The Long and Cross of Analytics

Anytime you see results from studies, especially from those that offer causation arguments, here is one qualifier question you should ask to determine whether or not a study is worth your time and its causation arguments  have any merit to them:

Is this a cross-sectional study or a longitudinal study?

Cross-sectional: It is the easiest one to conduct, so everyone does it. The only conclusion anyone can draw from such a study is Correlation. To find causation is simply wrong. A cross-sectional study analyzes a cross-section of the target set (be it businesses, customers etc) at a given point in time. It classifies the target set into “winners” and “losers” based on a metric the researcher chooses and looks for traits that are present in one and not the other. That is positive traits that are present in winners and lacking in losers and negative traits absent in winners and present in losers. Then it hints at or calls out the winners are winners because of their positive traits and their lack of negative traits.

Cross-sectional studies follow  the  general pattern,

“Seven/Eight/etc  habits/traits/etc of  enormously successful individuals/businesses/entrepreneurs/bloggers”

Longitudinal:  It is very hard to conduct a longitudinal study and it takes time (literally). It analyzes a target set over a period of time. It identifies winners and losers too but not based on traits but based on some action that was taken or a condition that was present at the beginning of time. Such a study follows the performance of those with the condition and those without over the period and measures the difference in their performance. Some just take point measurements at two different points of time.

Longitudinal studies follow the general pattern,

“Businesses/Entrepreneurs/Individuals who employed  factor/action/method  saw their performance increase by x%  over 7/8/9 years”

We see lot more cross-sectional studies than we see longitudinal studies.

If it is cross-sectional, I recommend you take a pass! It has all kinds of biases in flaws and very specifically confusing correlation with causation.

If it is longitudinal give it a second look.  But resist the temptation to accept the causation suggested by these studies, you still need to be aware of lurking variables and survivorship bias and most importantly beware of some that make the time flow back.