Stephen Royle

I was interested in the analysis by
[Frontiers](http://blog.frontiersin.org/2015/12/21/4782/) on the lack of
a correlation between the rejection rate of a journal and the "impact"
(as measured by the JIF). There's a nice follow here at [Science
Open](http://blog.scienceopen.com/2016/01/the-relationship-between-journal-rejections-and-their-impact-factors/).
The [Times Higher Education
Supplement](https://www.timeshighereducation.com/news/high-rejection-rates-by-journals-pointless)
also reported on this with the line that "mass rejection of research
papers by selective journals in a bid to achieve a high impact factor is
an enormous waste of academics' time".

First off, the JIF is a flawed metric in a
[number](https://quantixed.org/2015/05/05/wrong-number-a-closer-look-at-impact-factors/)
of
[ways](https://quantixed.org/2016/01/05/the-great-curve-ii-citation-distributions-and-reverse-engineering-the-jif/)
but even at face value, *what does this analysis really tell us*?

[![IF-vs-Rej-Rate-1-1-768x406](https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/if-vs-rej-rate-1-1-768x406.png?resize=640%2C338&ssl=1){.aligncenter
.size-full .wp-image-739 loading="lazy" decoding="async"
attachment-id="739"
permalink="https://quantixed.org/if-vs-rej-rate-1-1-768x406/"
orig-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/if-vs-rej-rate-1-1-768x406.png?fit=768%2C406&ssl=1"
orig-size="768,406" comments-opened="1"
image-meta="{\"aperture\":\"0\",\"credit\":\"\",\"camera\":\"\",\"caption\":\"\",\"created_timestamp\":\"0\",\"copyright\":\"\",\"focal_length\":\"0\",\"iso\":\"0\",\"shutter_speed\":\"0\",\"title\":\"\",\"orientation\":\"0\"}"
image-title="IF-vs-Rej-Rate-1-1-768×406" image-description=""
image-caption=""
medium-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/if-vs-rej-rate-1-1-768x406.png?fit=300%2C159&ssl=1"
large-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/if-vs-rej-rate-1-1-768x406.png?fit=640%2C338&ssl=1"
width="640" height="338"
srcset="https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/if-vs-rej-rate-1-1-768x406.png?resize=768%2C406&ssl=1 768w, https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/if-vs-rej-rate-1-1-768x406.png?resize=300%2C159&ssl=1 300w"
sizes="(max-width: 640px) 100vw, 640px"
recalc-dims="1"}](https://quantixed.org/2016/02/01/throes-of-rejection-no-link-between-rejection-rates-and-impact/if-vs-rej-rate-1-1-768x406/){rel="attachment wp-att-739"}

This plot is taken from the post by Jon Tennant at Science Open.

As others have pointed out:

1.  The rejection rate is dominated by desk rejects, which although very
    annoying, don't take that much time.
2.  Without knowing the journal name it is difficult to know what to
    make of the plot.

The data are available from
[Figshare](https://figshare.com/articles/Selecting_for_impact_data_20160106_xlsx/2060589/1)
and -- thanks to Thomson-Reuters habit of reporting JIF to 3 d.p. -- we
can easily pull the journal titles from a list using JIF as a key. The
list is
[here](https://github.com/quantixed/blog-standard/blob/master/rjxn_vs_jif.csv).
Note that there may be errors due to this quick-and-dirty method.

The list takes on a different meaning when you can see the Journal
titles alongside the numbers for rejection rate and JIF.

[![rjxn](https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/rjxn.png?resize=544%2C1024&ssl=1){.alignleft
.size-large .wp-image-740 loading="lazy" decoding="async"
attachment-id="740" permalink="https://quantixed.org/rjxn/"
orig-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/rjxn.png?fit=612%2C1151&ssl=1"
orig-size="612,1151" comments-opened="1"
image-meta="{\"aperture\":\"0\",\"credit\":\"\",\"camera\":\"\",\"caption\":\"\",\"created_timestamp\":\"0\",\"copyright\":\"\",\"focal_length\":\"0\",\"iso\":\"0\",\"shutter_speed\":\"0\",\"title\":\"\",\"orientation\":\"0\"}"
image-title="rjxn" image-description="" image-caption=""
medium-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/rjxn.png?fit=160%2C300&ssl=1"
large-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/rjxn.png?fit=544%2C1024&ssl=1"
width="544" height="1024"
srcset="https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/rjxn.png?w=612&ssl=1 612w, https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/rjxn.png?resize=160%2C300&ssl=1 160w, https://i0.wp.com/quantixed.org/wp-content/uploads/2016/02/rjxn.png?resize=544%2C1024&ssl=1 544w"
sizes="(max-width: 544px) 100vw, 544px"
recalc-dims="1"}](https://quantixed.org/2016/02/01/throes-of-rejection-no-link-between-rejection-rates-and-impact/rjxn/){rel="attachment wp-att-740"}

 

Looking for familiar journals -- whichever field you are in -- you will
be disappointed. There's an awful lot of noise in there. By this, I mean
journals that are outside of your field.

This is the problem with this analysis as I see it. It is difficult to
compare Nature Neuroscience with Mineralium Deposita...

My plan with this dataset was to replot rejection rate versus JIF2014
for a few different journal *categories*, but I don't think there's
enough data to do this and make a convincing case one way or the other.
So, I think the jury is still out on this question.

It would be interesting to do this analysis on a bigger dataset.
Journals releasing their numbers on rejection rates would be a step
forward to doing this.

**One final note:**

The Orthopedic Clinics of North America is a tough journal. Accepts only
2 papers in every 100 for an impact factor of 1!

 

::: {style="clear:both;"}
:::

---

The post title is from "Throes of Rejection" by Pantera from their Far
Beyond Driven LP. I rejected the title "Satan Has Rejected my Soul" by
Morrissey for obvious reasons.


I was interested in the analysis by Frontiers on the lack of a correlation between the rejection rate of a journal and the “impact” (as measured by the JIF). There’s a nice follow here at Science Open.

Messaggi di Rogue Scholar

Throes of Rejection: No link between rejection rates and impact?