Are Two Screeners Better Than One? A Simulation Study of Correlation and Classification in Universal Screening

White, Christine M.; Schatschneider, Christopher

doi:10.1007/s40688-023-00478-0

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…In both cases, however, while the priority lies on one of these metrics, we generally want to maximise both (Jenkins et al, 2007;Wilson & Lonigan, 2010). When evaluating a screening tools' classification accuracy, a common benchmark is a minimum of .80 sensitivity and specificity (Petscher et al, 2019), though some call for sensitivities to exceed .90 (White & Schatschneider, 2023).…”

Section: Sensitivity Vs Specificitymentioning

confidence: 99%

Differences in Reading Screening Accuracy by Percentile Cutoff and English Proficiency: Feature Selection and Group-wise Prediction Evaluation

Siebert,

Bell,

Gutierrez

et al. 2024

Preprint

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This exploratory predictive study investigates our capacity to predict risk of developing reading difficulties based on a battery of tasks tapping into commonly used reading screening constructs. A key question pertains to the extent to which we can predict fairly and with equal accuracy for native English-speakers (EO) and emerging multilinguals (English learners [ELs] with sufficient English-proficiency to participate in English screening). This prediction exercise takes place in a linguistically diverse sample, over different prediction intervals (end of same and subsequent academic year), and across a range of percentile cut-offs (1st-25th) defining reading difficulty.Specifically, using a sample of 1,692 kindergarteners and first-graders from 24 schools across 13 Californian public school districts, we (a) identified the most important reading screening tasks for a range of percentile cut-offs defining ‘risk’ using random forest feature selection; (b) built a series of logisitic regression classifiers; and (c) evaluated these models’ performances for ELs and EO students spearately. When risk is defined as performance below the tenth percentile or higher, the same tasks generally surface as most important predictors of later word reading difficulty (letter naming and sounds for kindergarten; word and non-word reading for first graders), though with different relative importance for EO vs EL students and for the different prediction intervals. Though most models can be optimised to exceed sensitivity and specificity values of .80, classification accuracy is higher for EO students, especially for two-year predictions. Though models meeting conventionally used minimum accuracy requirements for both the EO and EL groups can be produced, more work is needed to achieve truly linguistically equitable (i.e., equally accurate) screening. We discuss the implications and feasibility of universal reading screening using English screening tasks in linguistically diverse populations.

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Section: Sensitivity Vs Specificitymentioning

confidence: 99%

Differences in Reading Screening Accuracy by Percentile Cutoff and English Proficiency: Feature Selection and Group-wise Prediction Evaluation

Siebert,

Bell,

Gutierrez

et al. 2024

Preprint

View full text Add to dashboard Cite

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A Review of Screeners to Identify Risk of Developmental Language Disorder

Bao,

Komesidou,

Hogan

2024

Am J Speech Lang Pathol

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Purpose: This review aims to comprehensively summarize, compare, and evaluate screeners used to identify risk for developmental language disorder (DLD), a common learning disability that is underidentified. Screening for DLD is a cost-effective way to identify children in need of further assessment and, in turn, provides much needed supports. Method: We identified 15 commercially available English language DLD screeners in North America. We then characterized each screener on 27 aspects in three domains, including (a) accessibility information (acronym, subtest, website, cost, materials included, publish year, examiner qualification, age range, administration time, and administration format), (b) usability features (dialect compatibility, progress monitoring function, actionable follow-up instruction, group assessment capability, and online administration availability), and (c) technical standards (the availability of a technical manual, conceptual definition, the sample size used in classification accuracy calculation, sample distribution, year of sample collection, outcome measure, sample base rate, cutoff score, sensitivity, specificity, positive predictive value, and negative predictive value). Results: We obtained sufficient accessibility information from 14 out of 15 (93%) screeners. In contrast, none of the screeners (0%) included comprehensive usability features. Ten screeners (67%) included a range of classification accuracy (70%–100% sensitivity and 68%–90% specificity). We provided areas of strength and weakness for each screener as a quick reference for users and generated screener recommendations for five practical scenarios. Conclusions: Our findings presented some DLD screeners that meet most standards and highlight numerous areas for improvement, including improving classification accuracy and clarifying follow-up instructions for children who are identified with DLD risk. Screening for DLD is critical to provide timely early identification, intervention, and classroom support, which in turn facilitates student outcomes.

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Are Two Screeners Better Than One? A Simulation Study of Correlation and Classification in Universal Screening

Cited by 2 publications

References 40 publications

Differences in Reading Screening Accuracy by Percentile Cutoff and English Proficiency: Feature Selection and Group-wise Prediction Evaluation

Differences in Reading Screening Accuracy by Percentile Cutoff and English Proficiency: Feature Selection and Group-wise Prediction Evaluation

A Review of Screeners to Identify Risk of Developmental Language Disorder

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