A Machine Learning Approach to Assess Differential Item Functioning in Psychometric Questionnaires Using the Elastic Net Regularized Ordinal Logistic Regression in Small Sample Size Groups Article Swipe
Vahid Ebrahimi
,
Zahra Bagheri
,
Zahra Shayan
,
Peyman Jafari
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/6854477
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/6854477
Assessing differential item functioning (DIF) using the ordinal logistic regression (OLR) model highly depends on the asymptotic sampling distribution of the maximum likelihood (ML) estimators. The ML estimation method, which is often used to estimate the parameters of the OLR model for DIF detection, may be substantially biased with small samples. This study is aimed at proposing a new application of the elastic net regularized OLR model, as a special type of machine learning method, for assessing DIF between two groups with small samples. Accordingly, a simulation study was conducted to compare the powers and type I error rates of the regularized and nonregularized OLR models in detecting DIF under various conditions including moderate and severe magnitudes of DIF (DIF = 0.4 and 0.8), sample size ( N ), sample size ratio ( R ), scale length ( I ), and weighting parameter ( w ). The simulation results revealed that for I = 5 and regardless of R , the elastic net regularized OLR model with w = 0.1, as compared with the nonregularized OLR model, increased the power of detecting moderate uniform DIF (DIF = 0.4) approximately 35% and 21% for N = 100 and 150, respectively. Moreover, for I = 10 and severe uniform DIF (DIF = 0.8), the average power of the elastic net regularized OLR model with 0.03 ≤ w ≤ 0.06, as compared with the nonregularized OLR model, increased approximately 29.3% and 11.2% for N = 100 and 150, respectively. In these cases, the type I error rates of the regularized and nonregularized OLR models were below or close to the nominal level of 0.05. In general, this simulation study showed that the elastic net regularized OLR model outperformed the nonregularized OLR model especially in extremely small sample size groups. Furthermore, the present research provided a guideline and some recommendations for researchers who conduct DIF studies with small sample sizes.
Related Topics
Concepts
Statistics
Mathematics
Differential item functioning
Elastic net regularization
Estimator
Logistic regression
Sample size determination
Ordered logit
Regression
Item response theory
Psychometrics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2021/6854477
- https://downloads.hindawi.com/journals/bmri/2021/6854477.pdf
- OA Status
- hybrid
- Cited By
- 6
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200090019
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200090019Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2021/6854477Digital Object Identifier
- Title
-
A Machine Learning Approach to Assess Differential Item Functioning in Psychometric Questionnaires Using the Elastic Net Regularized Ordinal Logistic Regression in Small Sample Size GroupsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Vahid Ebrahimi, Zahra Bagheri, Zahra Shayan, Peyman JafariList of authors in order
- Landing page
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https://doi.org/10.1155/2021/6854477Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/bmri/2021/6854477.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/bmri/2021/6854477.pdfDirect OA link when available
- Concepts
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Statistics, Mathematics, Differential item functioning, Elastic net regularization, Estimator, Logistic regression, Sample size determination, Ordered logit, Regression, Item response theory, PsychometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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