I must admit I'm obsessed with BI designs for business performance measurement. I guess I'm jaundiced, having seen too many naive, single group, pretest-treatment-posttest analyses offered as “proof” of a new initiative's success, when the results can just as easily be explained by other factors. As I continued my search for answers to BI design, a Google of “research designs” landed me on the very informative web page of a graduate course at North Carolina State University taught by G. David Garson, entitled Quantitative Research for Public Adminstration. 

For Garson, research designs are characterized as either experimental or quasi-experimental, depending on whether subjects are randomly assigned to treatment and control groups. The randomization deployed in experimental designs "goes a long way toward controlling for variables which are not included in the study", while quasi-experimental designs have to "control for confounding variables explicitly through statistical techniques." Garson proceeds to provide a comprehensible-for-BI taxonomy of experimental and quasi-experimental designs borrowing from the classic Quasi-Experimentation: Design and Analysis Issues for Field Settings, and its updated cousin Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Armed with the powerful designs for analytics presented in these sources, BI practitioners can make stronger cases for what's really happening when they measure and attribute business performance. The remainder of this column focuses on experimental designs pertinent for business; a subsequent article will look at quasi-experimental designs.

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