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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Item Classification by Difficulty Using Functional Principal Component Clustering and Neural Networks.

James Zoucha¹, Igor Himelfarb², Nai-En Tang²

¹University of Northern Colorado, Greeley, CO, USA.

Educational and Psychological Measurement

|January 6, 2025

Summary

This study introduces a functional data analysis (FDA) method for classifying test item difficulty, offering a consistent alternative to visual inspection. The approach accurately categorizes items, improving fairness in examinee classification.

Keywords:

functional principal component clustering item classification neural networks

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Area of Science:

Psychometrics
Statistical Modeling
Educational Measurement

Background:

Consistent item difficulty is vital for fair examinee classification.
Subjective visual inspection can lead to inconsistencies in item difficulty categorization.

Purpose of the Study:

To present a practical procedure for classifying test items based on difficulty levels using functional data analysis (FDA).
To offer an empirical and consistent method for item classification, enhancing test fairness.

Main Methods:

Clustering item characteristic curves (ICCs) into difficulty groups using functional principal components (FPCs).
Employing a neural network to predict item difficulty based on ICCs.
Comparing FDA-based classification with traditional visual inspection.

Main Results:

Most discrepancies between visual and FDA classification differed by only one adjacent difficulty level.
FDA categorized 67% of medium to hard items into higher difficulty levels.
A neural network achieved 79.6% accuracy in predicting item difficulty.
Misclassifications by the neural network also differed by only one adjacent level compared to FDA clustering.

Conclusions:

The FDA method provides an efficient and practical procedure for classifying test items by difficulty.
This empirical approach enhances consistency and fairness in educational testing programs.
The method is particularly beneficial for testing programs with dispersed examinee populations and varied testing schedules.