Samples from Different Professional Occupations Yield Different Dimensions of National Culture
Auteur : Michael Minkov, Vesselin Ivanov Blagoev
Date de publication : 2014
Éditeur : SSRN
Nombre de pages : Non disponible
Résumé du livre
A very important issue in the analysis and use of the research results is about the validity of the data and then of course - the validity of the results based on the analysis of that data. From science point of view, we would not consider enough valid research data, which cannot be replicated in another similar research. From the business point of view, i.e. marketing research on the consumer behavior, we would not value high survey results that are obtained from a sample that does not coincide with the survey population. In this paper we analyze this issue from the point of view of the cross-cultural analysis where the data is often obtained based on convenience sampling, because there is no other, more scientific in the classical jargon, way of surveying the research population. For example, in cross-national studies of employees' values and beliefs, it is common practice to use mixed samples - for instance pools of middle managers or office workers with different job descriptions and from diverse companies. A number of such studies of imperfectly matched national samples, including that by Project GLOBE, have reported national indices for many countries. But what do these indices represent? Are they reliable measures of national culture as some of these studies claim? For instance, are the dimensions susceptible to replication? We analyzed two sets of value items - desirable values for children and personal values - from the latest wave of the nationally representative World Values Survey. We compared nation-level value structures from four types of samples in 46 countries: 1) nationally representative, 2) managers, 3) experts (without supervisory duties), and 4) skilled and semi-skilled workers. Following Shalom Schwartz's method, we started the analysis of each sample with multidimensional scaling (MDS), and then performed a factor analysis.