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| Monimuuttujainen selittävä tutkimus× | Selittävä tutkimus× | |
|---|---|---|
| Tieteenala | Tutkimusasetelma | Tutkimusasetelma |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | Mid-to-late 20th century (consolidated ~1960s–1980s) | 1960s–1980s (codified in behavioral and social science methodology) |
| Kehittäjä≠ | Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others | Formalized by Earl Babbie and Fred Kerlinger among others |
| Tyyppi≠ | Quantitative research design | Non-experimental quantitative research design |
| Alkuperäislähde≠ | Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 | Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417559 |
| Rinnakkaisnimet | multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER | analytical research, causal research, explanatory study, explanatory quantitative research |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | Multivariate explanatory research is a quantitative design that simultaneously examines multiple independent variables to explain variance in one or more outcomes. Rather than describing what exists or simply correlating pairs of variables, it seeks causal or structural explanations by testing theoretically grounded models with techniques such as multiple regression, MANOVA, or structural equation modeling on survey, administrative, or observational numeric data. | Explanatory research is a non-experimental quantitative research design that goes beyond describing a phenomenon to identifying why it occurs — examining the relationships or mechanisms that account for observed patterns. Rooted in positivist social science methodology, it uses theory-driven hypotheses and statistical analysis to test whether specific variables explain variation in an outcome, without necessarily manipulating those variables. |
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