Improving the Performance of Tracking Studies
51 Pages Posted: 29 Sep 2013
Date Written: March 3, 2013
Tracking studies are prevalent in the social sciences. These studies are predominantly implemented via repeated cross-sectional surveys of independent, non-overlapping samples, which are much less costly than recruiting and maintaining a longitudinal panel that track the same sample of respondents over time. In the existing literature, data from repeated cross-sectional surveys are analyzed either independently for each time period, or longitudinally by focusing on the dynamics of the aggregate measures (e.g., sample averages). In this study, we propose a multivariate latent state-space model that can be applied directly to the individual-level data from each of the cross-sectional surveys over time, taking full advantage of three patterns embedded in the data: a) inter-temporal dependence within the population means of each survey variable, b) temporal co-movements across the population means of different survey variables and c) cross-sectional co-variation across individual responses within each sample. We illustrate our proposed model on two applications. In the first application, we have access to all the individual-level purchase data from one large population of grocery shoppers over a span of 36 months. This provides us with a testing ground for benchmarking our proposed model against existing approaches in a Monte Carlo experiment, in order to determine which model performs best in inferring population trends using data sampled through repeated cross-sections. In the second application, we apply the proposed model to repeated cross-sectional surveys that track customer perceptions and satisfaction for an automotive dealer, a situation that is often encountered by marketing researchers.
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