方法对比
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| 比较关系调查× | 多变量相关研究× | |
|---|---|---|
| 领域 | 研究设计 | 研究设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | Mid-20th century onward; systematized in educational research c. 1960s–1990s | 1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s |
| 提出者≠ | Rooted in survey methodology tradition; formalized by scholars such as Fraenkel, Wallen, and Creswell | Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others |
| 类型≠ | Quantitative non-experimental survey design | Non-experimental quantitative research design |
| 开创性文献≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill. ISBN: 978-0073525 670 | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 |
| 别名≠ | comparative correlational survey, multi-group relational survey, cross-group relational survey design | multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research |
| 相关≠ | 4 | 2 |
| 摘要≠ | A comparative relational survey is a quantitative, non-experimental design that examines the relationships among variables within a single study while simultaneously comparing those relationship patterns across two or more distinct groups. It extends a standard relational (correlational) survey by adding a comparative dimension, revealing whether associations observed in one group hold, differ, or even reverse in another. It is widely used in education, psychology, organizational behavior, and health sciences. | 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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