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, and 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. That swimming can cause
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.

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 URLs are out of order with respect to links; for
    example, if position 2 has fewer links than position 3
  4. Record that anomaly
  5. Collect the same SERP in 14 days
  6. Record if the anomaly has been corrected (ie: position 3 now
    out-ranks position 2)
  7. Repeat across ten thousand keywords and test a variety of
    factors (backlinks, social shares, etc.)

So what are the benefits of this methodology? By looking at
change over time, we can see whether the ranking factor (correlate)
is a leading or lagging feature. A lagging feature can
automatically be ruled out as causal. A leading factor has the
potential to be a causal factor.

We collect a search result. We record where the search result
differs from the expected predictions of a particular variable
(like links or social shares). We then collect the same search
result 2 weeks later to see if the search engine has corrected the
out-of-order results.

Following this methodology, we tested 3 different common
correlates produced by ranking factors studies: Facebook shares,
number of root linking domains, and Page Authority. The first step
involved collecting 10,000 SERPs from randomly selected keywords in
our Keyword Explorer corpus. We then recorded Facebook Shares, Root
Linking Domains, and Page Authority for every URL. We noted every
example where 2 adjacent URLs (like positions 2 and 3 or 7 and 8)
were flipped with respect to the expected order predicted by the
correlating factor. For example, if the #2 position had 30 shares
while the #3 position had 50 shares, we noted that pair. Finally, 2
weeks later, we captured the same SERPs and identified the percent
of times that Google rearranged the pair of URLs to match the
expected correlation. We also randomly selected pairs of URLs to
get a baseline percent likelihood that any 2 adjacent URLs would
switch positions. Here were the results…

The outcome

It’s important to note that it is incredibly rare to expect a
leading factor to show up strongly in an analysis like this. While
the experimental method is sound, it’s not as simple as a factor
predicting future — it assumes that in some cases we will know
about a factor before Google does. The underlying assumption is
that in some cases we have seen a ranking factor (like an increase
in links or social shares) before Googlebot has and that in the 2
week period, Google will catch up and correct the incorrectly
ordered results. As you can expect, this is a rare occasion.
However, with a sufficient number of observations, we should be
able to see a statistically significant difference between lagging
and leading results. However, the methodology only detects when a
factor is both leading and
Moz Link Explorer discovered the relevant factor before
Google
.

Factor Percent Corrected P-Value 95% Min 95% Max
Control 18.93% 0
Facebook Shares Controlled for PA 18.31% 0.00001 -0.6849 -0.5551
Root Linking Domains 20.58% 0.00001 0.016268 0.016732
Page Authority 20.98% 0.00001 0.026202 0.026398

Control:

In order to create a control, we randomly selected adjacent URL
pairs in the first SERP collection and determined the likelihood
that the second will outrank the first in the final SERP
collection. Approximately 18.93% of the time the worse ranking URL
would overtake the better ranking URL. By setting this control, we
can determine if any of the potential correlates are leading
factors – that is to say that they are potential causes of improved
rankings.

Facebook Shares:

Facebook Shares performed the worst of the three tested
variables. Facebook Shares actually performed worse than random
(18.31% vs 18.93%), meaning that randomly selected pairs would be
more likely to switch than those where shares of the second were
higher than the first. This is not altogether surprising as it is
the general industry consensus that social signals are lagging
factors — that is to say the traffic from higher rankings drives
higher social shares, not social shares drive higher rankings.
Subsequently, we would expect to see the ranking change first
before we would see the increase in social shares.

RLDs

Raw root linking domain counts performed substantially better
than shares at ~20.5%. As I indicated before, this type of analysis
is incredibly subtle because it only detects when a factor is
both leading and Moz Link Explorer discovered the relevant
factor before Google
. Nevertheless, this result was
statistically significant with a P value <0.0001 and a 95%
confidence interval that RLDs will predict future ranking changes
around 1.5% greater than random.

Page Authority

By far, the highest performing factor was Page Authority. At
21.5%, PA correctly predicted changes in SERPs 2.6% better than
random. This is a strong indication of a leading factor, greatly
outperforming social shares and outperforming the best predictive
raw metric, root linking domains.This is not unsurprising. Page
Authority is built to predict rankings, so we should expect that it
would outperform raw metrics in identifying when a shift in
rankings might occur. Now, this is not to say that Google uses Moz
Page Authority to rank sites, but rather that Moz Page Authority is
a relatively good approximation of whatever link metrics Google is
using to determine ranking sites.

Concluding thoughts

There are so many different experimental designs we can use to
help improve our research industry-wide, and this is just one of
the methods that can help us tease out the differences between
causal ranking factors and lagging correlates. Experimental design
does not need to be elaborate and the statistics to determine
reliability do not need to be cutting edge. While machine learning
offers much promise for improving our predictive models, simple
statistics can do the trick when we’re establishing the
fundamentals.

Now, get out there and do some great research!

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