Modeling Individual-Level Change in Marketing Phenomena
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چکیده
Many important marketing issues deal with the study of change in marketing variables based on an analysis of repeated measurements of entities (consumers, salespeople, companies, brands, etc.) observed at different points in time or at different levels of an independent variable. Traditionally, such data have been analyzed using OLS regression pooled across repeated measurements. We describe and illustrate the potential of three powerful new methodologies for studying individuallevel change in marketing phenomena, viz., hierarchical linear modeling, latent curve analysis, and hierarchical linear Bayes. These techniques can be used to simultaneously model (1) the average change trajectory at the population level, (2) individual differences in this average trajectory across entities, and (3) the systematic effect of exogenous variables on individual change processes. The three methods are compared on a number of substantive and statistical modeling criteria. An empirical illustration is provided using data on household purchases of private label cola over a five-year period. None of the three techniques clearly dominates the others, and their relative strength is contingent on the specific research situation and the data. However, the three new methods are preferable to OLS regression in terms of statistical assumptions made and substantive findings obtained.
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تاریخ انتشار 2003