方法对比
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| 多元模型检验研究× | 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≠ | 1970s–1980s (multivariate model testing as a distinct approach) | 1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s |
| 提出者≠ | 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 |
| 类型≠ | Quantitative confirmatory research design | Non-experimental quantitative research design |
| 开创性文献 | 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 |
| 别名 | multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research | multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research |
| 相关≠ | 5 | 2 |
| 摘要≠ | 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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