Cross-Cultural Analysis is the sequel to Culture's Consequences, the classic work published by Geert Hofstede, one of the most influential management thinkers in today's times. Hofstede's original work introduced a new research paradigm in cross-cultural analysis: studying cultural differences through nation-level dimensions (complex variables defined by intercorrelated items). This paradigm has been subsequently used by hundreds of prominent scholars all over the world and has produced solid results.
This new text takes the next step: It critically examines in one comprehensive volume the current, prevalent approaches to cross-cultural analysis at the level of nations that have been developed since Hofstede's work, offering students and researchers the theoretical and practical advantages and potential pitfalls of each method.
The book is structured into four distinct parts. Parts I and II focus on the main theoretical and statistical issues in cross-cultural analysis using Hofstede's approach and the different research methods now associated with it. Part II consists of presentations of all well-known (and some lesser known) large-scale cross-cultural studies since Hofstede's work that have explained cross-cultural variation in terms of dimensional models. Part III summarizes the main conclusions to be drawn from the presentations in Part II and I explains how the proposed models have contributed to our practical understanding of cross-cultural diversity.
Chapter 8: Data Analysis
Sociologists and most other social scientists regard the establishment of generalizations or “laws,” i.e. verified statements of correlations between phenomena, as their primary aim.
Subjective constructions are the essence of art, but do they also occur in science? Do scientists see vastly different things when they look at the same constellation of data?
This chapter discusses various issues associated with the analysis of cross-cultural data, mostly at the societal level. It defends a relativist position. Although it is reasonable to adhere to some generally accepted conventions, one should not believe in absolute rules in data analysis because they cannot have an objective foundation. Hence, the selection ...