Assessing response styles across modes of data collection
- 1.1k Downloads
Cross-mode surveys are on the rise. The current study compares levels of response styles across three modes of data collection: paper-and-pencil questionnaires, telephone interviews, and online questionnaires. The authors make the comparison in terms of acquiescence, disacquiescence, and extreme and midpoint response styles. To do this, they propose a new method, namely, the representative indicators response style means and covariance structure (RIRSMACS) method. This method contributes to the literature in important ways. First, it offers a simultaneous operationalization of multiple response styles. The model accounts for dependencies among response style indicators due to their reliance on common item sets. Second, it accounts for random error in the response style measures. As a consequence, random error in response style measures is not passed on to corrected measures. The method can detect and correct cross-mode response style differences in cases where measurement invariance testing and multitrait multimethod designs are inadequate. The authors demonstrate and discuss the practical and theoretical advantages of the RIRSMACS approach over traditional methods.
KeywordsResponse styles Modes of data-collection Measurement invariance Survey research
The authors would like to thank the Intercollegiate Center for Management Sciences (Belgium) and Insites for supporting the research reported in this paper. Further, the authors would like to thank the following people for their feedback on previous versions of the paper: Hans Baumgartner, Jaak Billiet, Marion Debruyne, Koen Dewettinck, Alain De Beuckelaer and Patrick Van Kenhove.
- Bruner, G. C., James, K. E., & Hensel, P. J. (2001). Marketing scales handbook: A compilation of multi-item measures, vol. 3. Chicago: American Marketing Association.Google Scholar
- De Jong, M. G., Steenkamp, J.-B. E .M., Fox, J.-P., & Baumgartner, H. (2007). Using item response theory to measure extreme response style in marketing research: A global investigation. Journal of Marketing Research (in press).Google Scholar
- De Leeuw, E. D. (2005). To mix or not to mix: data collection modes in surveys. Journal of Official Statistics, 21(2), 233–255.Google Scholar
- Finney, S. J., & DiStefano, C. (2006). Nonnormal and categorical data in. In G. R. Hancock, & R. O. Mueller (Eds.) Structural equation modeling: A second course. Greenwich, CT: Information Age Publishing.Google Scholar
- McGee, R. K. (1967). Response set in relation to personality: An orientation. In I. A. Berg (Ed.) Response set in personality assessment (pp. 1–31). Chicago: Aldine.Google Scholar
- Muthén, L. K., & Muthén, B. O. (2004). Mplus, statistical analysis with latent variables: user guide (3rd ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
- Venkatesh, S., Smith, A. K., & Rangaswamy, A. (2003). Customer satisfaction and loyalty in online and offline environments. International Journal of Research in Marketing, 20(2), 153–175.Google Scholar