S
Sven Hilbert
Researcher at University of Regensburg
Publications - 52
Citations - 1139
Sven Hilbert is an academic researcher from University of Regensburg. The author has contributed to research in topics: Cognition & Working memory. The author has an hindex of 11, co-authored 46 publications receiving 756 citations. Previous affiliations of Sven Hilbert include Ludwig Maximilian University of Munich.
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Journal ArticleDOI
Masking misfit in confirmatory factor analysis by increasing unique variances: a cautionary note on the usefulness of cutoff values of fit indices.
TL;DR: Using data simulation, the authors illustrate how the value of the chi-square test, the root-mean-square error of approximation, and the standardized root- mean-square residual are decreased when unique variances are increased although model misspecification is present.
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Beyond green: Broad support for biodiversity in multicultural European cities
Leonie K. Fischer,Jasmin Honold,Rozalija Cvejić,Tim Delshammar,Sven Hilbert,Sven Hilbert,Raffaele Lafortezza,Raffaele Lafortezza,Mojca Nastran,Anders Busse Nielsen,Anders Busse Nielsen,Marina Pintar,Alexander P.N. van der Jagt,Ingo Kowarik +13 more
TL;DR: In this article, a field survey revealed that biodiversity matters: people largely prefer higher plant species richness in urban greenspaces (i.e., parks, wastelands, streetscapes).
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Personality traits predict smartphone usage.
Clemens Stachl,Sven Hilbert,Sven Hilbert,Jiew–Quay Au,Daniel Buschek,Alexander De Luca,Bernd Bischl,Heinrich Hussmann,Markus Bühner +8 more
TL;DR: In this paper, individual differences can predict frequency and duration of actual behavior, manifested in mobile application usage on smartphones, and individual differences were investigated to what degree individual differences could predict actual behavior.
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Sensitivity of SEM Fit Indexes with Respect to Violations of Uncorrelated Errors.
TL;DR: In this article, the sensitivity of commonly used cutoff values for global-model-fit indexes, with regard to different degrees of violations of the assumption of uncorrelated errors in confirmatory factor analysis, was investigated.
Journal ArticleDOI
Personality Research and Assessment in the Era of Machine Learning
Clemens Stachl,Clemens Stachl,Florian Pargent,Sven Hilbert,Gabriella M. Harari,Ramona Schoedel,Sumer S. Vaid,Samuel D. Gosling,Samuel D. Gosling,Markus Bühner +9 more
TL;DR: The main challenges that researchers face when building, interpreting, and validating machine learning models are illustrated and some key issues that arise from the use of latent variables in the modelling process are highlighted.