Abstract
The semantic theory of survey responses (STSR) proposes that the prime source of statistical covariance in survey data is the degree of semantic similarity (overlap of meaning) among the items of t...
Highlights
IntroductionIs it possible to simulate and predict real survey responses before they happen?
Is it possible to simulate and predict real survey responses before they happen? And what would that tell us? The present article describes and tests a method to create artificial responses according to the semantic properties of the survey items based on the semantic theory of survey responses (STSR; Arnulf, Larsen, Martinsen, & Bong, 2014)
The alphas generated from random semantic responses are inadequate and keep deteriorating as items are replaced by simulated responses
Summary
Is it possible to simulate and predict real survey responses before they happen? The present article describes and tests a method to create artificial responses according to the semantic properties of the survey items based on the semantic theory of survey responses (STSR; Arnulf, Larsen, Martinsen, & Bong, 2014). According to STSR, the semantic relationships will shape the baseline of correlations among items. Such relationships are accessible a priori through the use of digital semantic algorithms. Survey responses should be predictable to the extent that their semantic relationships are fixed. The present study seeks to develop such a method and apply it to a well-known leadership questionnaire, the Multifactor Leadership Questionnaire (MLQ; Avolio, Bass, & Jung, 1995). Thereafter, we briefly show how it performs using a different measurement scale
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