방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 이요인 모형 (일반 요인 및 특정 요인)× | 맥도날드 오메가(ω) 신뢰도 계수× | |
|---|---|---|
| 분야 | 심리측정학 | 심리측정학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 1937 | 1999 |
| 창시자≠ | Holzinger & Swineford (1937); modern revival by Reise (2012) | Roderick P. McDonald |
| 유형≠ | Confirmatory latent variable model | Reliability coefficient / latent variable model |
| 원전≠ | Reise, S. P. (2012). The Rediscovery of Bifactor Measurement Models. Multivariate Behavioral Research, 47(5), 667–696. DOI ↗ | McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates. ISBN: 978-0805830750 |
| 별칭≠ | Bifaktör Modeli — Genel ve Spesifik Faktörler, hierarchical factor model, general-specific factor model, Schmid-Leiman model | omega reliability, ω coefficient, omega total, omega hierarchical |
| 관련 | 6 | 6 |
| 요약≠ | The bifactor measurement model specifies that every indicator loads simultaneously on a single general factor and on one of several specific (group) factors. Formally introduced by Holzinger and Swineford in 1937 and brought into mainstream psychometrics by Reise (2012), it is now the standard tool for evaluating whether a multidimensional scale can legitimately yield a single composite score. | McDonald's omega is a factor-analysis-based reliability coefficient introduced by Roderick P. McDonald (1999) that quantifies the internal consistency of a composite score without requiring the restrictive assumption that all items contribute equally to the latent factor. It yields two complementary indices: ω_total, which captures overall reliability of the sum score, and ω_hierarchical (ωh), which reports how much of the composite's variance is explained specifically by a single general factor. |
| ScholarGate데이터셋 ↗ |
|
|