Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzmainīgā kohortas izpēte× | Daudzvariantu longitudinālie pētījumi× | |
|---|---|---|
| Nozare | Pētījuma dizains | Pētījuma dizains |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1950s–1970s (cohort methods); multivariate extensions prominent from 1970s onward | 1970s–1980s (formalized in behavioral sciences literature) |
| Autors≠ | Epidemiology and biostatistics tradition; advanced by Rothman, Breslow, and colleagues | Nesselroade, Baltes, and the developmental/behavioral sciences tradition |
| Tips≠ | Observational quantitative research design | Quantitative observational research design |
| Pirmavots≠ | 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 |
| Citi nosaukumi | 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 |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. |
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