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Posted by rjonesx.

Correlation studies have been a staple of the search engine optimization community for many years. Each time a new study is released, a chorus of naysayers seem to come magically out of the woodwork to remind us of the one thing they remember from high school statistics — that “correlation doesn’t mean causation.” They are, of course, right in their protestations and, to their credit, an unfortunate number of times it seems that those conducting the correlation studies have forgotten this simple aphorism.

We collect a search result. We then order the results based on different metrics like the number of links. Finally, we compare the orders of the original search results with those produced by the different metrics. The closer they are, the higher the correlation between the two.

That being said, correlation studies are not altogether fruitless simply because they don’t necessarily uncover causal relationships (ie: actual ranking factors). What correlation studies discover or confirm are correlates.

Correlates are simply measurements that share some relationship with the independent variable (in this case, the order of search results on a page). For example, we know that backlink counts are correlates of rank order. We also know that social shares are correlates of rank order.

Correlation studies also provide us with direction of the relationship. For example, ice cream sales are positive correlates with temperature and winter jackets are negative correlates with temperature — that is to say, when the temperature goes up, ice cream sales go up but winter jacket sales go down.

Finally, correlation studies can help us rule out proposed ranking factors. This is often overlooked, but it is an incredibly important part of correlation studies. Research that provides a negative result is often just as valuable as research that yields a positive result. We’ve been able to rule out many types of potential factors — like keyword density and the meta keywords tag — using correlation studies.

Unfortunately, the value of correlation studies tends to end there. In particular, we still want to know whether a correlate causes the rankings or is spurious. Spurious is just a fancy sounding word for “false” or “fake.” A good example of a spurious relationship would be that ice cream sales cause an increase in drownings. In reality, the heat of the summer increases both ice cream sales and people who go for a swim. More swimming means more drownings. So while ice cream sales is a correlate of drowning, it is spurious. It does not cause the drowning.

How might we go about teasing out the difference between causal and spurious relationships? One thing we know is that a cause happens before its effect, which means that a causal variable should predict a future change. This is the foundation upon which I built the following model.

An alternative model for correlation studies

I propose an alternate methodology for conducting correlation studies. Rather than measure the correlation between a factor (like links or shares) and a SERP, we can measure the correlation between a factor and changes in the SERP over time.

The process works like this:

  1. Collect a SERP on day 1
  2. Collect the link counts for each of the URLs in that SERP
  3. Look for any URL pairs that are out of order with respect to …

    You can read the full article at Moz Blog

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    Posted by rjonesx.Correlation studies have been a staple of the search engine optimization community for many years. Each time a new study is released, a chorus of naysayers seem to come magically out of the woodwork to remind us of the one thing they remember from high school statistics...