Reliability and Rankings Defended on Thursday, 14 April 2011
Questionnaires are an important way to gather information about large populations for both qualitative and quantitative research. Hence, the value of a good questionnaire design and the quality of questionnaire data cannot be emphasized enough. This thesis discusses some aspects of the statistical analysis of measurement data obtained via questionnaires.
In the first part of this thesis we focus on maximizing scale reliability. We derive the asymptotic distribution of maximal reliability measures to construct confidence intervals in order to assess the adequacy of the measure. We stress the use of confidence intervals accompanying single measures that summarize the parameters to assess the adequacy of the measure. The results can lead to better designs of questionnaires, which in turn lead to more precise survey outcomes.
The second part of this thesis proposes methodologies to perform statistical analysis of stated consumer preferences measured as rankings data, especially in the context of conjoint measurements. Our statistical models allow for the efficient use of partial rankings to collect preference data. As a partial rankings task amount to a smaller burden for respondents than a complete ranking task, they may be more motivated to complete the task and as such the quality of the obtained data may improve. Moreover, we show that our model is able to extract sufficient preference information from partial rankings data to take into account respondents' heterogeneity in their choice and preference behavior, which is generally assumed in marketing. This certainly will help marketers to identify and target consumers by understanding their preference behavior, and to implement a more efficient and optimal marketing strategy.
reliability, measurement scale, maximal reliability, preference rankings, multiple comparisons, ranking models, conjoint analysis, finite mixture models, segmentation, respondents' heterogeneity