{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T08:55:27Z","timestamp":1791276927724,"version":"4.1.0"},"reference-count":41,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Psychometrika"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Self-report scales are central to psychological and behavioral science, yet most analyses assume a single response process. In practice, respondents may follow cognitively distinct processes to answer the same item, and a respondent may switch processes across items. Ignoring between-person and\/or within-person heterogeneity in item response process may bias item parameter estimates and person scores. In this study, we introduce a response-level mixture model, namely, MIX-R. The proposed model represents two or more distinct response processes using item response trees and specifies the probability that a specific process is used with a prevalence model, which includes additive person and item effects and optional covariates. We fit MIX-R in a fully Bayesian framework. Applied to three-category items from the verbal aggression questionnaire, MIX-R reveals distinct process-specific item functioning so that expected scores of the same item vary across response processes. As shown in our simulation studies, MIX-R exhibits better model fit when it is consistent with the data-generating process and does not overfit. In addition, it accurately recovers person-level and item-level process prevalence and item parameters. We conclude this study by discussing its limitations and outlining directions for future research.<\/jats:p>","DOI":"10.1017\/psy.2026.10146","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T08:44:15Z","timestamp":1791276255000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["Investigating Within-Person Variation in Response Processes for Likert-Scale Data with a Response-Level Mixture IRTree Model"],"prefix":"10.1017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8511-7165","authenticated-orcid":false,"given":"Jinwen","family":"Luo","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01kq0pv72","id-type":"ROR","asserted-by":"publisher"}],"name":"South China Normal 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Published by Cambridge University Press on behalf of Psychometric Society","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https:\/\/creativecommons.org\/licenses\/by\/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}