Your browser doesn't support javascript.
loading
From alpha to omega and beyond! A look at the past, present, and (possible) future of psychometric soundness in the Journal of Applied Psychology.
Cortina, Jose M; Sheng, Zitong; Keener, Sheila K; Keeler, Kathleen R; Grubb, Leah K; Schmitt, Neal; Tonidandel, Scott; Summerville, Karoline M; Heggestad, Eric D; Banks, George C.
Afiliación
  • Cortina JM; Department of Management and Entrepreneurship, Virginia Commonwealth University.
  • Sheng Z; Future of Work Institute, Curtin University.
  • Keener SK; Department of Management, Old Dominion University.
  • Keeler KR; Department of Management and Human Resources, The Ohio State University.
  • Grubb LK; Department of Management Information Systems, East Carolina University.
  • Schmitt N; Department of Psychology, Michigan State University.
  • Tonidandel S; Department of Management, University of North Carolina-Charlotte.
  • Summerville KM; Organizational Science, University of North Carolina-Charlotte.
  • Heggestad ED; Department of Psychological Science, University of North Carolina-Charlotte.
  • Banks GC; Department of Management, University of North Carolina-Charlotte.
J Appl Psychol ; 105(12): 1351-1381, 2020 Dec.
Article en En | MEDLINE | ID: mdl-32772525
ABSTRACT
The psychometric soundness of measures has been a central concern of articles published in the Journal of Applied Psychology (JAP) since the inception of the journal. At the same time, it isn't clear that investigators and reviewers prioritize psychometric soundness to a degree that would allow one to have sufficient confidence in conclusions regarding constructs. The purposes of the present article are to (a) examine current scale development and evaluation practices in JAP; (b) compare these practices to recommended practices, previous practices, and practices in other journals; and (c) use these comparisons to make recommendations for reviewers, editors, and investigators regarding the creation and evaluation of measures including Excel-based calculators for various indices. Finally, given that model complexity appears to have increased the need for short scales, we offer a user-friendly R Shiny app (https//orgscience.uncc.edu/about-us/resources) that identifies the subset of items that maximize a variety of psychometric criteria rather than merely maximizing alpha. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Psicología Aplicada Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: J Appl Psychol Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Psicología Aplicada Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: J Appl Psychol Año: 2020 Tipo del documento: Article