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| Analisis Fungsi Item Bayesian (Bayesian DIF)× | Fungsi Butiran Berbeza Berbilang Kumpulan (MG-DIF)× | |
|---|---|---|
| Bidang | Psikometrik | Psikometrik |
| Keluarga | Latent structure | Latent structure |
| Tahun asal≠ | 1990s–2000s | 1980s-1990s |
| Pengasas≠ | H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s | Shealy & Stout (SIBTEST framework); Lord (IRT-based DIF) |
| Jenis≠ | Item bias detection / Bayesian inference | Measurement bias detection |
| Sumber perintis≠ | Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370. DOI ↗ | Millsap, R. E. (2012). Statistical Approaches to Measurement Invariance. Routledge. ISBN: 978-1848728936 |
| Alias | Bayesian DIF, Bayesian DIF analysis, Bayesian item bias detection, BDIF | MG-DIF, multi-group DIF, differential item functioning across groups, multiple-group DIF analysis |
| Berkaitan≠ | 5 | 6 |
| Ringkasan≠ | Bayesian differential item functioning analysis detects whether a test item behaves differently across demographic or cultural groups — such as males vs. females — after accounting for the underlying ability or trait being measured. It applies Bayesian IRT estimation to obtain posterior distributions of item parameters separately per group, then evaluates group differences with posterior credibility intervals or Bayes factors rather than classical p-values. | Multi-group differential item functioning examines whether test or scale items function equivalently across three or more distinct groups — such as gender, ethnicity, or country — after matching respondents on the underlying trait being measured. Items that behave differently across groups threaten fair measurement and valid score comparisons. |
| ScholarGateSet data ↗ |
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