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| Recerca Confirmatòria Assistida per Simulació× | Anàlisi Factorial Confirmatòria (CFA)× | |
|---|---|---|
| Camp≠ | Disseny de recerca | Psicometria |
| Família≠ | Process / pipeline | Latent structure |
| Any d'origen≠ | 1980s–2000s (widespread integration in behavioral and social sciences) | 1969 |
| Autor original≠ | No single originator; tradition formalized through Monte Carlo methods (Metropolis & Ulam, 1949) applied to confirmatory designs | Karl Gustav Jöreskog |
| Tipus≠ | Quantitative hybrid design | Hypothesis-testing latent variable model |
| Font seminal≠ | 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 ↗ |
| Àlies | simulation-based confirmatory design, Monte Carlo confirmatory research, computational confirmatory study, simulation-enhanced hypothesis testing | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Relacionats≠ | 5 | 4 |
| Resum≠ | 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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