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	Modelowanie wielopoziomowe	Badania panelowe
Dziedzina	Statystyka w badaniach	Projektowanie badań
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1992	1970s-1980s (econometric formalization); earlier social survey use from 1940s
Twórca	Anthony Bryk and Stephen Raudenbush	Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s
Typ	Method	Quantitative longitudinal observational design
Źródło pierwotne	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.	Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717
Inne nazwy	HLM, mixed-effects models, random effects models, MLM	panel study, panel survey, longitudinal panel, repeated-measures panel
Pokrewne	3	3
Podsumowanie	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.	Panel research is a quantitative longitudinal design in which the same individuals, organizations, or other units are measured repeatedly across two or more time points. Unlike cross-sectional surveys that capture a single snapshot, a panel tracks change within units, enabling researchers to separate genuine within-unit change from between-unit differences and to model causal dynamics over time.