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| 다변량 상관 연구× | 경로 분석(Path Analysis)× | |
|---|---|---|
| 분야≠ | 연구설계 | 통계학 |
| 계열≠ | Process / pipeline | Latent structure |
| 기원 연도≠ | 1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s | 1921 |
| 창시자≠ | Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others | Sewall Wright |
| 유형≠ | Non-experimental quantitative research design | Causal / mediation model |
| 원전≠ | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 | Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585. link ↗ |
| 별칭 | multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research | PA, path coefficient analysis, observed-variable SEM, causal path modeling |
| 관련≠ | 2 | 5 |
| 요약≠ | 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. | Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference. |
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