方法对比
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| 模拟辅助验证性研究× | 验证性因子分析（CFA）× | |
|---|---|---|
| 领域≠ | 研究设计 | 心理测量学 |
| 方法族≠ | Process / pipeline | Latent structure |
| 起源年份≠ | 1980s–2000s (widespread integration in behavioral and social sciences) | 1969 |
| 提出者≠ | No single originator; tradition formalized through Monte Carlo methods (Metropolis & Ulam, 1949) applied to confirmatory designs | Karl Gustav Jöreskog |
| 类型≠ | Quantitative hybrid design | Hypothesis-testing latent variable model |
| 开创性文献≠ | Morey, R. D., Chambers, C. D., Aitken, M. R. F., Harris, C. R., Hoekstra, R., Lakens, D., Lewandowsky, S., Morey, C. C., Newman, D. P., Schonbrodt, F. D., Vanpaemel, W., Wagenmakers, E. J., & Zwaan, R. A. (2022). The Peer Reviewers' Openness Initiative: Incentivising open research practices through peer review. Royal Society Open Science, 3(1), 150547. link ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| 别名 | simulation-based confirmatory design, Monte Carlo confirmatory research, computational confirmatory study, simulation-enhanced hypothesis testing | CFA, confirmatory FA, measurement model, restricted factor analysis |
| 相关≠ | 5 | 4 |
| 摘要≠ | Simulation-assisted confirmatory research integrates computational simulation — most commonly Monte Carlo methods — into a hypothesis-driven, confirmatory study design. Before or alongside empirical data collection, the researcher runs simulated data under specified model assumptions to establish expected parameter distributions, verify statistical power, and anticipate the behavior of the chosen analysis. The empirical findings are then evaluated against those simulation-derived benchmarks, strengthening the evidential value of confirmatory conclusions. | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. |
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