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Abstract Header

Revisiting Item Semantics in Measurement: A New Perspective Using Modern Natural Language Processing Embedding Techniques

Guo, Feng
http://orcid.org/0000-0002-5054-1839
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1688076205153493

Abstract Details

2023, Doctor of Philosophy (Ph.D.), Bowling Green State University, Psychology/Industrial-Organizational.
Language understanding plays a crucial role in psychological measurement and so it is important that semantic cues should be studied for more effective and accurate measurement practices. With advancements in computer science, natural language processing (NLP) techniques have emerged as efficient methods for analyzing textual data and have been used to improve psychological measurement. This dissertation investigates the application of NLP embeddings to address fundamental methodological challenges in psychological measurement, specifically scale development and validation. In Study 1, a word embedding-based approach was used to develop a corporate personality measure, which resulted in a three-factor solution closely mirroring three dimensions out of the Big Five framework (i.e., Extraversion, Agreeableness, and Conscientiousness). This research furthers our conceptual understanding of corporate personality by identifying similarities and differences between human and organizational personality traits. In Study 2, the sentence-based embedding model was applied to predict empirical pairwise item response relationships, comparing its performance with human ratings. This study also demonstrated the effectiveness of fine-tuned NLP models for classifying item pair relationships into trivial/low or moderate/high empirical relationships, which provides preliminary validity evidence without collecting human responses. The research seeks to enhance psychological measurement practices by leveraging NLP techniques, fostering innovation and improved understanding in the field of social sciences.
Michael Zickar, Ph.D. (Committee Chair)
Neil Baird, Ph.D. (Other)
Richard Anderson, Ph.D. (Committee Member)
Samuel McAbee, Ph.D. (Committee Member)
137 p.
Psychological Tests; Psychology; Quantitative Psychology
measurement; psychometrics; natural language processing; NLP; machine learning; scale development; scale validation;

Recommended Citations

Citations

  • Guo, F. (2023). Revisiting Item Semantics in Measurement: A New Perspective Using Modern Natural Language Processing Embedding Techniques [Doctoral dissertation, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1688076205153493

    APA Style (7th edition)

  • Guo, Feng. Revisiting Item Semantics in Measurement: A New Perspective Using Modern Natural Language Processing Embedding Techniques. 2023. Bowling Green State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1688076205153493.

    MLA Style (8th edition)

  • Guo, Feng. "Revisiting Item Semantics in Measurement: A New Perspective Using Modern Natural Language Processing Embedding Techniques." Doctoral dissertation, Bowling Green State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1688076205153493

    Chicago Manual of Style (17th edition)

bgsu1688076205153493
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© 2023, all rights reserved.
This open access ETD is published by Bowling Green State University and OhioLINK.