Сравнение на методи

	Изследване с йерархично тестване на модели	Изследване за тестване на модели
Област	Дизайн на изследването	Дизайн на изследването
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)	1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s
Създател	Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen	Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition
Тип	Quantitative confirmatory research design	Confirmatory quantitative research design
Основополагащ източник	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344
Други названия	multilevel model testing, hierarchical SEM, nested model testing, HLM model testing	model-based research, structural model testing, theory-testing research, MTR
Свързани	5	5
Резюме	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.	Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (CFA), it evaluates whether the data-implied covariance structure is consistent with the theoretically derived one, yielding fit indices that indicate model-data correspondence.