विधियों की तुलना करें

	बहुभिन्नरूपी अनुदैर्ध्य अनुसंधान	बहुस्तरीय मॉडलिंग
क्षेत्र	अनुसंधान अभिकल्प	अनुसंधान सांख्यिकी
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1970s–1980s (formalized in behavioral sciences literature)	1992
प्रवर्तक	Nesselroade, Baltes, and the developmental/behavioral sciences tradition	Anthony Bryk and Stephen Raudenbush
प्रकार	Quantitative observational research design	Method
मौलिक स्रोत	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.
उपनाम	longitudinal multivariate design, MLR, multivariate panel study, multivariate repeated-measures design	HLM, mixed-effects models, random effects models, MLM
संबंधित	4	3
सारांश	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.