Abstract:
There are many examples in marketing research where cross-sectional surveys are taken at periodic intervals. As different respondents are interviewed each period, the advantages of a panel cannot be exploited. The purpose of this study is to develop a methodology capable of modeling gross-change patterns using repeated cross-sectional surveys. Individuals at one time period are matched with similar individuals at the following time period using data fusion (statistical matching) techniques, thus creating ‘pseudo individuals’. Aging characteristics of individuals over time are taken into account in this process. The attitudes and behaviour of all pseudo individuals in a data set can then be tracked over multiple time periods. The method is developed using lifestyles data from the Nielsen Company’s Panel Views survey. The use of panel data ensures the accuracy of the statistical matches can be tested. A simulation study is then conducted to test the method when different sample sizes are apparent, and lastly, the method is applied to TNS Context data, a series of repeated cross-sectional lifestyles studies. Results demonstrate some interesting trends in terms of gross change in lifestyles patterns and behavior. This development of this technique contributes to the area of marketing methodology by providing an alternative to panel data, extends the application of data fusion techniques beyond their usual scope of creating ‘single source’ data from two sources collected at a similar time point, and contributes to theory on consumer lifestyles.
