The Power-Corrected H-Index

I was going to write this blog post eventually, but the online first publication of Radosic and Diener’s (2021) article “Citation Metrics in Psychological Science” provided a good opportunity to do so now.

Radosic and Diener’s (2021) article’s main purpose was to “provide norms to help evaluate the citation counts of psychological scientists” (p. 1). The authors also specify the purpose of these evaluations. “Citation metrics are one source of information that can be used in hiring, promotion, awards, and funding, and our goal is to help these evaluations” (p. 1).

The authors caution readers that they are agnostic about the validity of citation counts as a measure of good science. “The merits and demerits of citation counts are beyond the scope of the current article” (p. 8). Yet, they suggest that “there is much to recommend citation numbers in evaluating scholarly records” (p. 11).

At the same time, they list some potential limitations of using citation metrics to evaluate researchers.

1. Articles that developed a scale can have high citation counts. For example, Ed Diener has over 71,000 citations. His most cited article is the 1985 article with his Satisfaction with Life Scale. With 12,000 citations, it accounts for 17% of his citations. The fact that articles that published a measure have such high citation counts reflects a problem in psychological science. Researchers continue to use the first measure that was developed for a new construct (e.g., Rosenberg’s 1965 self-esteem scale) instead of improving measurement which would lead to citations of newer articles. So, the high citation counts of articles with scales is a problem, but it is only a problem if citation counts are used as a metric. A better metric is the H-Index that takes number of publications and citations into account. Ed Diener also has a very high H-Index of 108 publications with 108 or more citations. His scale article is only of these articles. Thus, scale development articles are not a major problem.

2. Review articles are cited more heavily than original research articles. Once more, Ed Diener is a good example. His second and third most cited articles are the 1984 and the co-authored 1999 Psychological Bulletin review articles on subjective well-being that together account for another 9,000 citations (13%). However, even review articles are not a problem. First, they also are unlikely to have an undue influence on the H-Index and second it is possible to exclude review articles and to compute metrics only for empirical articles. Web of Science makes this very easy. In WebofScience 361 out of Diener’s 469 publications are listed as articles. The others are listed as reviews, book chapters, or meeting abstracts. With a click of a button, we can produce the citation metrics only for the 361 articles. The H-Index drops from 108 to 102. Careful hand-selection of articles is unlikely to change this.

3. Finally, Radosic and Diener (2021) mention large-scale collaborations as a problem. For example, one of the most important research projects in psychological science in the last decade was the Reproducibility Project that examined the replicability of psychological science with 100 replication studies (Open Science Collaboration, 2015). This project required a major effort by many researchers. Participation earned researchers over 2,000 citations in just five years and the article is likely to be the most cited article for many of the collaborators. I do not see this as a problem because large-scale collaborations are important and can produce results that no single lab can produce. Thus, high citation counts provide a good incentive to engage in these collaborations.

To conclude, Radosic and Diener’s article provides norms for a citation counts that can and will be used to evaluate psychological scientists. However, the article sidesteps the main question about the use of citation metrics, namely (a) what criteria should be used to evaluate scientists and (b) are citation metrics valid indicators of these criteria. In short, the article is just another example that psychologists develop and promote measures without examining their construct validity (Schimmack, 2021).

What is a good scientists?

I didn’t do an online study to examine the ideal prototype of a scientist, so I have to rely on my own image of a good scientist. A key criterion is to search for some objectively verifiable information that can inform our understanding of the world, or in psychology ourselves; that is, humans affect, behavior, and cognition – the ABC of psychology. The second criterion elaborates the term objective. Scientists use methods that produce the same results independent of the user of the methods. That is, studies should be reproducible and results should be replicable within the margins of error. Third, the research question should have some significance beyond the personal interests of a scientist. This is of course a tricky criterion, but research that solves major problems like finding a vaccine for Covid-19 is more valuable and more likely to receive citations than research on the liking of cats versus dogs (I know, this is the most controversial statement I am making; go cats!). The problem is that not everybody can do research that is equally important to a large number of people. Once more Ed Diener is a good example. In the 1980s, he decided to study human happiness, which was not a major topic in psychology. Ed Diener’s high H-Index reflects his choice of a topic that is of interest to pretty much everybody. In contrast, research on stigma of minority groups is not of interest to a large group of people and unlikely to attract the same amount of attention. Thus, a blind focus on citation metrics is likely to lead to research on general topics and avoid research that applies research to specific problems. The problem is clearly visible in research on prejudice, where the past 20 years have produced hundreds of studies with button-press tasks by White researchers with White participants that gobbled up funding that could have been used for BIBOC researchers to study the actual issues in BIPOC populations. In short, relevance and significance of research is very difficult to evaluate, but it is unlikely to be reflected in citation metrics. Thus, a danger is that metrics are being used because they are easy to measure and relevance is not being used because it is harder to measure.

Do Citation Metrics Reward Good or Bad Research?

The main justification for the use of citation metrics is the hypothesis that the wisdom of crowds will lead to more citations of high quality work.

“The argument in favor of personal judgments overlooks the fact that citation counts are also based on judgments by scholars. In the case of citation counts, however, those judgments are broadly derived from the whole scholarly community and are weighted by the scholars who are publishing about the topic of the cited publications. Thus, there is much to recommend citation
numbers in evaluating scholarly records.” (Radosic & Diener, 2021, p. 8).

This statement is out of touch with discussions about psychological science over the past decade in the wake of the replication crisis (see Schimmack, 2020, for a review; I have to cite myself to get up my citation metrics. LOL). In order to get published and cited, researchers of original research articles in psychological science need statistically significant p-values. The problem is that it can be difficult to find significant results when novel hypotheses are false or effect sizes are small. Given the pressure to publish in order to rise in the H-Index rankings, psychologists have learned to use a number of statistical tricks to get significant results in the absence of strong evidence in the data. These tricks are known as questionable research practices, but most researchers think they are acceptable (John et al., 2012). However, these practices undermine the value of significance testing and published results may be false positives or difficult to replicate, and do not add to the progress of science. Thus, citation metrics may have the negative consequence to pressure scientists into using bad practices and to reward scientists who publish more false results just because they publish more.

Meta-psychologists have produced strong evidence that the use of these practices was widespread and accounts for the majority of replication failures that occurred over the past decade.

Schimmack, U. (2020). A meta-psychological perspective on the decade of replication failures in social psychology. Canadian Psychology/Psychologie canadienne, 61(4), 364–376. https://doi.org/10.1037/cap0000246

Motyl et al. (2017) collected focal test statistics from a representative sample of articles in social psychology. I analyzed their data using z-curve.2.0 (Brunner & Schimmack, 2020; Bartos & Schimmack, 2021). Figure 1 shows the distribution of the test-statistics after converting them into absolute z-scores, where higher values show a higher signal/noise (effect size / sampling error) ratio. A z-score of 1.96 is needed to claim a discovery with p < .05 (two-sided). Consistent with publication practices since the 1960s, most focal hypothesis tests confirm predictions (Sterling, 1959). The observed discovery rate is 90% and even higher if marginally significant results are included (z > 1.65). This high success rate is not something to celebrate. Even I could win all marathons if I use a short-cut and run only 5km. The problem with this high success rate is clearly visible when we fit a model to the distribution of the significant z-scores and extrapolate the distribution of z-scores that are not significant (the blue curve in the figure). Based on this distribution, the significant results are only 19% of all tests, indicating that many more non-significant results are expected than observed. The discrepancy between the observed and estimated discovery rate provides some indication of the use of questionable research practices. Moreover, the estimated discovery rate shows how much statistical power studies have to produce significant results without questionable research practices. The results confirm suspicions that power in social psychology is abysmally low (Cohen, 1961; Tversky & Kahneman, 1971).

The use of questionable practices makes it possible that citation metrics may be invalid. When everybody in a research field uses p < .05 as a criterion to evaluate manuscripts and these p-values are obtained with questionable research practices, the system will reward researchers how use the most questionable methods to produce more questionable results than their peers. In other words, citation metrics are no longer a valid criterion of research quality. Instead, bad research is selected and rewarded (Smaldino & McElreath, 2016). However, it is also possible that implicit knowledge helps researchers to focus on robust results and that questionable research practices are not rewarded. For example, prediction markets suggest that it is fairly easy to spot shoddy research and to predict replication failures (Dreber et al., 2015). Thus, we cannot assume that citation metrics are valid or invalid. Instead, citation metrics – like all measures – require a program of construct validation.

Do Citation Metrics Take Statistical Power Into Account?

A few days ago, I published the first results of an ongoing research project that examines the relationship between researchers’ citation metrics and estimates of the average power of their studies based on z-curve analyses like the one shown in Figure 1 (see Schimmack, 2021, for details). The key finding is that there is no statistically or practically significant relationship between researchers H-Index and the average power of their studies. Thus, researchers who invest a lot of resources in their studies to produce results with a low false positive risk and high replicability are not cited more than researchers who flood journals with low powered studies that produce questionable results that are difficult to replicate.

These results show a major problem of citation metrics. Although methodologists have warned against underpowered studies, researchers have continued to use underpowered studies because they can use questionable practices to produce the desired outcome. This strategy is beneficial for scientists and their career, but hurts the larger goal of science to produce a credible body of knowledge. This does not mean that we need to abandon citation metrics altogether, but it must be complemented with other information that reflects the quality of researchers data.

The Power-Corrected H-Index

In my 2020 review article, I proposed to weight the H-Index by estimates of researchers’ replicability. For my illustration, I used the estimated replication rate, which is the average power of significant tests, p < .05 (Brunner & Schimmack, 2020). One advantage of the ERR is that it is highly reliable. The reliability of the ERRs for 300 social psychologists is .90. However, the ERR has some limitations. First, it predicts replication outcomes under the unrealistic assumption that psychological studies can be replicated exactly. However, it has been pointed out that this often impossible, especially in social psychology (Strobe & Strack, 2014). As a result, ERR predictions are overly optimistic and overestimate the success rate of actual replication studies (Bartos & Schimmack, 2021). In contrast, EDR estimates are much more in line with actual replication outcomes because effect sizes in replication studies can regress towards the mean. For example, Figure 1 shows an EDR of 19% for social psychology and the actual success rate (if we can call it that) for social psychology was 25% in the reproducibility project (Open Science Collaboration, 2015). Another advantage of the EDR is that it is sensitive to questionable research practices that tend to produce an abundance of p-values that are just significant. Thus, the EDR more strongly punishes researchers for using these undesirable practices. The main limitation of the EDR is that it is less reliable than the ERR. The reliability for 300 social psychologists was only .5. Of course, it is not necessary to chose between ERR and EDR. Just like there are many citation metrics, it is possible to evaluate the pattern of power-corrected metrics using ERR and EDR. I am presenting both values here, but the rankings are sorted by EDR weighted H-Indices.

The H-Index is an absolute number that can range from 0 to infinity. In contrast, power is limited to a range from 5% (with alpha = .05) to 100%. Thus, it makes sense to use power as a weight and to weight the H-index by a researchers EDR. A researcher who published only studies with 100% power has a power-corrected H-Index that is equivalent to the actual H-Index. The average EDR of social psychologists, however, is 35%. Thus, the average H-index is reduced to a third of the unadjusted value.

To illustrate this approach, I am using two researchers with a large H-Index, but different EDRs. One researcher is James J. Gross with an H-Index of 99 in WebofScience. His z-curve plot shows some evidence that questionable research practices were used to report 72% significant results with 50% power. However, the 95%CI around the EDR ranges from 23% to 78% and includes the point estimate. Thus, the evidence for QRPs is weak and not statistically significant. More important, the EDR -corrected H-Index is 90 * .50 = 45.

A different example is provided by Shelly E. Taylor with a similarly high H-Index of 84, but her z-curve plot shows clear evidence that the observed discovery rate is inflated by questionable research practices. Her low EDR reduces the H-Index considerably and results in a PC-H-Index of only 12.6.

Weighing the two researchers’ H-Index by their respective ERR’s, 77 vs. 54, has similar, but less extreme effects in absolute terms, ERR-adjusted H-Indices of 76 vs. 45.

In the sample of 300 social psychologists, the H-Index (r = .74) and the EDR (r = .65) contribute about equal amounts of variance to the power-corrected H-Index. Of course, a different formula could be used to weigh power more or less.

Discussion

Ed Diener is best known for his efforts to measure well-being and to point out that traditional economic indicators of well-being are imperfect. While wealth of countries is a strong predictor of citizens’ average well-being, r ~ .8, income is a poor predictor of individuals’ well-being with countries. However, economists continue to rely on income and GDP because it is more easily quantified and counted than subjective life-evaluations. Ironically, Diener advocates the opposite approach when it comes to measuring research quality. Counting articles and citations is relatively easy and objective, but it may not measure what we really want to measure, namely how much is somebody contributing to the advancement of knowledge. The construct of scientific advancement is probably as difficult to define as well-being, but producing replicable results with reproducible studies is one important criterion of good science. At present, citation metrics fail to track this indicator of research quality. Z-curve analyses of published results make it possible to measure this aspect of good science and I recommend to take it into account when researchers are being evaluated.

However, I do not recommend the use of quantitative information for the evaluation of hiring and promotion decisions. The reward system in science is too biased to reward privileged upper-class, White, US Americans (see APS rising stars lists). That being said, a close examination of published articles can be used to detect and eliminate researchers who severely p-hacked to get their significant results. Open science criteria can also be used to evaluate researchers who are just starting their career.

In conclusion, Radosic and Diener’s (2021) article disappointed me because it sidesteps the fundamental questions about the validity of citation metrics as a criterion for scientific excellence.

Conflict of Interest Statement: At the beginning of my career I was motivated to succeed in psychological science by publishing as many JPSP articles as possible and I made the unhealthy mistake to try to compete with Ed Diener. That didn’t work out for me. Maybe I am just biased against citation metrics because my work is not cited as much as I would like. Alternatively, my disillusionment with the system reflects some real problems with the reward structure in psychological science and helped me to see the light. The goal of science cannot be to have the most articles or the most citations, if these metrics do not really reflect scientific contributions. Chasing indicators is a trap, just like chasing happiness is a trap. Most scientists can hope to make maybe one lasting contribution to the advancement of knowledge. You need to please others to stay in the game, but beyond those minimum requirements to get tenure, personal criteria of success are better than social comparisons for the well-being of science and scientists. The only criterion that is healthy is to maximize statistical power. As Cohen said, less is more and by this criterion psychology is not doing well as more and more research is published with little concern about quality.

NameEDR.H.IndexERR.H.IndexH-IndexEDRERR
James J. Gross5076995077
John T. Cacioppo48701024769
Richard M. Ryan4661895269
Robert A. Emmons3940468588
Edward L. Deci3643695263
Richard W. Robins3440576070
Jean M. Twenge3335595659
William B. Swann Jr.3244555980
Matthew D. Lieberman3154674780
Roy F. Baumeister31531013152
David Matsumoto3133397985
Carol D. Ryff3136486476
Dacher Keltner3144684564
Michael E. McCullough3034446978
Kipling D. Williams3034446977
Thomas N Bradbury3033486369
Richard J. Davidson30551082851
Phoebe C. Ellsworth3033466572
Mario Mikulincer3045714264
Richard E. Petty3047744064
Paul Rozin2949585084
Lisa Feldman Barrett2948694270
Constantine Sedikides2844634570
Alice H. Eagly2843614671
Susan T. Fiske2849664274
Jim Sidanius2730426572
Samuel D. Gosling2733535162
S. Alexander Haslam2740624364
Carol S. Dweck2642663963
Mahzarin R. Banaji2553683778
Brian A. Nosek2546574481
John F. Dovidio2541663862
Daniel M. Wegner2434524765
Benjamin R. Karney2427376573
Linda J. Skitka2426327582
Jerry Suls2443633868
Steven J. Heine2328376377
Klaus Fiedler2328386174
Jamil Zaki2327356676
Charles M. Judd2336534368
Jonathan B. Freeman2324307581
Shinobu Kitayama2332455071
Norbert Schwarz2235564063
Antony S. R. Manstead2237593762
Patricia G. Devine2125375867
David P. Schmitt2123307177
Craig A. Anderson2132593655
Jeff Greenberg2139732954
Kevin N. Ochsner2140573770
Jens B. Asendorpf2128415169
David M. Amodio2123336370
Bertram Gawronski2133434876
Fritz Strack2031553756
Virgil Zeigler-Hill2022277481
Nalini Ambady2032573556
John A. Bargh2035633155
Arthur Aron2036653056
Mark Snyder1938603263
Adam D. Galinsky1933682849
Tom Pyszczynski1933613154
Barbara L. Fredrickson1932523661
Hazel Rose Markus1944642968
Mark Schaller1826434361
Philip E. Tetlock1833454173
Anthony G. Greenwald1851613083
Ed Diener18691011868
Cameron Anderson1820276774
Michael Inzlicht1828444163
Barbara A. Mellers1825325678
Margaret S. Clark1823305977
Ethan Kross1823345267
Nyla R. Branscombe1832493665
Jason P. Mitchell1830414373
Ursula Hess1828404471
R. Chris Fraley1828394572
Emily A. Impett1819257076
B. Keith Payne1723305876
Eddie Harmon-Jones1743622870
Wendy Wood1727434062
John T. Jost1730493561
C. Nathan DeWall1728453863
Thomas Gilovich1735503469
Elaine Fox1721276278
Brent W. Roberts1745592877
Harry T. Reis1632433874
Robert B. Cialdini1629513256
Phillip R. Shaver1646652571
Daphna Oyserman1625463554
Russell H. Fazio1631503261
Jordan B. Peterson1631394179
Bernadette Park1624384264
Paul A. M. Van Lange1624384263
Jeffry A. Simpson1631572855
Russell Spears1529522955
A. Janet Tomiyama1517236576
Jan De Houwer1540552772
Samuel L. Gaertner1526423561
Michael Harris Bond1535423584
Agneta H. Fischer1521314769
Delroy L. Paulhus1539473182
Marcel Zeelenberg1429373979
Eli J. Finkel1426453257
Jennifer Crocker1432483067
Steven W. Gangestad1420483041
Michael D. Robinson1427413566
Nicholas Epley1419265572
David M. Buss1452652280
Naomi I. Eisenberger1440512879
Andrew J. Elliot1448712067
Steven J. Sherman1437592462
Christian S. Crandall1421363959
Kathleen D. Vohs1423453151
Jamie Arndt1423453150
John M. Zelenski1415206976
Jessica L. Tracy1423324371
Gordon B. Moskowitz1427472957
Klaus R. Scherer1441522678
Ayelet Fishbach1321363759
Jennifer A. Richeson1321403352
Charles S. Carver1352811664
Leaf van Boven1318274767
Shelley E. Taylor1244841452
Lee Jussim1217245271
Edward R. Hirt1217264865
Shigehiro Oishi1232522461
Richard E. Nisbett1230432969
Kurt Gray1215186981
Stacey Sinclair1217304157
Niall Bolger1220343658
Paula M. Niedenthal1222363461
Eliot R. Smith1231422973
Tobias Greitemeyer1221313967
Rainer Reisenzein1214215769
Rainer Banse1219264672
Galen V. Bodenhausen1228462661
Ozlem Ayduk1221353459
E. Tory. Higgins1238701754
D. S. Moskowitz1221333663
Dale T. Miller1225393064
Jeanne L. Tsai1217254667
Roger Giner-Sorolla1118225180
Edward P. Lemay1115195981
Ulrich Schimmack1122353263
E. Ashby Plant1118363151
Ximena B. Arriaga1113195869
Janice R. Kelly1115225070
Frank D. Fincham1135601859
David Dunning1130432570
Boris Egloff1121372958
Karl Christoph Klauer1125392765
Caryl E. Rusbult1019362954
Tessa V. West1012205159
Jennifer S. Lerner1013224661
Wendi L. Gardner1015244263
Mark P. Zanna1030621648
Michael Ross1028452262
Jonathan Haidt1031432373
Sonja Lyubomirsky1022382659
Sander L. Koole1018352852
Duane T. Wegener1016273660
Marilynn B. Brewer1027442262
Christopher K. Hsee1020313163
Sheena S. Iyengar1015195080
Laurie A. Rudman1026382568
Joanne V. Wood916263660
Thomas Mussweiler917392443
Shelly L. Gable917332850
Felicia Pratto930402375
Wiebke Bleidorn920273474
Jeff T. Larsen917253667
Nicholas O. Rule923303075
Dirk Wentura920312964
Klaus Rothermund930392376
Joris Lammers911165669
Stephanie A. Fryberg913194766
Robert S. Wyer930471963
Mina Cikara914184980
Tiffany A. Ito914224064
Joel Cooper914352539
Joshua Correll914233862
Peter M. Gollwitzer927461958
Brad J. Bushman932511762
Kennon M. Sheldon932481866
Malte Friese915263357
Dieter Frey923392258
Lorne Campbell914233761
Monica Biernat817292957
Aaron C. Kay814283051
Yaacov Schul815233664
Joseph P. Forgas823392159
Guido H. E. Gendolla814302747
Claude M. Steele813312642
Igor Grossmann815233566
Paul K. Piff810165063
Joshua Aronson813282846
William G. Graziano820302666
Azim F. Sharif815223568
Juliane Degner89126471
Margo J. Monteith818243277
Timothy D. Wilson828451763
Kerry Kawakami813233356
Hilary B. Bergsieker78116874
Gerald L. Clore718391945
Phillip Atiba Goff711184162
Elizabeth W. Dunn717262864
Bernard A. Nijstad716312352
Mark J. Landau713282545
Christopher R. Agnew716213376
Brandon J. Schmeichel714302345
Arie W. Kruglanski728491458
Eric D. Knowles712183864
Yaacov Trope732571257
Wendy Berry Mendes714312244
Jennifer S. Beer714252754
Nira Liberman729451565
Penelope Lockwood710144870
Jeffrey W Sherman721292371
Geoff MacDonald712183767
Eva Walther713193566
Daniel T. Gilbert727411665
Grainne M. Fitzsimons611232849
Elizabeth Page-Gould611164066
Mark J. Brandt612173770
Ap Dijksterhuis620371754
James K. McNulty621331965
Dolores Albarracin618331956
Maya Tamir619292164
Jon K. Maner622431452
Alison L. Chasteen617252469
Jay J. van Bavel621302071
William A. Cunningham619302064
Glenn Adams612173573
Wilhelm Hofmann622331866
Ludwin E. Molina67124961
Lee Ross626421463
Andrea L. Meltzer69134572
Jason E. Plaks610153967
Ara Norenzayan621341761
Batja Mesquita617232573
Tanya L. Chartrand69282033
Toni Schmader518301861
Abigail A. Scholer59143862
C. Miguel Brendl510153568
Emily Balcetis510153568
Diana I. Tamir59153562
Nir Halevy513182972
Alison Ledgerwood58153454
Yoav Bar-Anan514182876
Paul W. Eastwick517242169
Geoffrey L. Cohen513252050
Yuen J. Huo513163180
Benoit Monin516291756
Gabriele Oettingen517351449
Roland Imhoff515212373
Mark W. Baldwin58202441
Ronald S. Friedman58192544
Shelly Chaiken522431152
Kristin Laurin59182651
David A. Pizarro516232069
Michel Tuan Pham518271768
Amy J. C. Cuddy517241972
Gun R. Semin519301564
Laura A. King419281668
Yoel Inbar414202271
Nilanjana Dasgupta412231952
Kerri L. Johnson413172576
Roland Neumann410152867
Richard P. Eibach410221947
Roland Deutsch416231871
Michael W. Kraus413241755
Steven J. Spencer415341244
Gregory M. Walton413291444
Ana Guinote49202047
Sandra L. Murray414251655
Leif D. Nelson416251664
Heejung S. Kim414251655
Elizabeth Levy Paluck410192155
Jennifer L. Eberhardt411172362
Carey K. Morewedge415231765
Lauren J. Human49133070
Chen-Bo Zhong410211849
Ziva Kunda415271456
Geoffrey J. Leonardelli46132848
Danu Anthony Stinson46113354
Kentaro Fujita411182062
Leandre R. Fabrigar414211767
Melissa J. Ferguson415221669
Nathaniel M Lambert314231559
Matthew Feinberg38122869
Sean M. McCrea38152254
David A. Lishner38132563
William von Hippel313271248
Joseph Cesario39191745
Martie G. Haselton316291154
Daniel M. Oppenheimer316261260
Oscar Ybarra313241255
Simone Schnall35161731
Travis Proulx39141962
Spike W. S. Lee38122264
Dov Cohen311241144
Ian McGregor310241140
Dana R. Carney39171553
Mark Muraven310231144
Deborah A. Prentice312211257
Michael A. Olson211181363
Susan M. Andersen210211148
Sarah E. Hill29171352
Michael A. Zarate24141331
Lisa K. Libby25101854
Hans Ijzerman2818946
James M. Tyler1681874
Fiona Lee16101358

References

Open Science Collaboration (OSC). (2015). Estimating the reproducibility
of psychological science. Science, 349, aac4716. http://dx.doi.org/10
.1126/science.aac4716

Radosic, N., & Diener, E. (2021). Citation Metrics in Psychological Science. Perspectives on Psychological Science. https://doi.org/10.1177/1745691620964128

Schimmack, U. (2021). The validation crisis. Meta-psychology. in press

Schimmack, U. (2020). A meta-psychological perspective on the decade of replication failures in social psychology. Canadian Psychology/Psychologie canadienne, 61(4), 364–376. https://doi.org/10.1037/cap0000246

2 thoughts on “The Power-Corrected H-Index

  1. I think it’s useful to revisit the meaningfulness of the h-index, particularly because it’s fast becoming ubiquitous. However, I have a couple of questions.

    1. Surely there’s a case for some kind of career length normalisation since more experienced researchers have more opportunity to publish and their papers are exposed for longer to the opportunity to be cited? This might inform your plot of H-Index and the average power of their studies. Also more recently trained researchers might have better-powered studies or vice versa?

    2.A non-trivial proportion of papers might not be experiments, e.g., case studies, theory, etc. This would certainly be the case in my field, i.e., computer science.

    Thanks – Martin

    Like

    1. Hi Martin, thanks for your comment. Statistical power only applies to some research areas. Even within the social sciences there can be sciences that focus on effect size estimation, where power would not apply. Adjusting the H-Index for age would make sense if we want to compare researchers at different career stages. We might however also have to adjust for cohort because it is much easier to publish today than it was just 20 years ago.

      Like

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