Abstract
Likert scale surveys are frequently used in cross-cultural studies on leadership. Recent publications using digital text algorithms raise doubt about the source of variation in statistics from such studies to the extent that they are semantically driven. The Semantic Theory of Survey Response (STSR) predicts that in the case of semantically determined answers, the response patterns may also be predictable across languages. The Multifactor Leadership Questionnaire (MLQ) was applied to 11 different ethnic samples in English, Norwegian, German, Urdu and Chinese. Semantic algorithms predicted responses significantly across all conditions, although to varying degree. Comparisons of Norwegian, German, Urdu and Chinese samples in native versus English language versions suggest that observed differences are not culturally dependent but caused by different translations and understanding. The maximum variance attributable to culture was a 5% unique overlap of variation in the two Chinese samples. These findings question the capability of traditional surveys to detect cultural differences. It also indicates that cross-cultural leadership research may risk lack of practical relevance.
Highlights
A simple search for “cross-cultural leadership” through ISI Web of Science returns around 500 hits at the time this is written
An unintended but striking finding in one of these studies was that the semantic patterns computed in English were highly predictive of survey patterns in a Norwegian sample, which raises an important question: If the statistical patterns in survey data are predictable across languages and cultures a priori, will such semantically driven surveys detect or neglect cultural differences?
This study explores the extent to which cross-national response patterns to a leadership survey are predictable a priori through digital semantic algorithms
Summary
A simple search for “cross-cultural leadership” through ISI Web of Science returns around 500 hits at the time this is written. A recent methodological development has evolved that sheds a different light on the nature of such data. An unintended but striking finding in one of these studies was that the semantic patterns computed in English were highly predictive of survey patterns in a Norwegian sample, which raises an important question: If the statistical patterns in survey data are predictable across languages and cultures a priori, will such semantically driven surveys detect or neglect cultural differences?. The main tenet of STSR is that responses to survey items will correlate if the items share overlapping meanings. While this has been known and even intended to ensure consistency within
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