Comparar métodos

	Pesquisa de Teste Comparativo de Modelos	Pesquisa Confirmatória Comparativa
Área	Delineamento de pesquisa	Delineamento de pesquisa
Família	Process / pipeline	Process / pipeline
Ano de origem	1969–2000s	1971 (Jöreskog); systematized in organizational research by 2000
Autor original	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)
Tipo	Quantitative confirmatory-comparative research design	Quantitative comparative research design
Fonte seminal	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.
Outros nomes	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
Relacionados	4	4
Resumo	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.