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Paul J. Lavrakas
The topic about which I am speaking today is very special to me, and I have been looking forward to making a presentation such as this for a long time. But please know that the ideas about which I will be speaking continue to evolve for me, and what I would have said about this topic twenty years ago, or even ten years ago, would not have been as �developed� as it is for me today (cf. Lavrakas 2012, 2013).
I would like to begin by noting the three major premises that underlie the views I will be expressing. These come from my nearly forty years as a researcher, during which I have encountered a great many and wide variety of social, behavioral, and marketing research studies, both quantitative and qualitative in nature.
First, I believe that many of these studies were conceptualized poorly, executed poorly, and/or interpreted poorly.
Second, I believe that the quality of most of these studies could have been improved with few, if any, cost implications.
And third, I believe that using the Total Error framework, about which I am speaking today, can help bring about a meaningful improvement in research quality.
Many in the audience already are familiar with the Total Survey Error (TSE) approach (cf. Groves 1989; Fuchs 2008). But I sense that many more are not familiar with it. Furthermore, from what I have observed in the past twenty-plus years, few appear to apply the approach broadly to the diverse realms of social, behavioral, and marketing research. And yet, it is what I call the �Total Error� perspective that underlies the TSE perspective.
I am not sure why this is the case, but my goal today is to demonstrate why thinking broadly about a Total Error (TE) approach, not merely �
