Differential Item Functioning: A Mixture Distribution Conceptualization

Differential item functioning (DIF) may be defined as an item that displays different statistical properties for different groups after matching the groups on an ability measure. For instance, with binary data, DIF exists when there is a difference in the conditional probabilities of a correct response for two manifest groups. This article argues that the occurrence of DIF can be explained by recognizing that the observed data do not reflect a homogeneous population of individuals, but are a mixture of data from multiple latent populations or classes. This conceptualization of DIF hypothesizes that when one observes DIF using the current conceptualization of DIF it is only to the degree that the manifest groups are represented in the latent classes in different proportions. A Monte Carlo study was conducted to compare various approaches to detecting DIF under this formulation of DIF. Results showed that as the latent class proportions became more equal the DIF detection methods identification rates approa...

[1]  Gregory Camilli,et al.  A Conceptual Analysis of Differential Item Functioning in Terms of a Multidimensional Item Response Model , 1992 .

[2]  R. Zwick,et al.  Assessment of Differential Item Functioning for Performance Tasks , 1993 .

[3]  Jürgen Rost,et al.  Rasch Models in Latent Classes: An Integration of Two Approaches to Item Analysis , 1990 .

[4]  Howard Wainer,et al.  Detection of differential item functioning using the parameters of item response models. , 1993 .

[5]  Frank B. Baker.  EQUATE 2.0: A Computer Program for the Characteristic Curve Method of IRT Equating , 1993 .

[6]  Dorothy T. Thayer,et al.  DIFFERENTIAL ITEM FUNCTIONING AND THE MANTEL‐HAENSZEL PROCEDURE , 1986 .

[7]  P. Holland,et al.  DIF DETECTION AND DESCRIPTION: MANTEL‐HAENSZEL AND STANDARDIZATION1,2 , 1992 .

[8]  Empirical Comparison of Selected Item Bias Detection Procedures with Bias Manipulation. , 1984 .

[9]  Terry A. Ackerman.  A Didactic Explanation of Item Bias, Item Impact, and Item Validity from a Multidimensional Perspective , 1992 .

[10]  H. Swaminathan,et al.  Detecting Differential Item Functioning Using Logistic Regression Procedures , 1990 .

[11]  Robert J. Mislevy,et al.  BILOG 3 : item analysis and test scoring with binary logistic models , 1990 .

[12]  Martha L. Stocking,et al.  Developing a Common Metric in Item Response Theory , 1983 .

[13]  Michael J. Zieky,et al.  Practical questions in the use of DIF statistics in test development. , 1993 .

[14]  Nambury S. Raju,et al.  Determining the Significance of Estimated Signed and Unsigned Areas Between Two Item Response Functions , 1990 .

[15]  Jürgen Rost,et al.  A logistic mixture distribution model for polychotomous item responses , 1991 .

[16]  Allan S. Cohen,et al.  IRTDIF: A Computer Program for IRT Differential Item Functioning Analysis , 1992 .

[17]  Robert J. Mislevy,et al.  Modeling item responses when different subjects employ different solution strategies , 1990 .

[18]  Terry A. Ackerman.  Developments in Multidimensional Item Response Theory , 1996 .

[19]  M. Reckase.  The Past and Future of Multidimensional Item Response Theory , 1997 .

[20]  George B. Macready,et al.  The Use of Loglinear Models for Assessing Differential Item Functioning Across Manifest and Latent Examinee Groups , 1990 .

[21]  Nambury S. Raju,et al.  The area between two item characteristic curves , 1988 .

[22]  L. Shepard,et al.  The Inadequacy of ANOVA for Detecting Test Bias , 1987 .

[23]  W. H. Angoff,et al.  Perspectives on differential item functioning methodology. , 1993 .

[24]  Howard Wainer,et al.  Use of item response theory in the study of group differences in trace lines. , 1988 .