• A PROBLEM WITH DISCRETIZING VALE–MAURELLI IN SIMULATION STUDIES 

      Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2019)
      Previous influential simulation studies investigate the effect of underlying non-normality in ordinal data using the Vale–Maurelli (VM) simulation method. We show that discretized data stemming from the VM method with a ...
    • Covariance Model Simulation Using Regular Vines 

      Grønneberg, Steffen; Foldnes, Njål (Journal article; Peer reviewed, 2017)
      We propose a new and flexible simulation method for non-normal data with user-specified marginal distributions, covariance matrix and certain bivariate dependencies. The VITA (VIne To Anything) method is based on regular ...
    • The dependence of chance-corrected weighted agreement coefficients on the power parameter of the weighting scheme: Analysis and measurement 

      Oest, Rutger Daniel van (Peer reviewed; Journal article, 2022)
      We consider the dependence of a broad class of chance-corrected weighted agreement coefficients on the weighting scheme that penalizes rater disagreements. The considered class encompasses many existing coefficients with ...
    • How general is the Vale-Maurelli simulation approach? 

      Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2015)
      The Vale-Maurelli (VM) approach to generating non-normal mul- tivariate data involves the use of Fleishman polynomials applied to an underly- ing Gaussian random vector. This method has been extensively used in Monte Carlo ...
    • On Identification and Non-normal Simulation in Ordinal Covariance and Item Response Models 

      Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2019)
      A standard approach for handling ordinal data in covariance analysis such as structural equation modeling is to assume that the data were produced by discretizing a multivariate normal vector. Recently, concern has been ...
    • Partial identification of latent correlations with binary data 

      Grønneberg, Steffen; Moss, Jonas; Foldnes, Njål (Journal article; Peer reviewed, 2020)
      The tetrachoric correlation is a popular measure of association for binary data and estimates the correlation of an underlying normal latent vector. However, when the underlying vector is not normal, the tetrachoric ...
    • Partial Identification of Latent Correlations with Ordinal Data 

      Moss, Jonas; Grønneberg, Steffen (Others, 2023)
      The polychoric correlation is a popular measure of association for ordinal data. It estimates a latent correlation, i.e., the correlation of a latent vector. This vector is assumed to be bivariate normal, an assumption ...