Abstract
In this article author presents the results of comparative analysis in reference to scales based on 5-, 7-, 9-, and 11-point response categories. An attempt was made to find the optimum number of responses among these scales but in this regard to the assumptions underlying the Confirmatory Factor Model and MultiTrait-MultiMethod. For this purpose, the data was collected from a sample of young consumers (n = 200) studying at the universities in Poland. The specific aim of the research was focused on their attitudes, which measured different aspects of the companies’ unethical behavior in the context of marketing activities. For the comparison of scales, the author has applied four models derived from the generalized CFA-MTMM model. This model allowed the recommendation of the best scale, and also helped to evaluate the effects associated with the use of particular type of scale on the CFA-MTMM alternative models and extracted, through their agency, factors.
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Notes
- 1.
In fact, the beginning of these models was due to works of Werts and Linn (1970) or Jöreskog (1970), who proposed to treat the MTMM matrix as a CFA model. Much later, because there were problems with estimation of the parameters in an ordinary CFA-MTMM model requiring at least three measures for each construct, Saris et al. (2004) have developed the Split-Ballot MTMM (or SB-MTMM) model where each respondent could answer all questions only twice.
- 2.
In practice, a hypothesized, e.g. CFA—Correlated Traits and Correlated Methods model is set as baseline and compared with the nested more restrictive models in which specific parameters are eliminated or are constrained to zero or 1.0.
- 3.
Adam Mickiewicz University, University of Technology, University of Economics, University of Life Sciences and University of Medical Sciences.
- 4.
They are computed on factor loadings either for factor traits or methods.
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Tarka, P. (2016). CFA-MTMM Model in Comparative Analysis of 5-, 7-, 9-, and 11-point A/D Scales. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_47
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