Reliability and Rankings


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 emphasised enough. By improving the methodology to analyze collected data, information can be obtained more efficiently and the quality of the information in the data can be increased.

In her thesis, entitled Reliability and Rankings, Kar Yin Lam discusses some aspects of the statistical analysis of measurement data obtained via questionnaires. She proposes new methodologies to collect consumer preferences data measured as partial rankings data, especially in the context of conjoint measurements.

As a partial rankings task amounts 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. This will certainly help marketers to identify and target consumers by understanding their preference behaviour, and to implement a more efficient and optimal marketing strategy.

 Kar Yin Lam defended her dissertation on April 14, 201. Her promoter was <link people _blank>Prof. Dr. Philip Hans Franses. Her co-promoter was Dr. A.J. Koning. Other members of the Doctoral Committee were Prof. Dr. P.J.F. Groenen, Prof. Dr. J. de Leeuw, and Prof. Dr. J.J. Louviere.

About Kar Yin Lam

Kar Yin Lam (1983) graduated in Econometrics at Erasmus University Rotterdam in 2006. In the same year, she joined the Erasmus Research Institute of Management to carry out her doctoral research. Her research interests are marketing modelling, psychometrics, and econometrics. She presented her research at various international conferences.

Abstract of Reliability and Rankings

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, Lam focuses on maximising scale reliability. She derives the asymptotic distribution of maximal reliability measures to construct confidence intervals in order to assess the adequacy of the measure. She stresses the use of confidence intervals accompanying single measures that summarise 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 analyses of stated consumer preferences measured as rankings data, especially in the context of conjoint measurements. Lam’s statistical models allow for the efficient use of partial rankings to collect preference data. As a partial rankings task amounts 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, Lam shows that her model is able to extract sufficient preference information from partial rankings data to take into account respondents' heterogeneity in their choice and preference behaviour, which is generally assumed in marketing. This will certainly help marketers to identify and target consumers by understanding their preference behaviour, and to implement a more efficient and optimal marketing strategy.