arXiv subjects

Adéla Hladká

Publications and source records attributed to Adéla Hladká.

4 recordsLinked to original sources

arXiv2026

Refining Effect-Size Measures and Classification for Differential Item Functioning: Toward Unified Guidelines Across Methods

Differential Item Functioning (DIF) analysis is used to identify potentially biased items in multi-item measurements. In addition to testing the statistical significance, it is essential to evaluate the practical significance of DIF through effect-size measures. We review existing DIF effect-size measures and cut-off values used to classify the effect-size magnitudes for the Mantel-Haenszel test, SIBTEST, and model-based methods for binary items, and introduce a refinement of area-based effect-size measures. A simulation study is conducted to investigate the properties of these effect-size measures and existing classification guidelines, and to assess their comparative performance. The results indicate that some commonly used effect-size measures exhibit undesirable properties, including inconsistent classifications, systematic underestimation of the magnitude of the underlying DIF, and strong dependence on design factors. To address these issues, we introduce usage restrictions for some effect-size measures, revise cut-off values that unify results across different methods, and propose new cut-off values for area-based effect-size measures. The methods are demonstrated using two real data examples. Implementation is provided in the R software.

stat.ME↗

arXiv2025

A novel nonparametric framework for DIF detection using kernel-smoothed item response curves

This study introduces a novel nonparametric approach for detecting Differential Item Functioning (DIF) in binary items through direct comparison of Item Response Curves (IRCs). Building on prior work on nonparametric comparison of regression curves, we extend the methodology to accommodate binary response data, which is typical in psychometric applications. The proposed approach includes a new estimator of the asymptotic variance of the test statistic and derives optimal weight functions that maximise local power. Because the asymptotic distribution of the resulting test statistic is unknown, a wild bootstrap procedure is applied for inference. A Monte Carlo simulation study demonstrates that the nonparametric approach effectively controls Type I error and achieves power comparable to the traditional logistic regression method, outperforming it in cases with multiple intersections of the underlying IRCs. The impact of bandwidth and weight specification is explored. Application to a verbal aggression dataset further illustrates the method's ability to detect subtle DIF patterns missed by parametric models. Overall, the proposed nonparametric framework provides a flexible and powerful alternative for detecting DIF, particularly in complex scenarios where traditional model-based assumptions may not be applicable.

stat.ME↗

arXiv2025

Enhancing Psychometric Analysis with Interactive SIA Modules

ShinyItemAnalysis (SIA) is an R package and shiny application for an interactive presentation of psychometric methods and analysis of multi-item measurements in psychology, education, and social sciences in general. In this article, we present a new feature introduced in the recent version of the package, called "SIA modules", which allows researchers and practitioners to offer new analytical methods for broader use via add-on extensions. SIA modules are designed to integrate with and build upon the SIA interactive application, enabling them to leverage the existing infrastructure for tasks such as data uploading and processing. They can access and further use a range of outputs from various analyses, including models and datasets. Because SIA modules come in R packages (or extend the existing ones), they may come bundled with their datasets, use object-oriented systems, or even compiled code. We illustrate the concepts using sample modules from the newly introduced SIAmodules package and other packages. After providing a general overview of building Shiny applications, we describe how to develop the SIA add-on modules with the support of the new SIAtools package. Finally, we discuss possibilities of future development and emphasize the importance of freely available, interactive psychometric software for dissemination of methodological innovations.

cs.HC↗

arXiv2024

New iterative algorithms for estimation of item functioning

This paper explores innovations to parameter estimation in generalized linear and nonlinear models, which may be used in item response modeling to account for guessing/pretending or slipping/dissimulation and for the effect of covariates. We introduce a new implementation of the EM algorithm and propose a new algorithm based on the parametrized link function. The two novel iterative algorithms are compared to existing methods in a simulation study. Additionally, the study examines software implementation, including the specification of initial values for numerical algorithms and asymptotic properties with an estimation of standard errors. Overall, the newly proposed algorithm based on the parametrized link function outperforms other procedures, especially for small sample sizes. Moreover, the newly implemented EM algorithm provides additional information regarding respondents' inclination to guess or pretend and slip or dissimulate when answering the item. The study also discusses applications of the methods in the context of the detection of differential item functioning and addresses the measurement error. Methods are offered in the difNLR package and in the interactive application of the ShinyItemAnalysis package; demonstration is provided using real data from psychological and educational assessments.

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