Examination of Gender-Related Differential Item Functioning Through Poly-BW Indices
Tsai-Wei Huang1, Pei-Chen Wu2, Magdalena Mo Ching Mok3,4
1Department of Counseling, National Chiayi University, Chiayi, Taiwan.
Frontiers in Psychology
|March 14, 2022
Summary
This study introduces Poly-BW indices to identify gender-related differential item functioning (DIF) in math tests. Defenselessness and power indices accurately predict DIF, guiding item modification for fairer assessments.
Area of Science:
- Educational Measurement
- Psychometrics
- Item Response Theory
Background:
- Traditional differential item functioning (DIF) detection methods focus on item difficulty and discrimination, limiting understanding of DIF item characteristics.
- Minimizing DIF is crucial in test construction, yet teachers and practitioners lack insights into DIF item associates.
- Existing DIF detection methods often lead to items being deleted or ignored, potentially losing valuable information.
Purpose of the Study:
- To investigate the contribution of Poly-BW indices (power, defenselessness, disturbance, hint) to understanding gender-related DIF in a mathematics achievement test.
- To provide teachers and testing practitioners with practical information on DIF item associates.
- To explore the predictive accuracy of Poly-BW indices for DIF measures.
Main Methods:
- Utilized data from a 34-item teacher-made mathematics achievement test administered to 1,439 seventh-grade students in Taiwan.
- Employed the Poly Simultaneous Item Bias Test (Poly-SIBTEST) procedure to estimate DIF measures.
- Calculated and analyzed Poly-BW indices, specifically defenselessness (mp) and power (cp), comparing male and female student groups.
Main Results:
- Differences in defenselessness (mp) and power (cp) indices between genders significantly predicted DIF measures obtained via Poly-SIBTEST.
- The Poly-BW indices demonstrated satisfactory accuracy in predicting DIF.
- Items with higher defenselessness for males indicated male-favoring DIF, while items with higher power for males suggested female-favoring DIF.
Conclusions:
- Poly-BW indices, particularly defenselessness and power, offer valuable insights into the nature of gender-related DIF.
- These indices provide practical directions for modifying test items to reduce bias.
- The findings support the use of Poly-BW indices as a tool for enhancing fairness in educational assessments.
Related Concept Videos
Friedman Two-way Analysis of Variance by Ranks
319
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
319
Two-Way ANOVA
2.8K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.8K
Test for Homogeneity
2.1K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.1K
Bonferroni Test
2.9K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.9K
One-Way ANOVA: Unequal Sample Sizes
6.0K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.0K
One-Way ANOVA: Equal Sample Sizes
3.5K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.5K


