방법 비교

	비교 모형 검증 연구	비교 확인 연구
분야	연구설계	연구설계
계열	Process / pipeline	Process / pipeline
기원 연도	1969–2000s	1971 (Jöreskog); systematized in organizational research by 2000
창시자	Rooted in structural equation modeling traditions; formalized through Jöreskog (1969) and extended by Vandenberg & Lance (2000)	Karl Jöreskog (multigroup CFA foundation); Robert Vandenberg & Charles Lance (organizational application)
유형	Quantitative confirmatory-comparative research design	Quantitative comparative research design
원전	Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344	Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.
별칭	comparative model comparison, cross-group model testing, competing model comparison research, comparative structural model evaluation	multigroup confirmatory research, cross-group confirmatory study, comparative hypothesis testing design, comparative model testing research
관련	4	4
요약	Comparative model testing research is a quantitative design in which two or more theoretically motivated models — or the same model evaluated across distinct groups or conditions — are systematically tested and compared using fit indices, likelihood-ratio tests, or information criteria. The goal is to determine which model better represents the data structure, or whether a model's parameter structure holds equally across comparison groups.	Comparative confirmatory research tests whether a pre-specified theoretical model or set of hypotheses holds equivalently across two or more distinct groups, time points, or contexts. It extends standard confirmatory analysis by explicitly imposing and evaluating equality constraints across groups, determining not only whether a model fits the data but whether its structure, factor loadings, and parameter estimates are comparable across populations. This design is foundational to cross-cultural, multi-site, and subgroup comparison studies.