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Segmented Profile Analysis (SEPA) offers a unified, ipsatized singular-value decomposition (SVD) framework that decomposes individual profiles into orthogonal level (LE) and pattern (PE) effects and, crucially, introduces plane-wise segment profiles (summaries of each person's response pattern within each variable-contrast dimension) as primary person-oriented objects. Within each low-dimensional plane, SEPA defines a projected response pattern (segment profile), domain\u2013person cosines that index variable-by-variable alignment, and plane-fit correlations that summarize how closely an individual\u2019s pattern follows the plane\u2019s domain structure. Across planes, SEPA aggregates information via singular-value weighting to yield an overall segment profile while preserving contrast-specific signal. A practical workflow combines ipsatized SVD, parallel analysis for PE dimensionality, marker-domain rules, bootstrap confidence intervals, and subspace-stability diagnostics. Using multivariate cognitive data from the Woodcock\u2013Johnson IV, SEPA identifies interpretable marker domains, reveals distinct pattern facets across planes, and provides person-oriented indices that can be carried into standard regression models. Simulation studies examine the stability of segment profiles and cosines under varying sample sizes and variance structures. SEPA thus supplies a reproducible, geometry-based foundation for person-centered measurement that connects Q-type factor analysis, biplot methods, and contemporary within-person profiling in multidomain assessments.<\/jats:p>","DOI":"10.3758\/s13428-026-03102-0","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T21:56:26Z","timestamp":1784757386000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Segmented profile analysis (SEPA): Plane-wise decomposition of within-person variation via ipsatized singular value decomposition"],"prefix":"10.3758","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0928-3396","authenticated-orcid":false,"given":"Se-Kang","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9617-2346","authenticated-orcid":false,"given":"Joe","family":"Grochowalski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"3102_CR1","unstructured":"Asparouhov, T. & Muth\u00e9n, B. 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All scientific content, analyses, and conclusions are solely those of the authors.","order":5,"name":"Ethics","label":"Disclosure of artificial intelligence-generated content (AIGC) tools","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Analysis code is provided in the Supplementary Materials, submitted alongside the manuscript. The data set used in this study is proprietary but may be made available upon reasonable request. None of the analyses were preregistered. Additionally, SEPA is freely available as an open-source R package on the Comprehensive R Archive Network (CRAN;\n                      \n                      ) and on GitHub (\n                      \n                      ), and as a Python implementation on the Python Package Index (PyPI;\n                      \n                      ).","order":6,"name":"Ethics","label":"Open practices statement","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"246"}}