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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 e

In this paper we consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we do not need to compute high-dimensional integrals. We extend the approximation technique proposed by \\citet{Henery1981} in the Thurstone-Mosteller-Daniels model for any Thurstone order statistics model and we show that our approach allows for a unified approach. Moreover, our approach also allows for the analysis of any partial ranking. Partial rankings are essential in practical conjoint analysis to collect data efficiently to relieve respondents' task burden.

Consumer preferences can be measured by rankings of alternatives. When there are too many alternatives, this consumer task becomes complex. One option is to have consumers rank only a subset of the available alternatives. This has an impact on subsequent statistical analysis, as now a large amount of ties is observed. We propose a simple methodology to perform proper statistical analysis in this case. It also allows to test whether (parts of the) rankings are random or not. An illustration shows its ease of application.

Subjective probabilities play an important role in marketing research, for example where individuals rate the likelihood that they will purchase a new to develop product. The tau-equivalent model can describe the joint behaviour of multiple test items measuring the same subjective probability. It improves the reliability of the subjective probability estimate by using a weighted sum as the outcome of the test rather than an unweighted sum. One can choose the weights to obtain maximal reliability. In this paper we stress the use of confidence intervals to assess maximal reliability, as this allows for a more critical assessment of the items as measurement instruments. Furthermore, two new confidence intervals for the maximal reliability are derived and compared to intervals derived earlier in \\citet{YuanBentler2002, RaykovPenev2006}. The comparison involves coverage curves, a methodology that is new in the field of reliability. The existing Yuan-Bentler and Raykov-Penev intervals are shown to overestimate the maximal reliability, whereas one of our proposed intervals, the stable interval, performs very well. This stable interval hardly shows any bias, and has a coverage for the true value which is approximately equal to the confidence level.