Re: Citation statistics

From: Stevan Harnad <amsciforum_at_GMAIL.COM>
Date: Sun, 15 Jun 2008 12:50:27 -0400

On Thu, 12 Jun 2008, Charles Oppenheim wrote:

> Re:
> International Mathematical Union announces Citation Statistics report
> Numbers with a number of problems
> Robert Adler, John Ewing (Chair), Peter Taylor
> I've now read the whole report. Yes, it tilts at the
> usual windmills, and rightly dismissed the use of Impact
> factors for anything but crude comparisons, but it fails
> to address the fundamental issue, which is: citation and
> other metrics correlate superbly with subjective peer
> review. Both methods have their faults, but they are
> clearly measuring the same (or closely related) things.
> Ergo, if you have evaluate research in some way, there is
> no reason NOT to use them! It also keeps referring to
> examples from the field of maths, which is a very strange
> subject citation-wise.

I have now read the IMU report too, and agree with Charles that it
makes many valid points but it misunderstands the one fundamental
point concerning the question at hand: Can and should metrics be
used in place of peer-panel based rankings in the UK Research
Assessment Exercise (RAE) and its successors and homologues elsewhere?
And there the answer is a definite Yes.

The IMU critique points out that research metrics in particular and
statistics in general are often misused, and this is certainly true.
It also points out that metrics are often used without validation.
This true is correct. There is also a simplistic tendency to try
to use one single metric, rather than multiple metrics that can
complement and correct one another. There too, a practical and
methodological error is correctly pointed out. It is also true that
the "journal impact factor" has many flaws, and should on no account
be used to rank individual papers of researchers, and especially
not alone, as a single metric.

But what all this valuable, valid cautionary discussion overlooks
is not only the possibility but the empirically demonstrated fact
that there exist metrics that are highly correlated with human
expert rankings. It follows that to the degree that such metrics
account for the same variance, they can substitute for the human
rankings. The substitution is desirable, because expert rankings
are extremely costly in terms of expert time and resources. Moreover,
a metric that can be shown to be highly correlated with an already
validated variable predictor variable (such as expert rankings)
thereby itself becomes a validated predictor variable. And this is
why the answer to the basic question of whether the RAE's decision
to convert to metrics was a sound one is: Yes.

Nevertheless, the IMU's cautions are welcome: Metrics do need to
be validated; they do need to be multiple, rather than a single,
unidimensional index; they do have to be separately validated for
each discipline, and the weights on the multiple metrics need to
be calibrated and adjusted both for the discipline being assessed
and for the properties on which it is being ranked. The RAE 2008
database provides the ideal opportunity to do all this discipline-specific
validation and calibration, because it is providing parallel data
from both peer panel rankings and metrics. The metrics, however,
should be as rich and diverse as possible, to capitalize on this
unique opportunity for joint validation.

Here are some comments on particular points in the IMU report. (All
quotes are from the report):

> The meaning of a citation can be even more subjective than
> peer review.

True. But if there is a non-metric criterion measure -- such as peer
review -- on which we already rely, then metrics can be cross-validated
against that criterion measure, and this is exactly what the RAE
2008 database makes it possible to do, for all disciplines, at the
level of an entire sizeable nation's total research output..

> The sole reliance on citation data provides at best an incomplete
> and often shallow understanding of research -- an understanding
> that is valid only when reinforced by other judgments.

This is correct. But the empirical fact has turned out to be that
a department's total article/author citation counts are highly
correlated with its peer rankings in the RAE in every discipline
tested. This does not mean that citation counts are the only metric
that should be used, or that they account for 100% of the variance
in peer rankings. But it is strong evidence that citation counts
should be among the metrics used, and it constitutes a (pairwise)

> Using the impact factor alone to judge a journal is like using
> weight alone to judge a person's health.

Using only the journal impact factor (the average citation counts
of article published in that journal) in place of the actual citation
counts for individual articles and authors is of course as absurd
as using only the average marks of a candidate's secondary school,
instead of the candidate's own actual marks, to decide on university
admission. However, the journal's average might still be used as
one of the battery of candidate metrics to be validated and
collaborated jointly, discipline by discipline, as it may give
further, valid independent information about the level of the
publication venue itself, over and above the individual citation

> For papers, instead of relying on the actual count of citations
> to compare individual papers, people frequently substitute the
> impact factor of the journals in which the papers appear.

As noted, this is a foolish error if the journal impact factor is
used alone, but it may enhance predictivity and hence validity if
added to a battery of jointly validated metrics.

> The validity of statistics such as the impact factor and h-index
> is neither well understood nor well studied.

The h-index (and its variants) were created ad hoc, without validation.
They turn out to be highly correlated with citation counts (for
obvious reasons, since they are in part based on them). Again, they
are all welcome in a battery of metrics to be jointly cross-validated
against peer rankings or other already-validated or face-valid

> citation data provide only a limited and incomplete view of
> research quality, and the statistics derived from citation
> data are sometimes poorly understood and misused.

It is certainly true that there are many more potential metrics of
research performance productivity, impact and quality than just
citation metrics (e.g., download counts, student counts, research
funding, etc.). They should all be jointly validated, discipline
by discipline and each metric should be weighted according to what
percentage of the criterion variance (e.g., RAE 2008 peer rankings)
it predicts.

> relying primarily on metrics (statistics) derived from citation
> data rather than a variety of methods, including judgments by
> scientists themselves...

The whole point is to cross-validate the metrics against the peer
judgments, and then use the weighted metrics in place of the peer
judgments, in accordance with their validated predictive power.

> bibliometrics (using counts of journal articles and their
> citations) will be a central quality index in this system [RAE]

Yes, but the successor of RAE is not yet clear on which metrics it
will use, and whether and how it will validate them. There is still
some risk that a small number of metrics will simply be picked a
priori, without systematic validation. It is to be hoped that the
IMU critique, along with other critiques and recommendations, will
result in the use of the 2008 parallel metric/peer data for a
systematic and exhaustive cross-validation exercise, separately for
each discipline. Future assessments can then use the metric battery,
with initialized weights (specific to each discipline), and can
calibrate and optimize them across the years, as more data accumulates
-- including spot-checks cross-validating periodically against
"light-touch" peer rankings and other validated or face-valid

> sole reliance on citation-based metrics replaces one kind of
> judgment with another. Instead of subjective peer review one
> has the subjective interpretation of a citation's meaning.

Correct. This is why multiple metrics are needed, and why they need
to be systematically cross-validated against already-validated or
face-valid criteria (such as peer judgment).

> Research usually has multiple goals, both short-term and long,
> and it is therefore reasonable that its value must be judged
> by multiple criteria.

Yes, and this means multiple, validated metrics. (Time-course
parameters, such as growth and decay rates of download, citation
and other metrics are themselves metrics.)

> many things, both real and abstract, that cannot be simply
> ordered, in the sense that each two can be compared

Yes, we should not compare the incomparable and incommensurable.
But whatever we are already comparing, by other means, can be used
to cross-validate metrics. (And of course it should be done discipline
by discipline, and sometimes even by sub-discipline, rather than
by treating all research as if it were of the same kind, with the
same metrics and weights.)

> plea to use multiple methods to assess the quality of research

Valid plea, but the multiple "methods" means multiple metrics, to
be tested for reliability and validity against already validated

> Measures of esteem such as invitations, membership on editorial
> boards, and awards often measure quality. In some disciplines
> and in some countries, grant funding can play a role. And peer
> review -- the judgment of fellow scientists -- is an important
> component of assessment.

These are all sensible candidate metrics to be included, alongside
citation and other candidate metrics, in the multiple regression
equation to be cross-validated jointly against already validated
criteria, such as peer rankings (especially in RAE 2008).

> lure of a simple process and simple numbers (preferably a
> single number) seems to overcome common sense and good judgment.

Validation should definitely be done with multiple metrics, jointly,
using multiple regression analysis, not with a single metric, and
not one at a time.

> special citation culture of mathematics, with low citation
> counts for journals, papers, and authors, makes it especially
> vulnerable to the abuse of citation statistics.

Metric validation and weighting should been done separately, field
by field.

> For some fields, such as bio-medical sciences, this is appropriate
> because most published articles receive most of their citations
> soon after publication. In other fields, such as mathematics,
> most citations occur beyond the two-year period.

Chronometrics -- growth and decay rates and other time-based parameters
for download, citations and other time-based, cumulative measures
-- should be among the battery of candidate metrics for validation.

> The impact factor varies considerably among disciplines... The
> impact factor can vary considerably from year to year, and the
> variation tends to be larger for smaller journals.

All true. Hence the journal impact factor -- perhaps with various
time constants -- should be part of the battery of candidate metrics,
not simply used a priori.

> The most important criticism of the impact factor is that its
> meaning is not well understood. When using the impact factor
> to compare two journals, there is no a priori model that defines
> what it means to be "better". The only model derives from the
> impact factor itself -- a larger impact factor means a better
> journal... How does the impact factor measure quality? Is it
> the best statistic to measure quality? What precisely does it
> measure? Remarkably little is known...

And this is because the journal impact factor (like most other
metrics) has not been cross-validated against face-valid criteria,
such as peer rankings.

> employing other criteria to refine the ranking and verify that
> the groups make sense

In other words, systematic cross-validation is needed.

> impact factor cannot be used to compare journals across
> disciplines

All metrics should be independently validated for each discipline.

> impact factor may not accurately reflect the full range of
> citation activity in some disciplines, both because not all
> journals are indexed and because the time period is too short.
> Other statistics based on longer periods of time and more
> journals may be better indicators of quality. Finally, citations
> are only one way to judge journals, and should be supplemented
> with other information

Chronometrics. And multiple metrics.

> The impact factor and similar citation-based statistics can
> be misused when ranking journals, but there is a more fundamental
> and more insidious misuse: Using the impact factor to compare
> individual papers, people, programs, or even disciplines

Individual citation counts and other metrics: Multiple metrics,
jointly validated.

> the distribution of citation counts for individual papers in
> a journal is highly skewed, approximating a so-called power
> law... highly skewed distribution and the narrow window of
> time used to compute the impact factor

To the extent that distributions are pertinent, they too can be
parametrized and taken into account in validating metrics. Comparing
like with like (e.g., discipline by discipline) should also help
maximize comparability.

> using the impact factor as a proxy for actual citation counts
> for individual papers

No need to use one metric as a proxy for another. Jointly validate
them all.

> if you want to rank a person's papers using only citations to
> measure the quality of a particular paper, you must begin by
> counting that paper's citations. The impact factor of the
> journal in which the paper appears is not a reliable substitute.

Correct, but this obvious truth does not need to be repeated so
many times, and it is an argument against single metrics in general;
and journal impact factor as a single factor in particular. But
there's nothing wrong with using it in a battery of metrics for

> h-index Hirsch extols the virtues of the h-index by claiming
> that "h is preferable to other single-number criteria commonly
> used to evaluate scientific output of a researcher..."[Hirsch
> 2005, p. 1], but he neither defines "preferable" nor explains
> why one wants to find "single-number criteria."... Much of the
> analysis consists of showing "convergent validity," that is,
> the h-index correlates well with other publication/citation
> metrics, such as the number of published papers or the total
> number of citations. This correlation is unremarkable, since
> all these variables are functions of the same basic phenomenon...

The h-index is again a single metric. And cross-validation only
works against either an already validated or a face-valid criterion,
not just another unvalidated metric. And the only way multiple
metrics, all inter-correlated, can be partitioned and weighted is
with multiple regression analysis -- and once again against a
criterion, such as peer rankings.

> Some might argue that the meaning of citations is immaterial
> because citation-based statistics are highly correlated with
> some other measure of research quality (such as peer review).

Not only might some say it: Many have said it, and they are quite
right. That means citation counts have been validated against peer
review, pairwise. Now it is time to cross-validate and entire
spectrum of candidate metrics, so each can be weighted for its
predictive contribution.

> The conclusion seems to be that citation-based statistics,
> regardless of their precise meaning, should replace other
> methods of assessment, because they often agree with them.
> Aside from the circularity of this argument, the fallacy of
> such reasoning is easy to see.

The argument is circular only if unvalidated metrics are being
cross-correlated with other unvalidated metrics. Then it's a skyhook.
But when they are cross-validated against a criterion like peer
rankings, which have been the predominant basis for the RAE for 20
years, they are being cross-validated against a face-valid criterion
-- for which they can indeed be subsequently substituted, if the
correlation turns out to be high enough.

> "Damned lies and statistics"

Yes, one can lie with unvalidated metrics and statistics. But we
are talking here about validating metics against validated or
face-valid criteria. In that case, the metrics lie no more (or less)
than the criteria did, before the substitution.

> Several groups have pushed the idea of using Google Scholar
> to implement citation-based statistics, such as the h-index,
> but the data contained in Google Scholar is often inaccurate
> (since things like author names are automatically extracted
> from web postings)...

This is correct. But Google Scholar's accuracy is growing daily,
with growing content, and there are ways to triangulate author
identity from such data even before the (inevitable) unique author
identifier is adopted.

> Citation statistics for individual scientists are sometimes
> difficult to obtain because authors are not uniquely identified...

True, but a good approximation is -- or will soon be -- possible
(not for arbitrary search on the works of "Lee," but, for example,
for all the works of all the authors in the UK university LDAPs).

> Citation counts seem to be correlated with quality, and there
> is an intuitive understanding that high-quality articles are
> highly-cited.

The intuition is replaced by objective data once the correlation
with peer rankings of quality is demonstrated (and replaced in
proportion to the proportion of the criterion variance accounted
for) by the predictor metric.

> But as explained above, some articles, especially in some
> disciplines, are highly-cited for reasons other than high
> quality, and it does not follow that highly-cited articles are
> necessarily high quality.

This is why validation/weighting of metrics must be done separately,
discipline by discipline, and why citation metrics alone are not
enough: multiple metrics are needed to take into account multiple
influences on quality and impact, and to weight them accordingly.

> The precise interpretation of rankings based on citation
> statistics needs to be better understood.

Once a sufficiently broad and predictive battery of metrics is
validated and its weights initialized (e.g., in RAE 2008), further
interpretation and fine-tuning can follow.

> In addition, if citation statistics play a central role in
> research assessment, it is clear that authors, editors, and
> even publishers will find ways to manipulate the system to
> their advantage.

True, but inasmuch as the new metric batteries will be Open Access,
there will also be multiple metrics for detecting metric anomalies,
inconsistency and manipulation, and for naming and shaming the
manipulators, which will serve to control the temptation.

Stevan Harnad

Harnad, S. (2001) Research access, impact and assessment. Times
Higher Education Supplement 1487: p. 16.

Harnad, S., Carr, L., Brody, T. & Oppenheim, C. (2003) Mandated
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Improving the UK Research Assessment Exercise whilst making it
cheaper and easier. Ariadne 35.

Brody, T., Kampa, S., Harnad, S., Carr, L. and Hitchcock, S. (2003)
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Brody, T., Carr, L., Harnad, S. and Swan, A. (2007) Time to Convert
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> On Wed, 11 Jun 2008 18:05:36 +0200
> "Armbruster, Chris" <Chris.Armbruster_at_EUI.EU> wrote:
>> It is true that Thomson is misspelled as Thompson, but
>> it is so consistently. It also the case that the Leiden
>> stalwarts A.J.F. van Raan (wide body of work on
>> performance measurement, university ranking etc.) and
>> H.F. Moed (Book: Citation analysis in research
>> evaluation) are not cited.
>> Nevertheless, after reading the report, I would caution
>> against dismissing it. Science and scientists should be
>> concerned about the politicisation of metrics.
>> Politicisation comes from governments and research
>> funders but is also going on inside academic
>> institutions. Moreover, in a general sense the citation
>> and usage metrics currently available are not 'fit for
>> purpose'. Worse still, politicisation carries with it the
>> significant risk of arresting the development of tools
>> for metric research evaluation. Evaluation is often
>> narrowly defined as assessment and performance of
>> institutions and indivudals for the purpose of awarding
>> or denying funding and employment. This is something
>> entirely different from metric evaluation as research
>> information service to aid scientists in reducing the
>> complexity of scientific information in their daily
>> research.
>> All we have at the moment are some 'quick fix metrics'.
>> And these are increasingly used to make and legitimate
>> all kinds of decisions. It is thus welcome that
>> mathematicians and statisticians scrutinise current
>> practices and show up the lack of validity and
>> reliability of many measures, technical faults as well as
>> the misguided judgements of peers, university management,
>> funding agencies and government.
>> My own contribution (working paper) may be found with
>> SSRN:
>> Armbruster, Chris, "Access, Usage and Citation Metrics:
>> What Function for Digital Libraries and Repositories in
>> Research Evaluation?" (January 29, 2008).
>> Available at SSRN:
>> If the link is broken, please use a search engine *SSRN
>> plus title*
>> Chris Armbruster
>> -----Original Message-----
>> From: American Scientist Open Access Forum on behalf of
>> Sent: Wed 11/06/2008 14:56
>> To:
>> Subject: Re: Citation statistics
>> I haven't had a chance to read the report yet, but I'd
>> be suspicious of any report that fails to spell "Thomson"
>> correctly and fails to cite Ton van Raan, THE expert on
>> the subject.
>> Charles
>> Professor Charles Oppenheim
>> Head
>> Department of Information Science
>> Loughborough University
>> Loughborough
>> Leics LE11 3TU
>> Tel 01509-223065
>> Fax 01509 223053
>> e mail
>> -----Original Message-----
>> From: American Scientist Open Access Forum
>> On Behalf Of Jean Kempf
>> Sent: 11 June 2008 12:01
>> To:
>> Subject: Citation statistics
>> Here's a report on citation statistics written by a
>> statistician
>> A press release that was mailed out today to journalists
>> is at:
Received on Mon Jun 16 2008 - 02:27:58 BST

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