data: 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.
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      – Text: English
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      – SubjectFull: Bayesian Statistics
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      – SubjectFull: Screening Tests
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      – SubjectFull: Academic Ability
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      – SubjectFull: At Risk Students
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      – SubjectFull: Hierarchical Linear Modeling
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