Marketing Research Blog

I’m certain that for a lot of folks, it would be hard to believe that organizations hire researchers to conduct pure research, that is, research conducted to expand knowledge-not to address a specific issue. But I was hired by the tobacco industry to conduct pure research not applied research.

As a freshly minted Ph.D., I was truly in “hog heaven” getting to conduct many, many experimental studies. Experimental studies, unlike correlational ones, permit the researcher to identify causal relationships between variables.Since I had specialized in human factors psychology, I was especially interested in the effect of smoking condition (smoker, non-smoker, and smoker-deprived) on complex human performance. Though I invariable found no significant differences (p > .05) as a function of smoking condition, I still was able to publish articles in the scientific literature in spite of the generally accepted wisdom that “null” results aren’t publishable.

Everyone that has ever taken statistics 101 has heard the expression “Correlation does not imply causation.” Consider a matrix with two rows (smoker vs. non-smoker) and two columns (cancer vs. no cancer). If proportionally more smokers have cancer than non-smokers, this would mean that smoker and cancer are correlated. But it does not mean that cancer causes smoking. Being such an emotional charged issue, though, many would have a hard time believing that such evidence doesn’t support an explanation that smoking causes cancer. The only way that one could prove that smoking “caused” cancer would be via experimentation. However, an experimental evaluation would be unethical and immoral. I say all this but I certainly don’t promote smoking. Even during my tobacco industry days, I was prone to telling folks that it wasn’t smart to intake anything into their lungs except air.

Here is an example of another non-causal relationship for which it should be readily understandable that it doesn’t define a causal relationship. Consider a matrix with two rows (households with TVs vs. households without TVs) and two columns (cancer vs. non-cancer). One would find higher cancer rates among household that didn’t have TVs. But I suspect that few folks would believe that providing televisions to non-TV households would decrease the cancer rates. Experimental studies demonstrate cause and effect; correlational studies don’t!