방법 비교

	다변량 종단 연구	다수준 모형
분야	연구설계	연구 통계
계열	Process / pipeline	Process / pipeline
기원 연도	1970s–1980s (formalized in behavioral sciences literature)	1992
창시자	Nesselroade, Baltes, and the developmental/behavioral sciences tradition	Anthony Bryk and Stephen Raudenbush
유형	Quantitative observational research design	Method
원전	Nesselroade, J. R., & Baltes, P. B. (Eds.). (1979). Longitudinal Research in the Study of Behavior and Development. Academic Press. ISBN: 978-0125154505	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
별칭	longitudinal multivariate design, MLR, multivariate panel study, multivariate repeated-measures design	HLM, mixed-effects models, random effects models, MLM
관련	4	3
요약	Multivariate longitudinal research is a quantitative observational design that follows the same units — individuals, groups, or organizations — across two or more time points while measuring several outcome and predictor variables simultaneously. By combining the temporal dimension of longitudinal tracking with multivariate statistical analysis, it allows researchers to examine how a system of variables co-evolves, how early measures predict later outcomes across multiple domains, and whether relationships among variables are stable or change over 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.