What is it about?

This study examines whether a new system of norm-referencing, dynamic norming, might be feasible. In contrast to "static norms" in which a fixed normative group is used to evaluate client performance, "Dynamic Norms" allows the clinician to select a subset of individuals from a database who reflect the important characteristics of the test taker to serve as a norm-referenced group for standardized testing. In this demonstration of Dynamic Norms, we consider the principles involved and the sample sizes needed to provide a stable normative comparison for language sample analysis using the SALT system.

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Why is it important?

Being able to select a normative sample that is more like the test taker in terms of important characteristics can provide a more accurate view of performance. Although this paper considered only age, other applications of Dynamic Norming might incorporate characteristics like sex, socio-economic status, educational status, racial/ethinic groups, or language variation. Therefore, the principles behind Dynamic Norms are a potentially powerful way to reduce assessment bias and increase assessment accuracy.

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This page is a summary of: Dynamic Norming for Systematic Analysis of Language Transcripts, Journal of Speech Language and Hearing Research, January 2022, American Speech-Language-Hearing Association (ASHA), DOI: 10.1044/2021_jslhr-21-00227.
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