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| 다변량 설명 연구× | 다변량 상관 연구× | |
|---|---|---|
| 분야 | 연구설계 | 연구설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Mid-to-late 20th century (consolidated ~1960s–1980s) | 1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s |
| 창시자≠ | Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others | Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others |
| 유형≠ | Quantitative research design | Non-experimental quantitative research design |
| 원전≠ | 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 |
| 별칭 | multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER | multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research |
| 관련≠ | 4 | 2 |
| 요약≠ | 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. | 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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