Using Log-Linear Smoothing to Improve Small-Sample DIF Estimation DIF
- Author(s):
- Puhan, Gautam; Moses, Tim P.; Yu, Lei; Dorans, Neil J.
- Publication Year:
- 2009
- Source:
- Journal of Educational Measurement, v46 n1 p59-83, Spr 2009
- Document Type:
- Article
- Page Count:
- 25
- Subject/Key Words:
- Differential Item Functioning (DIF), Smoothing (Numerical analysis), Loglinear Models, SIBTEST
Abstract
This study examined the extent to which log-linear smoothing could improve the accuracy of differential item functioning (DIF) estimates in small samples of examinees. Examinee responses from a certification test were analyzed using White examinees in the reference group and African American examinees in the focal group. Using a simulation approach, separate DIF estimates for seven small-sample-size conditions were obtained using unsmoothed (U) and smoothed (S) score distributions. These small sample U and S DIF estimates were compared to a criterion (i.e., DIF estimates obtained using the unsmoothed total data) to assess their degree of variability (random error) and accuracy (bias). Results indicate that for most studied items smoothing the raw score distributions reduced random error and bias of the DIF estimates, especially in the small-sample-size conditions. Implications of these results for operational testing programs are discussed.