Abstract
Stochastic simulation is a significant aspect of statistical research. Model building, parameter estimation, hypothesis tests, and other statistical tools for data analysis require verification to assess their validity and reliability, typically via simulated data. In many research fields, data sets often include ordinal variables, e.g. measured on a Likert scale, or count variables. In this work, we present and discuss a simulation method for generating ordinal and discrete random variables, whose marginal distributions and correlation matrix (expressed in terms of Pearson or Spearman’s pairwise correlations) are assigned by the user.
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Barbiero, A., Ferrari, P.A. (2014). Simulating Correlated Ordinal and Discrete Variables with Assigned Marginal Distributions. In: Melas, V., Mignani, S., Monari, P., Salmaso, L. (eds) Topics in Statistical Simulation. Springer Proceedings in Mathematics & Statistics, vol 114. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2104-1_4
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DOI: https://doi.org/10.1007/978-1-4939-2104-1_4
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