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@ARTICLE{Camilleri:894837,
      author       = {Camilleri, Julia and Eickhoff, S. B. and Weis, Susanne and
                      Chen, Ji and Amunts, J. and Sotiras, A. and Genon, S.},
      title        = {{A} machine learning approach for the factorization of
                      psychometric data with application to the {D}elis {K}aplan
                      {E}xecutive {F}unction {S}ystem},
      journal      = {Scientific reports},
      volume       = {11},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2021-03421},
      pages        = {16896},
      year         = {2021},
      abstract     = {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.},
      cin          = {INM-7},
      ddc          = {600},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34413412},
      UT           = {WOS:000686768700076},
      doi          = {10.1038/s41598-021-96342-3},
      url          = {https://juser.fz-juelich.de/record/894837},
}