Best Practices in Scale Deployment: Maximizing Relevance to Cross-Cultural and Marketing Strategy Research

Five comments below provide strong and interesting perspectives on multi-item scale use. They define contexts and research areas where developed scales are valuable and where they are vulnerable. Katsikeas and Madan begin by taking a global perspective on scale use, demonstrating how the use and transferability of scales become even more problematic as researchers move across languages and cultures. They provide guidance for scale use that is particularly relevant to international marketing and marketing strategy research. Brendl and Calder acknowledge the use of well-formed scales as measured variables in psychological experiments, both as independent and dependent variables, but critique the use of multi-item scales to directly reveal latent unobservable constructs. As with any observed variable, scales should be used to test empirical predictions based on theoretical hypotheses about causal connections between theoretical constructs. Lehmann applauds the variability of multi-item scales and urges the exploration of the impact of various items within a scale. He advocates for flexibility and variation in multi-item scales related to psychological theories, simple three-item scales for manipulation checks, and one-item scales when measuring objective actions or beliefs. Baumgartner and Weijters focus on how to validate multi-item scales, particularly when used as mediators or moderators where a unique interpretation of the scale is so central. They recommend meta-analyses of scales that test relationships among measured scales. Like Lehmann, they worry about the impact of exhaustive scales on respondents and the impact of exhausted respondents on the scales themselves. In the final comment, Wang and Huang update our thinking on emerging ways to define and refine scales. They discuss ways to identify focal and orbital constructs and suggest item response theory as a way to adapt scales to subsets of items that best contribute to identifying individual differences between respondents. They support confirmatory factor analysis across different studies to assess scale equivalence across different contexts, cultures, and languages.