For the past five years, our team at Perficient has conducted continuous studies to evaluate the long-term impact of links on rankings. These studies consistently show that links are important as a ranking factor, but they also show that Google places an emphasis on the quality / authority of those links. We can say this based on the strong correlation our recent studies show between the Domain Authority (DA) and the Page Authority (PA) for linking pages and the ranking of the pages that receive the links.

Every year Moz has kindly given us access to the Link Explorer Index, which is where we get the raw data for our analysis. In these studies, we perform Spearman correlation analysis to show how the following variables correlate with ranking:

  • Number of links to the ranking page
  • DA of links to the ranking page
  • PA of links to the ranking page

Please note that Moz never compensated us for including or asking for the DA and PA calculations – we chose to do so at our own request. In fact, it wasn't until the beginning of October 2020 that the Moz staff found out that we had done this, despite the fact that we had published the post with our results on April 16, 2020 (to be honest, I was too busy to catch up with the Moz team and let them be You know). In short, there is no consideration here.

Study methodology

Go ahead and jump to the study results section if you just want to see how things went!

We have been conducting this study almost annually since May 2016. In 2016, we started with an initial query set of 6K queries, but over time we expanded that to 32K queries. The queries were obtained by pulling keyword ranking reports for websites in three different target market sectors and then manually reviewing to remove any queries that were poorly formed or not relevant to that sector. The market sectors we looked at were technology, healthcare, and finance.

Here is the breakdown of the query set sizes across the five studies we've done so far:

  • May 2016: 6K requests
  • Aug 2016: 16,000 queries
  • May 2017: 16,000 inquiries
  • August 2018: 27,000 inquiries
  • December 2019: 32,000 inquiries

Each of the advanced query sets includes all the queries from previous studies, so we can track the results through 2016 on a consistent basis.

In compiling this study, I also consulted two statistics experts: Paul Berger of Bentley University and Per Enge, formerly Stanford University. Because of the nature and structure of the dataset, it was decided that the best approach was to calculate the root mean square of the Spearman's correlation for all of the queries in the study.

I chose this approach because it uses the square of the correlation variable (where the correlation value is R, the root mean square uses the square R). This is important because the correlation variable R may be useful to know about it, but it doesn't allow you to be specific about what it means in the real world.

More interesting, however, is the R-square value. For example, if you have an R-squared value of 0.56, we can say that 56% of the variability in observed behavior Y (in our case, rankings) is due to the test variable X (in our study Number of Links, or DA / PA ).

Here is a picture of how this calculation process works:

How a root mean square calculation works

summary of results

As part of the study, we obtained data on the growth of the Link Explorer index over time. Since we have index size data over three years for our 16K query set, we looked at this snapshot of the data to show how the Link Explorer index expanded:

Links Explorer Index growth

One of the main goals of the study was to find the correlation between the number of links on a page and its ranking. Of course, the most important factors in ranking are the relevance and quality of the content – they have to be. Here is the squared mean calculation result of how the number of links on a page correlates with its ranking:

Number of links as a ranking factor

This score is slightly lower than in previous years (the score in August 2018 was 0.293, but it still indicates that the number of links to a page is significantly correlated with that page's ranking).

However, we also took a close look at the correlation of Moz DA and PA with the ranking of a page. Here are the results we saw for this review:

If youIf youAuthority of links as a ranking factor

This shows that Moz DA and PA are both better predictors of ranking than the total number of links. The values ​​of 0.328 and 0.307 are both very strong correlations in an environment as complex as organic search.

This is an important finding as it is in line with what many of us in the SEO community have long believed: The websites you get links from are more important than the sheer number of links. In addition, it is likely that most of the pages in our query set that contained large numbers of links also had significant numbers of higher-authority links.

Please note that the Moz team has informed me that they are working on an update to the algorithm for PA that will be released in the near future and that they will improve these results.

Conclusion

This data confirms that getting links from more authoritative websites is worth more than getting large numbers of links. Google's perspective of what makes better links is likely to be more nuanced than a simple metric like PageRank (the Google metric that DA and PA are most similar to). This may include assessing the relevance to the topic and the overall quality perception of the linking page.

As SEO experts, increasing the quality and authority of our own website remains one of our main tasks in order to grow our websites or our clients' websites.

You can see the full original links as a ranking factor study here.

Important note: Google does not have access to Moz's DA and PA metric data and Google does not use Moz DA or Moz PA as a ranking factor.

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