Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel-based Confirmatory Research× | Apstiprinošā faktoru analīze (AFA)× | |
|---|---|---|
| Nozare≠ | Pētījuma dizains | Psihometrija |
| Saime≠ | Process / pipeline | Latent structure |
| Izcelsmes gads≠ | 1960s–1980s (formalization of panel methods with confirmatory inference) | 1969 |
| Autors≠ | Multiple contributors; panel data analysis formalized by Yair Mundlak, Zvi Griliches, and Edwin Kuh in the 1960s–1970s; confirmatory integration developed across econometrics and SEM traditions | Karl Gustav Jöreskog |
| Tips≠ | Quantitative longitudinal research design | Hypothesis-testing latent variable model |
| Pirmavots≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Citi nosaukumi | confirmatory panel design, longitudinal confirmatory study, panel confirmatory analysis, PBCR | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | Panel-based confirmatory research combines the longitudinal power of panel data — repeated observations of the same units over time — with a pre-specified, hypothesis-driven analytic framework. Instead of exploring patterns post-hoc, the researcher commits to theoretical propositions before data collection and uses the panel structure to test causal or directional claims while controlling for unobserved time-invariant confounders. It is widely used in economics, sociology, epidemiology, and organizational research. | 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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