পদ্ধতির তুলনা করুন

	Hierarchical Model Testing Research	দীর্ঘমেয়াদী মডেল পরীক্ষা গবেষণা
ক্ষেত্র	গবেষণা নকশা	গবেষণা নকশা
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)	1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s)
প্রবর্তক	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)
ধরন	Quantitative confirmatory research design	Quantitative, confirmatory, longitudinal design
মৌলিক উৎস	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
অপর নাম	multilevel model testing, hierarchical SEM, nested model testing, HLM model testing	longitudinal confirmatory modeling, longitudinal SEM, panel model testing, longitudinal structural modeling
সম্পর্কিত	5	6
সারসংক্ষেপ	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.