Multivariate models provide an effective psychometric solution to the variability in classification accuracy of D-KEFS Stroop performance validity cutoffs.

Cutler, Laura; Greenacre, Matthew; Abeare, Christopher A; Sirianni, Christina D; Roth, Robert; Erdodi, Laszlo A

Cutler, Laura; Greenacre, Matthew; Abeare, Christopher A; Sirianni, Christina D; Roth, Robert; Erdodi, Laszlo A.

Afiliação

Cutler L; Department of Psychology, Neuropsychology Track, University of Windsor, Windsor, Ontario, Canada.
Greenacre M; Schulich School of Medicine, Western University, London, Ontario, Canada.
Abeare CA; Department of Psychology, Neuropsychology Track, University of Windsor, Windsor, Ontario, Canada.
Sirianni CD; School of Social Work, University of Windsor, Windsor, Ontario, Canada.
Roth R; Department of Psychiatry, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA.
Erdodi LA; Department of Psychology, Neuropsychology Track, University of Windsor, Windsor, Ontario, Canada.

Clin Neuropsychol ; 37(3): 617-649, 2023 04.

Article em En | MEDLINE | ID: mdl-35946813

Assuntos

Pacientes; Humanos; Testes Neuropsicológicos; Psicometria; Reprodutibilidade dos Testes; Sensibilidade e Especificidade

Palavras-chave

D-KEFS Stroop; embedded validity indicators; performance validity

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pacientes Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo

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PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pacientes Idioma: En Ano de publicação: 2023 Tipo de documento: Article