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	Hierarchiczne badania opisowe	Modelowanie wielopoziomowe
Dziedzina	Projektowanie badań	Statystyka w badaniach
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1980s–1990s (multilevel descriptive formalization)	1992
Twórca	Formalized within survey and educational research traditions; associated with Hox, Raudenbush, Bryk, and Creswell	Anthony Bryk and Stephen Raudenbush
Typ	Quantitative observational/descriptive design	Method
Źródło pierwotne	Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge. ISBN: 978-1848728455	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Inne nazwy	multilevel descriptive design, nested descriptive study, hierarchical survey design, stratified descriptive research	HLM, mixed-effects models, random effects models, MLM
Pokrewne	4	3
Podsumowanie	Hierarchical descriptive research is an observational design that documents the current state of a phenomenon across two or more nested levels — for example, students within classrooms within schools, or employees within teams within organizations. Rather than testing hypotheses or explaining causation, it describes distributions, frequencies, and relationships at each level, making explicit the structured, layered nature of the population being studied.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.