@inproceedings{cho-etal-2026-pre,
title = "Pre-Data Embedding Diagnostics for Construct Validity in Multi-Domain Instruments",
author = "Cho, Youngmi and
Wu, Tong and
Oh, Hyeonjoo",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting-Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Full Papers",
month = oct,
year = "2026",
address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
publisher = "National Council on Measurement in Education (NCME)",
url = "https://aclanthology.org/2026.aimecon-main.62/",
pages = "551--559",
ISBN = "979-8-9983004-0-0",
abstract = "Centroid margin, an embedding-based measure of relative domain distinctiveness, was evaluated as a pre-data item-selection tool for multi-domain instruments. Across four samples, centroid-guided selection improved model fit and discriminant validity, whereas own-domain cosine similarity increased overlap among closely related domains, revealing a key limitation of absolute similarity metrics."
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%0 Conference Proceedings
%T Pre-Data Embedding Diagnostics for Construct Validity in Multi-Domain Instruments
%A Cho, Youngmi
%A Wu, Tong
%A Oh, Hyeonjoo
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F cho-etal-2026-pre
%X Centroid margin, an embedding-based measure of relative domain distinctiveness, was evaluated as a pre-data item-selection tool for multi-domain instruments. Across four samples, centroid-guided selection improved model fit and discriminant validity, whereas own-domain cosine similarity increased overlap among closely related domains, revealing a key limitation of absolute similarity metrics.
%U https://aclanthology.org/2026.aimecon-main.62/
%P 551-559
Markdown (Informal)
[Pre-Data Embedding Diagnostics for Construct Validity in Multi-Domain Instruments](https://aclanthology.org/2026.aimecon-main.62/) (Cho et al., AIME-Con 2026)
ACL