Item response theory.

Reise, Steven P.; Moore, Tyler M.

doi:10.1037/0000318-037

Cited by 4 publications

(3 citation statements)

References 73 publications

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“…3. It's crucial to examine the relationship between student abilities and item di culty levels within the framework of latent variables, a possibility afforded by the mathematical model system in IRT [10]. This IRT technique also aims to enhance accuracy in performance classi cation [11], [12].…”

Section: Resultsmentioning

confidence: 99%

Enhancing Biochemistry Assessment Quality in Medical Education Through Item Response Theory (IRT)

Baharuddin,

Handayani,

Rusli

2024

Preprint

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Background In medical education, particularly in biochemistry, crafting high-quality assessment questions is a primary challenge. Each item necessitates thorough evaluation, and precise identification of student abilities is crucial for maximally reflecting learning achievement. Objective This study aims to enhance assessment quality in biochemistry medical education by implementing Item Response Theory (IRT). This approach addresses Classical Test Theory (CTT) limitations. Recognizing the critical role of question quality in the learning process, the study investigates how IRT can more holistically and equitably assess student abilities. It includes a comparative analysis of student scores before and after IRT implementation. Methods Employing a mixed-method research approach, this study combines comparative quantitative analysis with qualitative ICC curve analysis in a pre-post experimental design. It focuses on biochemistry exam data from medical students (n = 89). IRT is used to measure the probability of student responses to questions, using parameters such as discrimination, difficulty level, and guessing probability. Jamovi software supports this analysis by accelerating computational processes. Results Significant improvements were observed in both question quality and student scores. Prior to IRT implementation, the average initial exam score was 56.1, which increased to 74.1 in the subsequent exam. The IRT evaluation indicated that the exam questions achieved a more effective differentiation between students of varying abilities. This improvement was evident from the increased person reliability and through Wright Map visualizations, which helped identify highly difficult questions via the Item Characteristic Curve (ICC). Conclusion The study advocates for integrating IRT as a standard method in biochemistry medical assessments. It highlights the necessity of assessments that are sensitive to individual student capabilities, providing more precise feedback for enhancing the quality of learning. These findings are crucial for evolving evaluation methodologies and advancing medical education standards.

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Section: Resultsmentioning

confidence: 99%

Enhancing Biochemistry Assessment Quality in Medical Education Through Item Response Theory (IRT)

Baharuddin,

Handayani,

Rusli

2024

Preprint

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“…Gessaroli and De Champlain's (1996) research found that identification of a second factor may increase with test length in dominance data fitting a 2-parameter logistic model. Drasgow and Hulin (1990) also noted that their unidimensional data appeared to be multidimensional with sample sizes of 750 (using modified parallel analysis) however this effect disappeared when the sample size increased to 5000. Therefore, the multidimensional finding for the 40-item clean datasets is likely due to sampling error and may be reduced with an increase in sample size.…”

Section: Discussionmentioning

confidence: 99%

The Effects of Aberrant Responding on Model-Fit Assuming Different Underlying Response Processes

Reimers,

Turner,

Tendeiro

et al. 2023

Applied Psychological Measurement

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Aberrant responding on tests and surveys has been shown to affect the psychometric properties of scales and the statistical analyses from the use of those scales in cumulative model contexts. This study extends prior research by comparing the effects of four types of aberrant responding on model fit in both cumulative and ideal point model contexts using graded partial credit (GPCM) and generalized graded unfolding (GGUM) models. When fitting models to data, model misfit can be both a function of misspecification and aberrant responding. Results demonstrate how varying levels of aberrant data can severely impact model fit for both cumulative and ideal point data. Specifically, longstring responses have a stronger impact on dimensionality for both ideal point and cumulative data, while random responding tends to have the most negative impact on data model fit according to information criteria (AIC, BIC). The results also indicate that ideal point data models such as GGUM may be able to fit cumulative data as well as the cumulative model itself (GPCM), whereas cumulative data models may not provide sufficient model fit for data simulated using an ideal point model.

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“…The basic idea in IRT models is that educational and psychological attributes (e.g., anxiety/stress, knowledge, attitude, etc.) are abstract latent entities that can be measured if they are elicited through devices called tests [2,3]. In fact, the responses of individuals to items of a test are observable manifestations of the hypothesized latent trait.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Kernel Smoothing Item Response Theory Analysis of Likert Items

Baghaei,

Effatpanah

2024

Psych

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Likert scales are the most common psychometric response scales in the social and behavioral sciences. Likert items are typically used to measure individuals’ attitudes, perceptions, knowledge, and behavioral changes. To analyze the psychometric properties of individual Likert-type items and overall Likert scales, mostly methods based on classical test theory (CTT) are used, including corrected item–total correlations and reliability indices. CTT methods heavily rely on the total scale scores, making it challenging to directly examine the performance of items and response options across varying levels of the trait. In this study, Kernel Smoothing Item Response Theory (KS-IRT) is introduced as a graphical nonparametric IRT approach for the evaluation of Likert items. Unlike parametric IRT models, nonparametric IRT models do not involve strong assumptions regarding the form of item response functions (IRFs). KS-IRT provides graphics for detecting peculiar patterns in items across different levels of a latent trait. Differential item functioning (DIF) can also be examined by applying KS-IRT. Using empirical data, we illustrate the application of KS-IRT to the examination of Likert items on a psychological scale.

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Item response theory.

Cited by 4 publications

References 73 publications

Enhancing Biochemistry Assessment Quality in Medical Education Through Item Response Theory (IRT)

Enhancing Biochemistry Assessment Quality in Medical Education Through Item Response Theory (IRT)

The Effects of Aberrant Responding on Model-Fit Assuming Different Underlying Response Processes

Nonparametric Kernel Smoothing Item Response Theory Analysis of Likert Items

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