방법 비교

	계층적 모형 검증 연구	확증적 연구
분야	연구설계	연구설계
계열	Process / pipeline	Process / pipeline
기원 연도	1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)	1934 (Popper); widely adopted in social sciences from 1960s onward
창시자	Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen	Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others
유형	Quantitative confirmatory research design	Quantitative research design
원전	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson. ISBN: 978-0415278447
별칭	multilevel model testing, hierarchical SEM, nested model testing, HLM model testing	hypothesis-testing research, deductive research, theory-testing research, confirmatory study
관련	5	4
요약	Hierarchical model testing research is a quantitative design that evaluates theoretically derived models using data with a nested or clustered structure — for example, students within classrooms, employees within organisations, or patients within hospitals. It applies hierarchical linear models (HLM) or multilevel structural equation models (ML-SEM) to test whether a proposed set of relationships holds after properly accounting for the non-independence introduced by grouping.	Confirmatory research is a deductive quantitative design in which the researcher specifies hypotheses derived from existing theory before data collection, then tests whether the data support or refute those hypotheses. Unlike exploratory approaches that generate ideas from data, confirmatory research begins with an established theoretical framework, pre-registers predictions, and applies statistical tests to evaluate those predictions against empirical evidence. It is the backbone of hypothesis-driven social, behavioral, and health science inquiry.