Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Cercetare de tip cohortă multivariată× | Cercetare cauzal-comparativă× | |
|---|---|---|
| Domeniu | Design de cercetare | Design de cercetare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1950s–1970s (cohort methods); multivariate extensions prominent from 1970s onward | 1964 |
| Autorul original≠ | Epidemiology and biostatistics tradition; advanced by Rothman, Breslow, and colleagues | Fred N. Kerlinger |
| Tip≠ | Observational quantitative research design | Non-experimental quantitative research design |
| Sursa seminală≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Denumiri alternative | multivariate cohort study, cohort study with multivariate analysis, multivariable cohort design, multivariate longitudinal cohort | ex post facto research, causal-comparative design, retrospective causal study, CCR |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | 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. | Causal-comparative research is a non-experimental quantitative design in which the researcher compares two or more groups that already differ on an independent variable — one that was not manipulated — to investigate possible causes or consequences of that difference. Because group membership is pre-existing rather than randomly assigned, the design can suggest causal relationships but cannot establish them with the certainty of a true experiment. It is widely used in education, psychology, and social sciences when experimental manipulation is impractical or unethical. |
| ScholarGateSet de date ↗ |
|
|