TY  - JOUR
AU  - Camilleri, Julia
AU  - Eickhoff, S. B.
AU  - Weis, Susanne
AU  - Chen, Ji
AU  - Amunts, J.
AU  - Sotiras, A.
AU  - Genon, S.
TI  - A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System
JO  - Scientific reports
VL  - 11
IS  - 1
SN  - 2045-2322
CY  - [London]
PB  - Macmillan Publishers Limited, part of Springer Nature
M1  - FZJ-2021-03421
SP  - 16896
PY  - 2021
AB  - While a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis-Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.
LB  - PUB:(DE-HGF)16
C6  - pmid:34413412
UR  - <Go to ISI:>//WOS:000686768700076
DO  - DOI:10.1038/s41598-021-96342-3
UR  - https://juser.fz-juelich.de/record/894837
ER  -