方法对比

	比较验证性研究	假设检验研究
领域	研究设计	研究设计
方法族	Process / pipeline	Process / pipeline
起源年份	1971 (Jöreskog); systematized in organizational research by 2000	Early 20th century (Fisher 1925; Neyman–Pearson 1933)
提出者	Karl Jöreskog (multigroup CFA foundation); Robert Vandenberg & Charles Lance (organizational application)	Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson
类型	Quantitative comparative research design	Quantitative confirmatory research design
开创性文献	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.	Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417603
别名	multigroup confirmatory research, cross-group confirmatory study, comparative hypothesis testing design, comparative model testing research	hypothetico-deductive research, confirmatory quantitative research, null hypothesis significance testing, NHST design
相关	4	4
摘要	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.	Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences.