I was quite pleased as I started to write this column. I'd just gone out to the Information Management site and seen from the comments that no less than 3 people had read Analytical Designs for BI Part 1. I'm pretty sure from tenor of the notes that those 3 will read this column as well. Progress.
Part 1 discussed randomized experiments for business performance measurement. Classic experiments, the platinum standard for BI investigations, are, unfortunately, not always practical in business settings. The less intrusive lottery designs, which deploy but mask randomization, may be suitable for an additional class of business evaluations. Plan B, when randomized experiments are out of consideration, involves quasi-experimental studies that attempt to control potential confounding variables by clever design techniques and statistical adjustment.
When designing quasi-experiments (or randomized experiments for that matter), BI analysts should be guided by considerations of internal validity of their investigations. Internal validity obsesses on threats to the proposed cause-effect relationship between experimental factors and the dependent measures. As G. David Garson, cited in Part 1, notes: When there is lack of internal validity, variables other than the independent(s) being studied may be responsible for part or all of the observed effect on the dependent variable(s).
Biases in BI
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