Cross-cultural research is burgeoning. Behavioral and social sciences such as psychology, sociology, management, marketing, and political science witness a steady increase in cross-cultural studies. For example, during the last decades, there has been a consistently increasing number of psychological studies on cross-cultural similarities and differences (Boer, Hanke, & He, 2018; Smith, Harb, Lonner, & Van de Vijver, 2001; Van de Vijver & Lonner, 1995). The increased interest is undoubtedly inspired by various factors, such as the opening of previously sealed international borders, large migration streams, globalization of the economic market, international tourism, increased cross-cultural communications, and technological innovations such as new means of telecommunication.

In the previous chapters, typical problems and pitfalls of cross-cultural research were discussed and solutions proposed. The current chapter briefly integrates the major methodological issues into eight statements. Each statement is followed by an explanation. The last section is devoted to our view on the future of cross-cultural research.

This chapter contains a description of the sampling of cultures, design, data analysis, and major strengths and weaknesses of the eight types of cross-cultural studies described in Chapter 2: structure- and level-oriented psychological differences studies; structure- and level-oriented generalizability studies; structure- and level-oriented contextual linkage exploration studies; and structure- and level-oriented contextual linkage validation studies. The structure- and level-oriented studies differ primarily in the analyses employed, so their presentation is integrated. A schematic overview is given in Table 5.1.

This book addresses the methodological features of cross-cultural research. The common characteristic of such studies is their comparative nature, which involves the comparison of at least two cultural populations. Many studies involve different nation states, in sociology (e.g., Inglehart & Welzel, 2010; Van Deth, Montero, & Westholm, 2007), education (e.g., Arnove, Torres, & Franz, 2012; Van de Werfhorst & Mijs, 2010), political sciences (e.g., Coffé & Bolzendahl, 2010; Poguntke & Webb, 2007), management (e.g., House et al., 2004), and psychology (e.g., Schmitt, Allik, McCrae, & Benet-Martínez, 2007). However, comparative studies can also involve different ethnic groups from a single country such as the comparison of ethnic groups in the United States (e.g., Trinidad, Pérez-Stable, White, Emery, & Messer, 2011) and in Europe (Phalet & Kosic, 2006).

Two closely related concepts play an essential role in cross-cultural comparisons, namely equivalence and bias (Poortinga, 1989; Van de Vijver, 2015). There is no consensual definition of either concept in the cross-cultural literature. Johnson (1998) identified more than fifty types of definitions of equivalence, addressing dissimilar features, such as constructs, methodology, language, and context. All definitions refer to some aspect that is shared across cultures or to a qualitative or quantitative procedure to establish the shared features. A review of bias approaches would probably show a comparable variety.

Four procedures for sampling cultures can be discerned (cf. Boehnke, Lietz, Schreier, & Wilhelm, 2011). In convenience sampling, researchers select a culture simply because of considerations of convenience. These considerations can derive from various sources; researchers may be from that culture, are acquainted with collaborators from that culture, or happen to stay there for a period of time. The choice of culture is not related to the theoretical questions raised and is often haphazard. Studies adopting this sampling scheme often fall into the category of psychological differences studies.

Data analysis in cross-cultural research involves more than the preparation of the correct instructions to run a computer program of a statistical package. It is a link in the long chain of empirical research that starts with the specification of a theoretical framework and ends with drawing conclusions. Strategic decisions in the data analysis such as the choice of statistical techniques can only be made on the basis of a combination of substantive considerations such as the research questions or hypotheses involved and statistical considerations such as measurement level and sample size.