Researchers are desperate to understand behavior. Health researchers want to know what leads to a lifetime of smoking and how the daily smoking routine affects the quality of life. Education researchers examine the behavior of model teaching environments and contemplate best practices. Psychologists look for signs of social exclusion among victims of brain injuries. Marketing researchers chase an elusive explanation for consumer behavior, wanting to know product and service preferences in every conceivable category. And, if that were not enough, researchers of all ilk, to a lesser or greater extent, grapple with an often ill-fated attempt to predict (and shape) behaviors to come.
But researchers have come to appreciate that behavior is not enough. It is not enough to simply ask about past behavior, observe current behavior, or capture in-the-moment experiences via mobile. Behavior only tells part of a person’s story and, so, researchers passionately beef-up their research designs to include “why” – focusing on not just what people do but why they do it. “Why,” of course, is often phrased as “what,” “how,” or “when” questions – “What was going on at the time you picked up your first cigarette?” – but, whatever the format, the goal is the same, i.e., to get Read Full Text
Greg Allenby, marketing chair at Ohio State’s business school, published an article in the May/June issue of Marketing Insights on heterogeneity or, more specifically, on the idea that 1) accounting for individual differences is essential to understanding the “why” and “how” that lurks within research data and 2) research designs often mask these differences by neglecting the relativenature of the constructs under investigation. For instance, research concerning preference or satisfaction is useful to the extent it helps explain why and how people think differently as it relates to their preferences or levels of satisfaction, yet these are inherently relative constructs that only hold meaning if the researcher understands the standard (the “point of reference”) by which the current question of preference or satisfaction is being weighed – i.e., my preference (or satisfaction) compared to…what? Since the survey researcher is rarely if ever clued-in on respondents’ points of reference, it would be inaccurate to make direct comparisons such as stating that someone’s product preference is two times greater compared to someone else’s.
The embedded “relativeness” associated with responding to constructs such as preference and satisfaction is just one of the pesky problems inherent in designing this type of research. A related but different problem revolves around the personal interpretation given Read Full Text
It is a common misperception among researchers that the analysis of research data is a process that is confined to the data itself. This is probably truer among qualitative researchers than survey researchers given that the latter frequently publish their work in the literature comparing and contrasting their data with relevant earlier studies. Qualitative research, on the other hand, is typically held up to less scrutiny; and, except for the usual comparisons of populations segments, it is rare to find an analytical discussion that goes beyond the patterns and themes derived from the qualitative data itself. This may be for any number of reasons. It may be associated with the idea that qualitative research by definition is chock full of uncontrollable variables that vary from study to study making data comparisons across studies unreliable, or it may be researchers’ unfamiliarity Read Full Text