The Importance of Thinking Multivariately When Setting Subscale Cutoff Scores.

classification cutoff scores measurement multivariate analysis reliability

Journal

Educational and psychological measurement
ISSN: 1552-3888
Titre abrégé: Educ Psychol Meas
Pays: United States
ID NLM: 0372767

Informations de publication

Date de publication:
Jun 2022
Historique:
entrez: 21 4 2022
pubmed: 22 4 2022
medline: 22 4 2022
Statut: ppublish

Résumé

Setting cutoff scores is one of the most common practices when using scales to aid in classification purposes. This process is usually done univariately where each optimal cutoff value is decided sequentially, subscale by subscale. While it is widely known that this process necessarily reduces the probability of "passing" such a test, what is not properly recognized is that such a test loses power to meaningfully discriminate between target groups with each new subscale that is introduced. We quantify and describe this property via an analytical exposition highlighting the counterintuitive geometry implied by marginal threshold-setting in multiple dimensions. Recommendations are presented that encourage applied researchers to think jointly, rather than marginally, when setting cutoff scores to ensure an informative test.

Identifiants

pubmed: 35444337
doi: 10.1177/00131644211023569
pii: 10.1177_00131644211023569
pmc: PMC9014732
doi:

Types de publication

Journal Article

Langues

eng

Pagination

517-538

Informations de copyright

© The Author(s) 2021.

Déclaration de conflit d'intérêts

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Références

J Pediatr Psychol. 1988 Mar;13(1):55-68
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Biochem Med (Zagreb). 2016 Oct 15;26(3):297-307
pubmed: 27812299
J Pediatr Psychol. 1997 Jun;22(3):313-28
pubmed: 9212550

Auteurs

Edward Kroc (E)

University of British Columbia, Vancouver, British Columbia, Canada.

Oscar L Olvera Astivia (OL)

University of Washington, Seattle, Washington, USA.

Classifications MeSH