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Imputing missing values in IB research using endpoint-heterogeneous beta regression models: An application to Hofstede's LTO measures

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  • Hahn, Eugene D.
  • Doh, Jonathan P.

Abstract

Addressing missing values in empirical research is a longstanding issue and challenge for scholars in all disciplines. In International Business, the challenge has typically been met with an array of strategies, ranging from listwise deletion to more sophisticated approaches, including regression-based imputation. Current models for addressing missing values where data is bounded have several limitations. We present a new model for imputing values for bounded data via endpoint-heterogeneous beta regression. We use this approach to generate imputed values for additional countries in Hofstede's long-term orientation (LTO) scale. LTO was the first widely used scale to be based on a non-Western value system, however, Hofstede was not able to collect LTO data for some countries that were part of his original survey and therefore the LTO measure has numerous missing values. Our paper introduces a novel approach to addressing missing values in LTO and provides a template for scholars facing similar challenges in a wide range of IB and other domains.

Suggested Citation

  • Hahn, Eugene D. & Doh, Jonathan P., 2025. "Imputing missing values in IB research using endpoint-heterogeneous beta regression models: An application to Hofstede's LTO measures," Journal of World Business, Elsevier, vol. 60(4).
    • Handle: RePEc:eee:worbus:v:60:y:2025:i:4:s1090951625000331
    DOI: 10.1016/j.jwb.2025.101644
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