手法を比較

	比較パネル調査	縦断研究	多層レベルモデリング
分野	研究デザイン	研究デザイン	研究統計
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1970s–1980s (formal integration of comparative and panel designs)	Late 19th–early 20th century; methodologically codified through the 20th century	1992
提唱者	Developed 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 Willett	Anthony Bryk and Stephen Raudenbush
種類	Quantitative longitudinal comparative design	Quantitative (or mixed) observational research design	Method
原典	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. ISBN: 978-1107038691	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.
別名	cross-national panel study, comparative longitudinal panel, pooled cross-sectional time-series design, multi-group panel design	longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study	HLM, mixed-effects models, random effects models, MLM
関連	3	4	3
概要	Comparative 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.