방법 비교

	종단 연구	다수준 모형	패널 연구
분야	연구설계	연구 통계	연구설계
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	Late 19th–early 20th century; methodologically codified through the 20th century	1992	1970s-1980s (econometric formalization); earlier social survey use from 1940s
창시자	No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett	Anthony Bryk and Stephen Raudenbush	Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s
유형	Quantitative (or mixed) observational research design	Method	Quantitative longitudinal observational design
원전	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.	Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717
별칭	longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study	HLM, mixed-effects models, random effects models, MLM	panel study, panel survey, longitudinal panel, repeated-measures panel
관련	4	3	3
요약	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.	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.