ScholarGate
Assistente

Comparar métodos

Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.

Ômega de McDonald Robusto×Teoria de Resposta ao Item (TRI)×
ÁreaPsicometriaPsicometria
FamíliaLatent structureLatent structure
Ano de origem1999 (omega); robust variant formalized in 2000s–2010s1952–1968
Autor originalRoderick P. McDonald (omega); robust extension via robust SEM estimators (MLR, DWLS)Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
TipoReliability coefficientProbabilistic measurement model
Fonte seminalMcDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates. ISBN: 978-0805830408Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗
Outros nomesrobust omega, omega total (robust), robust omega-total, robust composite reliabilityIRT, latent trait theory, item characteristic curve theory, modern test theory
Relacionados45
ResumoRobust McDonald's omega estimates the internal consistency reliability of a composite scale using factor-analytic loadings obtained through robust estimation methods (such as MLR or DWLS). Unlike standard omega or Cronbach's alpha, it remains accurate when item distributions are non-normal, skewed, or when the sample contains influential outliers — conditions common in applied psychological and educational measurement.Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons.
ScholarGateConjunto de dados
  1. v1
  2. 2 Fontes
  3. PUBLISHED
  1. v1
  2. 2 Fontes
  3. PUBLISHED

Ir para a pesquisa Baixar slides

ScholarGateComparar métodos: Robust McDonald's Omega · Item Response Theory. Recuperado em 2026-06-19 de https://scholargate.app/pt/compare