• covsim: An R Package for Simulating Non-normal Data for Structural Equation Models Using Copulas 

      Grønneberg, Steffen; Foldnes, Njål; Marcoulides, Katerina (Peer reviewed; Journal article, 2022)
      In factor analysis and structural equation modeling non-normal data simulation is traditionally performed by specifying univariate skewness and kurtosis together with the target covariance matrix. However, this leaves ...
    • Improved Goodness of Fit Procedures for Structural Equation Models 

      Foldnes, Njål; Moss, Jonas; Grønneberg, Steffen (Peer reviewed; Journal article, 2024)
      We propose new ways of robustifying goodness-of-fit tests for structural equation modeling under non-normality. These test statistics have limit distributions characterized by eigenvalues whose estimates are highly unstable ...
    • Non-normal Data Simulation using Piecewise Linear Transforms 

      Foldnes, Njål; Grønneberg, Steffen (Peer reviewed; Journal article, 2021)
      We present PLSIM, a new method for generating nonnormal data with a pre-specified covariance matrix that is based on coordinate-wise piecewise linear transformations of standard normal variables. In our presentation, the ...
    • School Entry Detection of Struggling Readers using Gameplay Data and Machine Learning 

      Foldnes, Njål; Uppstad, Per Henning; Grønneberg, Steffen; Thomson, Jenny (Peer reviewed; Journal article, 2024-10)
      Current methods for reading difficulty risk detection at school entry remain error-prone. We present a novel approach utilizing machine learning analysis of data from GraphoGame, a fun and pedagogical literacy app. The app ...