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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
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
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, Taiwan
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
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
French BF, Maller SJ (2007) Iterative purification and effect size use with logistic regression for differential item functioning detection. Educ Psychol Meas 67:373–393
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
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
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
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
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
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
Narayanan P, Swaminathan H (1996) Identification of items that show nonuniform DIF. Appl Psychol Meas 20:257–274. doi:10.1177/014662169602000306
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
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
Roussos L, Stout W (1996) A multidimensionality-based DIF analysis paradigm. Appl Psychol Meas 20:355–371
Somes GW (1986) The generalized Mantel–Haenszel statistics. Am Stat 40:106–108. doi:10.1080/00031305.1986.10475369
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
Wang W-C (2000a) Modeling effects of differential item functioning in polytomous items. J Appl Meas 1:63–82
Wang W-C (2000b) The simultaneous factorial analysis of differential item functioning. Methods Psychol Res 5:56–76
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
Acknowledgment
The research was supported by the General Research Fund, Hong Kong Research Grants Council (No. 844110).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Jin, KY., Chen, HF., Wang, WC. (2015). Assessing Differential Item Functioning in Multiple Grouping Variables with Factorial Logistic Regression. In: Millsap, R., Bolt, D., van der Ark, L., Wang, WC. (eds) Quantitative Psychology Research. Springer Proceedings in Mathematics & Statistics, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-319-07503-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-07503-7_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07502-0
Online ISBN: 978-3-319-07503-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)