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| Multivariantne uurimiskvantitatiivne uurimus× | Multivariatiivne korrelatiivuuring× | |
|---|---|---|
| Valdkond | Uurimisdisain | Uurimisdisain |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward | 1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s |
| Looja≠ | Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century) | Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others |
| Tüüp≠ | Quantitative research design | Non-experimental quantitative research design |
| Algallikas≠ | Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 |
| Rööpnimetused | multivariate exploratory design, exploratory multivariate analysis, multivariate data exploration, MEQ research | multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research |
| Seotud≠ | 5 | 2 |
| Kokkuvõte≠ | Multivariate exploratory quantitative research is a design in which researchers simultaneously examine multiple quantitative variables without imposing a predetermined structural model, using techniques such as exploratory factor analysis, cluster analysis, or principal component analysis to detect latent patterns, natural groupings, or underlying dimensions in the data. The goal is discovery and pattern recognition rather than hypothesis confirmation. | Multivariate correlational research is a non-experimental quantitative design that examines the simultaneous associations among three or more variables. Rather than manipulating conditions, the researcher measures naturally occurring variables and uses techniques such as multiple regression, canonical correlation, or structural equation modeling to map the pattern and strength of their interrelationships. It is the dominant design when the goal is to understand how a set of predictors jointly relates to one or more outcome variables. |
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