विधियों की तुलना करें

	बहुचर कोहोर्ट अनुसंधान	बहुभिन्नरूपी अनुदैर्ध्य अनुसंधान
क्षेत्र	अनुसंधान अभिकल्प	अनुसंधान अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1950s–1970s (cohort methods); multivariate extensions prominent from 1970s onward	1970s–1980s (formalized in behavioral sciences literature)
प्रवर्तक	Epidemiology and biostatistics tradition; advanced by Rothman, Breslow, and colleagues	Nesselroade, Baltes, and the developmental/behavioral sciences tradition
प्रकार	Observational quantitative research design	Quantitative observational research design
मौलिक स्रोत	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641	Nesselroade, J. R., & Baltes, P. B. (Eds.). (1979). Longitudinal Research in the Study of Behavior and Development. Academic Press. ISBN: 978-0125154505
उपनाम	multivariate cohort study, cohort study with multivariate analysis, multivariable cohort design, multivariate longitudinal cohort	longitudinal multivariate design, MLR, multivariate panel study, multivariate repeated-measures design
संबंधित	5	4
सारांश	Multivariate cohort research follows a defined group of individuals forward in time, collecting data on multiple exposures, outcomes, and covariates simultaneously. By applying multivariate statistical models — such as Cox regression, mixed-effects models, or structural equation models — researchers can disentangle the independent contributions of several predictors to one or more outcomes while controlling for confounders. The design is widely used in epidemiology, public health, psychology, and social sciences.	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.