Language
English
Publication Date
7-22-2026
Journal
Behavior Research Methods
DOI
10.3758/s13428-026-03102-0
PMID
42487077
PMCID
PMC13391806
PubMedCentral® Posted Date
7-22-2026
PubMedCentral® Full Text Version
Post-print
Abstract
Traditional profile analyses summarize multivariate person data with overall mean levels and relative patterns, but existing methods often blur these sources of variation or reduce each individual to a single best-fitting profile. 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-person cosines that index variable-by-variable alignment, and plane-fit correlations that summarize how closely an individual's pattern follows the plane's 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-Johnson 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.
Keywords
Data Interpretation, Statistical, Biplot, Ipsatization, SVD/PCA, Person-centered profiling, Cosine similarity, Multidimensional profiles
Published Open-Access
yes
Recommended Citation
Se-Kang Kim and Joe Grochowalski, "Segmented Profile Analysis (Sepa): Plane-Wise Decomposition of Within-Person Variation via Ipsatized Singular Value Decomposition" (2026). Faculty, Staff and Students Publications. 7746.
https://digitalcommons.library.tmc.edu/baylor_docs/7746