{"title":"Shortening Psychological Scales: Semantic Similarity Matters.","authors":"Sevilay Kilmen, Okan Bulut","doi":"10.1177/00131644251319047","DOIUrl":null,"url":null,"abstract":"<p><p>In this study, we proposed a novel scale abbreviation method based on sentence embeddings and compared it to two established automatic scale abbreviation techniques. Scale abbreviation methods typically rely on administering the full scale to a large representative sample, which is often impractical in certain settings. Our approach leverages the semantic similarity among the items to select abbreviated versions of scales without requiring response data, offering a practical alternative for scale development. We found that the sentence embedding method performs comparably to the data-driven scale abbreviation approaches in terms of model fit, measurement accuracy, and ability estimates. In addition, our results reveal a moderate negative correlation between item discrimination parameters and semantic similarity indices, suggesting that semantically unique items may result in a higher discrimination power. This supports the notion that semantic features can be predictive of psychometric properties. However, this relationship was not observed for reverse-scored items, which may require further investigation. Overall, our findings suggest that the sentence embedding approach offers a promising solution for scale abbreviation, particularly in situations where large sample sizes are unavailable, and may eventually serve as an alternative to traditional data-driven methods.</p>","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":" ","pages":"00131644251319047"},"PeriodicalIF":2.1000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851598/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational and Psychological Measurement","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/00131644251319047","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, we proposed a novel scale abbreviation method based on sentence embeddings and compared it to two established automatic scale abbreviation techniques. Scale abbreviation methods typically rely on administering the full scale to a large representative sample, which is often impractical in certain settings. Our approach leverages the semantic similarity among the items to select abbreviated versions of scales without requiring response data, offering a practical alternative for scale development. We found that the sentence embedding method performs comparably to the data-driven scale abbreviation approaches in terms of model fit, measurement accuracy, and ability estimates. In addition, our results reveal a moderate negative correlation between item discrimination parameters and semantic similarity indices, suggesting that semantically unique items may result in a higher discrimination power. This supports the notion that semantic features can be predictive of psychometric properties. However, this relationship was not observed for reverse-scored items, which may require further investigation. Overall, our findings suggest that the sentence embedding approach offers a promising solution for scale abbreviation, particularly in situations where large sample sizes are unavailable, and may eventually serve as an alternative to traditional data-driven methods.
期刊介绍:
Educational and Psychological Measurement (EPM) publishes referred scholarly work from all academic disciplines interested in the study of measurement theory, problems, and issues. Theoretical articles address new developments and techniques, and applied articles deal with innovation applications.