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Detection of Gender-Biased Items in the Peabody Picture Vocabulary Test

1
Sciences Humaines, Lettres et Communication, Université Téluq, Quebec City, QC G1K 9H6, Canada
2
Département D’administration et Fondements de L’éducation, Université de Montréal, Montreal, QC H2V 2S9, Canada
3
Institute of English Studies, Jagiellonian University, 31-120 Krakow, Poland
*
Author to whom correspondence should be addressed.
Languages 2019, 4(2), 27; https://doi.org/10.3390/languages4020027
Received: 11 March 2019 / Revised: 12 April 2019 / Accepted: 24 April 2019 / Published: 5 May 2019
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PDF [2016 KB, uploaded 5 May 2019]
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Abstract

This study investigated possible gender bias on a vocabulary test, using a method suggested by Andrich and Hagquist to detect “real” differential item functioning (DIF). A total of 443 adult ESL learners completed all 228 items of the Peabody Picture Vocabulary Test (PPVT-IV). The 310 female and 133 male participants were assumed to be of equal competence, corresponding to levels B1 and B2 on the Common European Framework of Reference for Languages. Male participants outscored female participants, possibly due to the multiple-choice format and to the fact that most gender-biased questions favored men rather than women. Finally, our analysis process yielded only seven items out of 228 as showing gender DIF, which is much lower than the numbers reported in the literature for ESL tests. This low figure suggests that the high number of gender-related DIF items reported in previous research might be attributed to the use of DIF detecting methods that do not take into account artificial DIF stemming from the cross-contamination of test items. View Full-Text
Keywords: language testing; gender bias; differential item functioning; second language acquisition; Peabody Picture Vocabulary Test language testing; gender bias; differential item functioning; second language acquisition; Peabody Picture Vocabulary Test
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Pichette, F.; Béland, S.; Leśniewska, J. Detection of Gender-Biased Items in the Peabody Picture Vocabulary Test. Languages 2019, 4, 27.

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