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	Badania podłużne	Modelowanie wielopoziomowe
Dziedzina	Projektowanie badań	Statystyka w badaniach
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	Late 19th–early 20th century; methodologically codified through the 20th century	1992
Twórca	No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett	Anthony Bryk and Stephen Raudenbush
Typ	Quantitative (or mixed) observational research design	Method
Źródło pierwotne	Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Inne nazwy	longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study	HLM, mixed-effects models, random effects models, MLM
Pokrewne	4	3
Podsumowanie	Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time.	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.