So sánh phương pháp

	Nghiên cứu kiểm định mô hình phân cấp	Nghiên cứu kiểm định mô hình theo chiều dọc
Lĩnh vực	Thiết kế nghiên cứu	Thiết kế nghiên cứu
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)	1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s)
Người khởi xướng	Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen	Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett)
Loại	Quantitative confirmatory research design	Quantitative, confirmatory, longitudinal design
Công trình gốc	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968
Tên gọi khác	multilevel model testing, hierarchical SEM, nested model testing, HLM model testing	longitudinal confirmatory modeling, longitudinal SEM, panel model testing, longitudinal structural modeling
Liên quan	5	6
Tóm tắt	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.	Longitudinal model testing research combines repeated measurement across time with formal, a priori structural modeling to confirm or disconfirm hypothesized relationships among constructs. Rather than simply describing change, it tests whether a pre-specified theoretical model — typically a structural equation model or growth model — fits observed data collected at two or more time points. This design supports causal inference more convincingly than cross-sectional approaches by capturing temporal ordering of variables.