Extracting Configurations of Values Mixing Scores From Experts and Ignoramus Using Bayesian Modeling

Abstract

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The article proposes a method for producing configurations of values in firms. Values have an impact in the long-term survival of businesses and guide managerial decision-making. The method produces cross-comparable latent rates of configurations of values. Data comes from a pool of 37 firms rated by both experts and ignoramus. By using Bayesian inference and Markov Chain Monte Carlo Methods the researcher can tune the expert rater bias. This generates robust estimates using a clear, overt and systematic procedure. The scores produced by the model are compared with a simple average of all raters.

Keywords

• values
• expert coding
• non-expert coding
• expert rater bias
• Bayesian inference