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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Shortening Psychological Scales: Semantic Similarity Matters.

Sevilay Kilmen^1,2, Okan Bulut¹

¹University of Alberta, Edmonton, Canada.

Educational and Psychological Measurement

|February 27, 2025

Summary

A new sentence embedding method offers a practical way to shorten scales without needing participant data. This approach matches traditional methods in accuracy and provides insights into item psychometric properties.

Keywords:

psychometrics scale abbreviation semantics sentence embedding

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Area of Science:

Psychometrics
Computational Linguistics

Background:

Traditional scale abbreviation requires large datasets, limiting its use.
Existing automatic methods often depend on response data.

Purpose of the Study:

Introduce a novel scale abbreviation method using sentence embeddings.
Compare this method against established data-driven techniques.
Evaluate the utility of semantic similarity for scale development.

Main Methods:

Developed a scale abbreviation technique based on sentence embeddings.
Leveraged semantic similarity among scale items.
Compared performance against two established automatic scale abbreviation methods.

Main Results:

The sentence embedding method showed comparable model fit, measurement accuracy, and ability estimates.
A negative correlation was found between item discrimination and semantic similarity.
Semantic features may predict psychometric properties, though not for reverse-scored items.

Conclusions:

Sentence embedding offers a viable, data-free alternative for scale abbreviation.
This method is particularly useful when large sample sizes are unavailable.
Semantic item features can inform psychometric properties of scales.