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CYRILLIC TO LATIN CONVERSION REQUIRED×Istraživanje – Longitudinalni dizajn istraživanja×Višerazinsko modelovanje×
OblastDizajn istraživanjaDizajn istraživanjaIstraživačka statistika
PorodicaProcess / pipelineProcess / pipelineProcess / pipeline
Godina nastanka1970s–1980s (formal integration of comparative and panel designs)Late 19th–early 20th century; methodologically codified through the 20th century1992
TvoracDeveloped across social science disciplines; seminal formalizations by Cheng Hsiao (panel econometrics) and Melvin Kohn (comparative sociology)No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John WillettAnthony Bryk and Stephen Raudenbush
TipQuantitative longitudinal comparative designQuantitative (or mixed) observational research designMethod
Temeljni izvorHsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. ISBN: 978-1107038691Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗
Drugi nazivicross-national panel study, comparative longitudinal panel, pooled cross-sectional time-series design, multi-group panel designlongitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational studyHLM, mixed-effects models, random effects models, MLM
Srodne343
SažetakComparative panel research tracks the same individuals, organizations, or macro-level units (e.g., countries, regions) across multiple time points while simultaneously comparing findings across two or more distinct groups or contexts. By combining the temporal depth of panel measurement with the analytical leverage of systematic comparison, this design can distinguish change processes that are universal from those that are context-specific — a capability neither pure panel nor single-sample longitudinal designs offer on their own.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.
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ScholarGateUporedite metode: Comparative Panel Research · Longitudinal Research · Multilevel Modeling. Preuzeto 2026-06-18 sa https://scholargate.app/sr/compare