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Papers on “standardized testing validity equity bias”

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  1. Testing for funnel plot asymmetry of standardized mean differences

    J. Pustejovsky, Melissa A. Rodgers · 2018 · Research Synthesis Methods · 307 cites

    Publication bias and other forms of outcome reporting bias are critical threats to the validity of findings from research syntheses. A variety of methods have been proposed for detecting selective outcome reporting in a collection of effect size estimates, including several methods based on assessment of asymmetry of funnel plots, such as the Egger's regression test, the rank correlation test, and the Trim‐and‐Fill test. Previous research has demonstated that the Egger's regression test is miscalibrated when applied to log‐odds ratio effect size estimates, because of artifactual correlation between the effect size estimate and its standard error. This study examines similar problems that occ

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  2. Concurrent validity and test-retest reliability of VALD ForceDecks' strength, balance, and movement assessment tests.

    T. Collings, Yuri Lopes Lima, B. Dutaillis, et al. · 2024 · Journal of science and medicine in sport · 113 cites

    OBJECTIVES To evaluate the concurrent validity and test-retest reliability of common movement, strength, and balance tests using portable uniaxial dual force plates. DESIGN Repeated measures cross-sectional study. METHODS Sixteen healthy individuals participated in two testing sessions, where they performed 12 different movement, strength, and balance tests. Vertical ground reaction force and centre of pressure data were collected using the VALD ForceDecks simultaneously with ground-embedded laboratory force plates. Concurrent validity was assessed using root mean square error for raw time-series data and Bland-Altman plots for discrete metrics. Test-retest reliability was assessed using

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  5. Discussion on the validity of commonly used reliability indices in sports medicine and exercise science: a critical review with data simulations

    K. Warneke, T. Gronwald, Sebastian Wallot, et al. · 2025 · European Journal of Applied Physiology · 39 cites

    Apart from objectivity and validity, reliability is considered a precondition for testing within scientific works, as unreliable testing protocols limit conclusions, especially for practical application. Classification guidelines commonly refer to relative reliability, focusing on Pearson correlation coefficients (rp) and intraclass correlation coefficients (ICC). On those, the standard error of measurement (SEM) and the minimal detectable change (MDC) are often calculated in addition to the variability coefficient (CV). These, however, do not account for systematic or random errors (e.g., standardization problems). To illustrate, we applied common reliability statistics in sports science on

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  6. Achieving Assessment Equity and Fairness: Identifying and Eliminating Bias in Assessment Tools and Practices

    Mohamad Ahmad Saleem Khasawneh, Yusra Jadallah Abed Khasawneh · 2023 · Preprints.org · 23 cites

    The objective of this study is to examine different types of bias present in assessment tools and techniques, identify and eliminate any forms of favoritism, and suggest effective approaches for advancing equity and objectivity in assessment procedures. The research utilizes a qualitative methodology, incorporating surveys and focus group discussions with educators and students to collect data. Thematic analysis is a method employed for the purpose of analyzing qualitative data. The results suggest that students hailing from diverse cultural backgrounds perceive partiality in the language and cultural context of evaluations, leading to diminished scores. According to academic professionals,

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  7. Equity and Generalizability of Artificial Intelligence for Skin-Lesion Diagnosis Using Clinical, Dermoscopic, and Smartphone Images: A Systematic Review and Meta-Analysis

    J. Tjiu, Chia-Fang Lu · 2025 · Medicina · 19 cites

    Background and Objectives: Artificial intelligence (AI) has shown promising performance in skin-lesion classification; however, its fairness, external validity, and real-world reliability remain uncertain. This systematic review and meta-analysis evaluated the diagnostic accuracy, equity, and generalizability of AI-based dermatology systems across diverse imaging modalities and clinical settings. Materials and Methods: A comprehensive search of PubMed, Embase, Web of Science, and ClinicalTrials.gov (inception–31 October 2025) identified diagnostic accuracy studies using clinical, dermoscopic, or smartphone images. Eighteen studies (11 melanoma-focused; 7 mixed benign–malignant) met inclusion

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