قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| نموذج راش المتين× | تحليل العوامل التأكيدي القوي× | |
|---|---|---|
| المجال≠ | القياس النفسي | الإحصاء |
| العائلة | Latent structure | Latent structure |
| سنة النشأة≠ | 1982 | 1984–1994 |
| صاحب الطريقة≠ | Mislevy & Bock (robust ability estimation); broader robust IRT formalized through 1980s–2000s | Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator) |
| النوع≠ | Robust item calibration model | Confirmatory latent variable model with robust estimation |
| المصدر التأسيسي≠ | Strobl, C., Wickelmaier, F., & Zeileis, A. (2011). Accounting for individual differences in Bradley-Terry models by means of recursive partitioning. Journal of Educational and Behavioral Statistics, 36(2), 135–153. DOI ↗ | Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage. link ↗ |
| الأسماء البديلة | robust IRT Rasch, robust dichotomous Rasch, outlier-resistant Rasch model, robust item calibration | Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA |
| ذات صلة≠ | 5 | 6 |
| الملخص≠ | The robust Rasch model applies the standard one-parameter logistic Rasch framework with estimation procedures designed to limit the influence of outlying item responses, aberrant respondents, or mild model violations, producing stable item and person parameter estimates that are less sensitive to data contamination than ordinary maximum likelihood or conditional maximum likelihood Rasch estimation. | Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable. |
| ScholarGateمجموعة البيانات ↗ |
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