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| Ricerca sulla verifica di modelli multivariati× | Ricerca Correlazionale Multivariata× | |
|---|---|---|
| Campo | Disegno della ricerca | Disegno della ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1970s–1980s (multivariate model testing as a distinct approach) | 1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s |
| Ideatore≠ | Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis) | Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others |
| Tipo≠ | Quantitative confirmatory research design | Non-experimental quantitative research design |
| Fonte seminale | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 |
| Alias | multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research | multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research |
| Correlati≠ | 5 | 2 |
| Sintesi≠ | Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis. | 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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