Evidence-Based Assessment in Special Education Research: Advancing the Use of Evidence in Assessment Tools and Empirical Processes

• Elizabeth Talbott ^[5] ; Andrés de los Reyes ^[1] ; Devin M. Kearns ^[2] ; Jeannette Mancilla-Martinez ^[3] ; Mo Wang ^[4]
  1. [1] University of Maryland, College Park

     University of Maryland, College Park

     Estados Unidos

     
  2. [2] University of Connecticut

     University of Connecticut

     Town of Mansfield, Estados Unidos

     
  3. [3] Vanderbilt University

     Vanderbilt University

     Estados Unidos

     
  4. [4] University of Florida

     University of Florida

     Estados Unidos

     
  5. [5] William and Mary

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• Localización: Exceptional children, ISSN-e 2163-5560, ISSN 0014-4029, Vol. 89, Nº. 4, 2023, págs. 467-487
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Evidence-based assessment (EBA) requires that investigators employ scientific theories and research findings to guide decisions about what domains to measure, how and when to measure them, and how to make decisions and interpret results. To implement EBA, investigators need high-quality assessment tools along with evidence-based processes. We advance EBA in three sections in this article. First, we describe an empirically grounded framework, the Operations Triad Model (OTM), to inform EBA decision-making in the articulation of relevant educational theory. Originally designed for interpreting mental health assessments, we describe features of the OTM that facilitate its fusion with educational theory, namely its falsifiability. In turn, we cite evidence to support the OTM's ability to inform hypothesis generation and testing, study design, instrument selection, and measurement validation. Second, we describe quality indicators for interpreting psychometric data about measurement tools, which informs both the development and selection of measures and the process of measurement validation. Third, we apply the OTM and EBA to research in special education in two contexts: (a) empirical research for causal explanation and (b) implementation science research. We provide open data resources to advance measurement validation and conclude with future directions for research.