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Assessing Differential Item Functioning in Multiple Grouping Variables with Factorial Logistic Regression

  • Kuan-Yu Jin
  • Hui-Fang ChenEmail author
  • Wen-Chung Wang
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 89)

Abstract

Differential item functioning (DIF) can occur among multiple grouping variables (e.g., gender and ethnicity). For such cases, one can either examine DIF one grouping variable at a time or combine all the grouping variables into a single grouping variable in a test without a substantial meaning. These two approaches, analogous to one-way analysis of variance (ANOVA), are less efficient than an approach that considers all the grouping variables simultaneously and decomposes the DIF effect into main effects of individual grouping variables and their interactions, which is analogous to factorial ANOVA. In this study, the idea of factorial ANOVA was applied to the logistic regression method for the assessment of uniform and nonuniform DIF, and the performance of this approach was evaluated with simulations. The results indicated that the proposed factorial approach outperformed conventional approaches when there was interaction between grouping variables; the larger the DIF effect size, the higher the power of detection; the more DIF items in the anchored test, the worse the DIF assessment. Given the promising results, the factorial logistic regression method is recommended for the assessment of uniform and nonuniform DIF when there are multiple grouping variables.

Keywords

Differential item functioning Logistic regression Uniform differential item functioning Nonuniform differential item functioning 

Notes

Acknowledgment

The research was supported by the General Research Fund, Hong Kong Research Grants Council (No. 844110).

References

  1. Bolt D, Gierl MJ (2006) Testing features of graphical DIF: application of a regression correction to three nonparametric statistical tests. J Educ Meas 43:313–333. doi: 10.1111/j.1756-3984.2006.00019.x CrossRefGoogle Scholar
  2. Candell GL, Hulin CL (1986) Cross-language and cross-cultural comparisons in scale translations: independent sources of information about item nonequivalence. J Cross Cult Psychol 17:417–440. doi: 10.1177/0022002186017004003 CrossRefGoogle Scholar
  3. Chen H-F, Jin K-Y, Wang W-C (2012) Assessing differential item functioning when interactions among subgroups exist. Paper presented at the Taiwan education research association international conference on education, Kaohsiung, TaiwanGoogle Scholar
  4. DeMars CE (2010) Type I error inflation for detecting DIF in the presence of impact. Educ Psychol Meas 70:961–972. doi: 10.1177/0013164410366691 CrossRefGoogle Scholar
  5. Drasgow F (1987) Study of the measurement bias of two standardized psychological tests. J Appl Psychol 72:19–29. doi: 10.1037/0021-9010.72.1.19 CrossRefGoogle Scholar
  6. French BF, Maller SJ (2007) Iterative purification and effect size use with logistic regression for differential item functioning detection. Educ Psychol Meas 67:373–393CrossRefMathSciNetGoogle Scholar
  7. Güler N, Penfield RD (2009) A comparison of the logistic regression and contingency table methods for simultaneous detection of uniform and nonuniform DIF. J Educ Meas 46:314–329. doi: 10.1111/j.1745-3984.2009.00083.x CrossRefGoogle Scholar
  8. Iwata N, Turner RJ, Lloyd DA (2002) Race/ethnicity and depressive symptoms in community-dwelling young adults: a differential item functioning analysis. Psychiatry Res 110:281–289. doi: 10.1016/S0165-1781(02)00102-6 CrossRefGoogle Scholar
  9. Kim J, Oshima TC (2013) Effect of multiple testing adjustment in differential item functioning detection. Educ Psychol Meas 73:458–470. doi: 10.1177/0013164412467033 CrossRefGoogle Scholar
  10. Kim SH, Cohen AS, Park TH (1995) Detection of differential item functioning in multiple groups. J Educ Meas 32:261–276. doi: 10.1111/j.1745-3984.1995.tb00466.x CrossRefGoogle Scholar
  11. Li YJ, Brooks GP, Johanson GA (2012) Item discrimination and Type I error in the detection of differential item functioning. Educ Psychol Meas 72:847–861. doi: 10.1177/0013164411432333 CrossRefGoogle Scholar
  12. Narayanan P, Swaminathan H (1994) Performance of the Mantel–Haenszel and simultaneous item bias procedures for detecting differential item functioning. Appl Psychol Meas 18:315–328. doi: 10.1177/014662169401800403 CrossRefGoogle Scholar
  13. Narayanan P, Swaminathan H (1996) Identification of items that show nonuniform DIF. Appl Psychol Meas 20:257–274. doi: 10.1177/014662169602000306 CrossRefGoogle Scholar
  14. Penfield RD (2001) Assessing differential item functioning among multiple groups: a comparison of three Mantel–Haenszel procedures. Appl Meas Educ 14:235–259. doi: 10.1207/S15324818AME1403_3 CrossRefGoogle Scholar
  15. Rogers HJ, Swaminathan H (1993) A comparison of logistic regression and Mantel–Haenszel procedures for detecting differential item functioning. Appl Psychol Meas 17:105–116. doi: 10.1177/014662169301700201 CrossRefGoogle Scholar
  16. Roussos L, Stout W (1996) A multidimensionality-based DIF analysis paradigm. Appl Psychol Meas 20:355–371CrossRefGoogle Scholar
  17. Somes GW (1986) The generalized Mantel–Haenszel statistics. Am Stat 40:106–108. doi: 10.1080/00031305.1986.10475369 zbMATHGoogle Scholar
  18. Swaminathan H, Rogers HJ (1990) Detecting differential item functioning using logistic regression procedures. J Educ Meas 27:361–370. doi: 10.1111/j.1745-3984.1990.tb00754.x CrossRefGoogle Scholar
  19. Wang W-C (2000a) Modeling effects of differential item functioning in polytomous items. J Appl Meas 1:63–82Google Scholar
  20. Wang W-C (2000b) The simultaneous factorial analysis of differential item functioning. Methods Psychol Res 5:56–76Google Scholar
  21. Zwick R, Donoghue JR, Grima A (1993) Assessment of differential item functioning for performance tasks. J Educ Stat 15:185–187. doi: 10.1111/j.1745-3984.1993.tb00425.x Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Assessment Research Centre, Hong Kong Institute of EducationTai PoHong Kong SAR
  2. 2.Department of Applied Social SciencesCity University of Hong KongKowloonHong Kong SAR

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