Category Archives: Replicability Ranking

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Replicability Ranking of Psychology Departments

Evaluations of individual researchers, departments, and universities are common and arguably necessary as science is becoming bigger. Existing rankings are based to a large extent on peer-evaluations. A university is ranked highly if peers at other universities perceive it to produce a steady stream of high-quality research. At present the most widely used objective measures rely on the quantity of research output and on the number of citations. These quantitative indicators of research quality work are also heavily influenced by peers because peer-review controls what gets published, especially in journals with high rejection rates, and peers decide what research they cite in their own work. The social mechanisms that regulate peer-approval are unavoidable in a collective enterprise like science that does not have a simple objective measure of quality (e.g., customer satisfaction ratings, or accident rates of cars). Unfortunately, it is well known that social judgments are subject to many biases due to conformity pressure, self-serving biases, confirmation bias, motivated biases, etc. Therefore, it is desirable to complement peer-evaluations with objective indicators of research quality.

Some aspects of research quality are easier to measure than others. Replicability rankings focus on one aspect of research quality that can be measured objectively, namely the replicability of a published significant result. In many scientific disciplines such as psychology, a successful study reports a statistically significant result. A statistically significant result is used to minimize the risk of publishing evidence for an effect that does not exist (or even goes in the opposite direction). For example, a psychological study that shows effectiveness of a treatment for depression would have to show that the effect in the study reveals a real effect that can be observed in other studies and in real patients if the treatment is used for the treatment of depression.

In a science that produces thousands of results a year, it is inevitable that some of the published results are fluke findings (even Toyota’s break down sometimes). To minimize the risk of false results entering the literature, psychology like many other sciences, adopted a 5% error rate. By using a 5% as the criterion, psychologists ensured that no more than 5% of results are fluke findings. With thousands of results published in each year, this still means that more than 50 false results enter the literature each year. However, this is acceptable because a single study does not have immediate consequences. Only if these results are replicated in other studies, findings become the foundation of theories and may influence practical decisions in therapy or in other applications of psychological findings (at work, in schools, or in policy). Thus, to outside observers it may appear safe to trust published results in psychology and to report about these findings in newspaper articles, popular books, or textbooks.

Unfortunately, it would be a mistake to interpret a significant result in a psychology journal as evidence that the result is probably true.  The reason is that the published success rate in journals has nothing to do with the actual success rate in psychological laboratories. All insiders know that it is common practice to report only results that support a researcher’s theory. While outsiders may think of scientists as neutral observers (judges), insiders play the game of lobbyist, advertisers, and self-promoters. The game is to advance one’s theory, publish more than others, get more citations than others, and win more grant money than others. Honest reporting of failed studies does not advance this agenda. As a result, the fact that psychological studies report nearly exclusively success stories (Sterling, 1995; Sterling et al., 1995) tells outside observers nothing about the replicability of a published finding and the true rate of fluke findings could be 100%.

This problem has been known for over 50 years (Cohen, 1962; Sterling, 1959). So it would be wrong to call the selective reporting of successful studies an acute crisis. However, what changed is that some psychologists have started to criticize the widely accepted practice of selective reporting of successful studies (Asendorpf et al., 2012; Francis, 2012; Simonsohn et al., 2011; Schimmack, 2012; Wagenmakers et al., 2011). Over the past five years, psychologists, particularly social psychologists, have been engaged in heated arguments over the so-called “replication crisis.”

One group argues that selective publishing of successful studies occurred, but without real consequences on the trustworthiness of published results. The other group argues that published results cannot be trusted unless they have been successfully replicated. The problem is that neither group has objective information about the replicability of published results.  That is, there is no reliable estimate of the percentage of studies that would produce a significant result again, if a representative sample of significant results published in psychology journals were replicated.

Evidently, it is not possible to conduct exact replication studies of all studies that have been published in the past 50 years. Fortunately, it is not necessary to conduct exact replication studies to obtain an objective estimate of replicability. The reason is that replicability of exact replication studies is a function of the statistical power of studies (Sterling et al., 1995). Without selective reporting of results, a 95% success rate is an estimate of the statistical power of the studies that achieved this success rate. Vice versa, a set of studies with average power of 50% is expected to produce a success rate of 50% (Sterling, et al., 1995).

Although selection bias renders success rates uninformative, the actual statistical results provide valuable information that can be used to estimate the unbiased statistical power of published results. Although selection bias inflates effect sizes and power, Brunner and Schimmack (forcecoming) developed and validated a method that can correct for selection bias. This method makes it possible to estimate the replicability of published significant results on the basis of the original reported results. This statistical method was used to estimate the replicabilty of research published by psychology departments in the years from 2010 to 2015 (see Methodology for details).

The averages for the 2010-2012 period (M = 59) and the 2013-2015 period (M = 61) show only a small difference, indicating that psychologists have not changed their research practices in accordance with recommendations to improve replicability in 2011  (Simonsohn et al., 2011). For most of the departments the confidence intervals for the two periods overlap (see attached powergraphs). Thus, the more reliable average across all years is used for the rankings, but the information for the two time periods is presented as well.

There are no obvious predictors of variability across departments. Private universities are at the top (#1, #2, #8), the middle (#24, #26), and at the bottom (#44, #47). European universities can also be found at the top (#4, #5), middle (#25) and bottom (#46, #51). So are Canadian universities (#9, #15, #16, #18, #19, #50).

There is no consensus on an optimal number of replicability.  Cohen recommended that researchers should plan studies with 80% power to detect real effects. If 50% of studies tested real effects with 80% power and the other 50% tested a null-hypothesis (no effect = 2.5% probability to replicate a false result again), the estimated power for significant results would be 78%. The effect on average power is so small because most of the false predictions produce a non-significant result. As a result, only a few studies with low replication probability dilute the average power estimate. Thus, a value greater than 70 can be considered broadly in accordance with Cohen’s recommendations.

It is important to point out that the estimates are very optimistic estimates of the success rate in actual replications of theoretically important effects. For a representative set of 100 studies (OSC, Science, 2015), Brunner and Schimmack’s statistical approach predicted a success rate of 54%, but the success rate in actual replication studies was only 37%. One reason for this discrepancy could be that the statistical approach assumes that the replication studies are exact, but actual replications always differ in some ways from the original studies, and this uncontrollable variability in experimental conditions posses another challenge for replicability of psychological results.  Before further validation research has been completed, the estimates can only be used as a rough estimate of replicability. However, the absolute accuracy of estimates is not relevant for the relative comparison of psychology departments.

And now, without further ado, the first objective rankings of 51 psychology departments based on the replicability of published significant results. More departments will be added to these rankings as the results become available.

Rank University 2010-2015 2010-2012 2013-2015
1 U Penn 72 69 75
2 Cornell U 70 67 72
3 Purdue U 69 69 69
4 Tilburg U 69 71 66
5 Humboldt U Berlin 67 68 66
6 Carnegie Mellon 67 67 67
7 Princeton U 66 65 67
8 York U 66 63 68
9 Brown U 66 71 60
10 U Geneva 66 71 60
11 Northwestern U 65 66 63
12 U Cambridge 65 66 63
13 U Washington 65 70 59
14 Carleton U 65 68 61
15 Queen’s U 63 57 69
16 U Texas – Austin 63 63 63
17 U Toronto 63 65 61
18 McGill U 63 72 54
19 U Virginia 63 61 64
20 U Queensland 63 66 59
21 Vanderbilt U 63 61 64
22 Michigan State U 62 57 67
23 Harvard U 62 64 60
24 U Amsterdam 62 63 60
25 Stanford U 62 65 58
26 UC Davis 62 57 66
27 UCLA 61 61 61
28 U Michigan 61 63 59
29 Ghent U 61 58 63
30 U Waterloo 61 65 56
31 U Kentucky 59 58 60
32 Penn State U 59 63 55
33 Radboud U 59 60 57
34 U Western Ontario 58 66 50
35 U North Carolina Chapel Hill 58 58 58
36 Boston University 58 66 50
37 U Mass Amherst 58 52 64
38 U British Columbia 57 57 57
39 The University of Hong Kong 57 57 57
40 Arizona State U 57 57 57
41 U Missouri 57 55 59
42 Florida State U 56 63 49
43 New York U 55 55 54
44 Dartmouth College 55 68 41
45 U Heidelberg 54 48 60
46 Yale U 54 54 54
47 Ohio State U 53 58 47
48 Wake Forest U 51 53 49
49 Dalhousie U 50 45 55
50 U Oslo 49 54 44
51 U Kansas 45 45 44

 

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Dr. R’s Blog about Replicability

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For generalization, psychologists must finally rely, as has been done in all the older sciences, on replication (Cohen, 1994).

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DEFINITION OF REPLICABILITY:  In empirical studies with random error variance replicability refers to the probability of a study with a significant result to produce a significant result again in an exact replication study of the first study using the same sample size and significance criterion.

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New (February, 2, 2016)
Reconstruction of a Train Wreck: How Priming Research Went off the Rails

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REPLICABILITY REPORTS:  Examining the replicability of research topics

RR No1. (April 19, 2016)  Is ego-depletion a replicable effect? 
RR No2. (May 21, 2016) Do mating primes have replicable effects on behavior?

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TOP TEN LIST

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1. 2015 Replicability Rankings of over 100 Psychology Journals
Based on reported test statistics in all articles from 2015, the rankings show the typical strength of evidence for a statistically significant result in particular journals.  The method also estimates the file-drawer of unpublished non-significant results. Links to powergraphs provide further information (e.g., whether a journal has too many just significant results (p .025).

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2. A (preliminary) Introduction to the Estimation of Replicability for Sets of Studies with Heterogeneity in Power (e.g., Journals, Departments, Labs)
This post presented the first replicability ranking and explains the methodology that is used to estimate the typical power of a significant result published in a journal.  The post provides an explanation of the new method to estimate observed power based on the distribution of test statistics converted into absolute z-scores.  The method has been developed further to estimate power for a wider range of z-scores by developing a model that allows for heterogeneity in power across tests.  A description of the new method will be published when extensive simulation studies are completed.

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3.  Replicability-Rankings of Psychology Departments
This blog presents rankings of psychology departments on the basis of the replicability of significant results published in 105 psychology journals (see the journal rankings for a list of journals).   Reported success rates in psychology journals are over 90%, but this percentage is inflated by selective reporting of significant results.  After correcting for selection bias, replicability is 60%, but there is reliable variation across departments.

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4. An Introduction to the R-Index
The R-Index can be used to predict whether a set of published results will replicate in a set of exact replication studies. It combines information about the observed power of the original studies with information about the amount of inflation in observed power due to publication bias (R-Index = Observed Median Power – Inflation). The R-Index has predicted the outcome of actual replication studies.

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5.  The Test of Insufficient Variance (TIVA)
The Test of Insufficient Variance is the most powerful test of publication bias and/or dishonest reporting practices. It can be used even if only two independent statistical results are available, although power to detect bias increases with the number of studies. After converting test results into z-scores, z-scores are expected to have a variance of one.   Unless power is very high, some of these z-scores will not be statistically significant (z .05 two-tailed).  If these non-significant results are missing, the variance shrinks, and TIVA detects that the variance is insufficient.  The observed variance is compared against the expected variance of 1 with a left-tailed chi-square test. The usefulness of TIVA is illustrated with Bem’s (2011) “Feeling the Future” data.

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6.  Validation of Meta-Analysis of Observed (post-hoc) Power
This post examines the ability of various estimation methods to estimate power of a set of studies based on the reported test statistics in these studies.  The results show that most estimation methods work well when all studies have the same effect size (homogeneous) or if effect sizes are heterogeneous and symmetrically distributed (heterogeneous). However, most methods fail when effect sizes are heterogeneous and have a skewed distribution.  The post does not yet include the more recent method that uses the distribution of z-scores (powergraphs) to estimate observe power because this method was developed after this blog was posted.

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7. Roy Baumeister’s R-Index
Roy Baumeister was a reviewer of my 2012 article that introduced the Incredibiliy Index to detect publication bias and dishonest reporting practices.  In his review and in a subsequent email exchange, Roy Baumeister admitted that his published article excluded studies that failed to produce results in support of his theory that blood-glucose is important for self-regulation (a theory that is now generally considered to be false), although he disagrees that excluding these studies was dishonest.  The R-Index builds on the incredibility index and provides an index of the strength of evidence that corrects for the influence of dishonest reporting practices.  This post reports the R-Index for Roy Baumeister’s most cited articles. The R-Index is low and does not justify the nearly perfect support for empirical predictions in these articles. At the same time, the R-Index is similar to R-Indices for other sets of studies in social psychology.  This suggests that dishonest reporting practices are the norm in social psychology and that published articles exaggerate the strength of evidence in support of social psychological theories.

http://schoolsnapshots.org/blog/2014/09/30/math-prize-for-girls-at-m-i-t/8. How robust are Stereotype-Threat Effects on Women’s Math Performance?
Stereotype-threat has been used by social psychologists to explain gender differences in math performance. Accordingly, the stereotype that men are better at math than women is threatening to women and threat leads to lower performance.  This theory has produced a large number of studies, but a recent meta-analysis showed that the literature suffers from publication bias and dishonest reporting.  After correcting for these effects, the stereotype-threat effect was negligible.  This blog post shows a low R-Index for the first article that appeared to provide strong support for stereotype-threat.  These results show that the R-Index can warn readers and researchers that reported results are too good to be true.

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9.  The R-Index for 18 Multiple-Study Psychology Articles in the Journal SCIENCE.
Francis (2014) demonstrated that nearly all multiple-study articles by psychology researchers that were published in the prestigious journal SCIENCE showed evidence of dishonest reporting practices (disconfirmatory evidence was missing).  Francis (2014) used a method similar to the incredibility index.  One problem of this method is that the result is a probability that is influenced by the amount of bias and the number of results that were available for analysis. As a result, an article with 9 studies and moderate bias is treated the same as an article with 4 studies and a lot of bias.  The R-Index avoids this problem by focusing on the amount of bias (inflation) and the strength of evidence.  This blog post shows the R-Index of the 18 studies and reveals that many articles have a low R-Index.

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10.  The Problem with Bayesian Null-Hypothesis Testing
Some Bayesian statisticians have proposed Bayes-Factors to provide evidence for a Null-Hypothesis (i.e., there is no effect).  They used Bem’s (2011) “Feeling the Future” data to argue that Bayes-Factors would have demonstrated that extra-sensory perception does not exist.  This blog post shows that Bayes-Factors depend on the specification of the alternative hypothesis and that support for the null-hypothesis is often obtained by choosing an unrealistic alternative hypothesis (e.g., there is a 25% probability that effect size is greater than one standard deviation, d > 1).  As a result, Bayes-Factors can favor the null-hypothesis when there is an effect, but the effect size is small (d = .2).  A Bayes-Factor in favor of the null is more appropriately interpreted as evidence that the alternative hypothesis needs to decrease the probabilities assigned to large effect sizes. The post also shows that Bayes-Factors based on a meta-analysis of Bem’s data provide misleading evidence that an effect is present because Bayesian statistics do not take publication bias and dishonest reporting practices into account.

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2015 Replicability Ranking of 100+ Psychology Journals

Replicability rankings of psychology journals differs from traditional rankings based on impact factors (citation rates) and other measures of popularity and prestige. Replicability rankings use the test statistics in the results sections of empirical articles to estimate the average power of statistical tests in a journal. Higher average power means that the results published in a journal have a higher probability to produce a significant result in an exact replication study and a lower probability of being false-positive results.

The rankings are based on statistically significant results only (p < .05, two-tailed) because only statistically significant results can be used to interpret a result as evidence for an effect and against the null-hypothesis.  Published non-significant results are useful for meta-analysis and follow-up studies, but they provide insufficient information to draw statistical inferences.

The average power across the 105 psychology journals used for this ranking is 70%. This means that a representative sample of significant results in exact replication studies is expected to produce 70% significant results. The rankings for 2015 show variability across journals with average power estimates ranging from 84% to 54%.  A factor analysis of annual estimates for 2010-2015 showed that random year-to-year variability accounts for 2/3 of the variance and that 1/3 is explained by stable differences across journals.

The Journal Names are linked to figures that show the powergraphs of a journal for the years 2010-2014 and 2015. The figures provide additional information about the number of tests used, confidence intervals around the average estimate, and power estimates that estimate power including non-significant results even if these are not reported (the file-drawer).

Rank   Journal 2010/14 2015
1   Social Indicators Research   81   84
2   Journal of Happiness Studies   81   83
3   Journal of Comparative Psychology   72   83
4   International Journal of Psychology   80   81
5   Journal of Cross-Cultural Psychology   78   81
6   Child Psychiatry and Human Development   75   81
7   Psychonomic Review and Bulletin   72   80
8   Journal of Personality   72   79
9   Journal of Vocational Behavior   79   78
10   British Journal of Developmental Psychology   75   78
11   Journal of Counseling Psychology   72   78
12   Cognitve Development   69   78
13   JPSP: Personality Processes
and Individual Differences
  65   78
14   Journal of Research in Personality   75   77
15   Depression & Anxiety   74   77
16   Asian Journal of Social Psychology   73   77
17   Personnel Psychology   78   76
18   Personality and Individual Differences   74   76
19   Personal Relationships   70   76
20   Cognitive Science   77   75
21   Memory and Cognition   73   75
22   Early Human Development   71   75
23   Journal of Sexual Medicine   76   74
24   Journal of Applied Social Psychology   74   74
25   Journal of Experimental Psychology: Learning, Memory & Cognition   74   74
26   Journal of Youth and Adolescence   72   74
27   Social Psychology   71   74
28   Journal of Experimental Psychology: Human Perception and Performance   74   73
29   Cognition and Emotion   72   73
30   Journal of Affective Disorders   71   73
31   Attention, Perception and Psychophysics   71   73
32   Evolution & Human Behavior   68   73
33   Developmental Science   68   73
34   Schizophrenia Research   66   73
35   Achive of Sexual Behavior   76   72
36   Pain   74   72
37    Acta Psychologica   72   72
38   Cognition   72   72
39   Journal of Experimental Child Psychology   72   72
40   Aggressive Behavior   72   72
41   Journal of Social Psychology   72   72
42   Behaviour Research and Therapy   70   72
43   Frontiers in Psychology   70   72
44   Journal of Autism and Developmental Disorders   70   72
45   Child Development   69   72
46   Epilepsy & Behavior   75   71
47   Journal of Child and Family Studies   72   71
48   Psychology of Music   71   71
49   Psychology and Aging   71   71
50   Journal of Memory and Language   69   71
51   Journal of Experimental Psychology: General   69   71
52   Psychotherapy   78   70
53   Developmental Psychology   71   70
54   Behavior Therapy   69   70
55   Judgment and Decision Making   68   70
56   Behavioral Brain Research   68   70
57   Social Psychology and Personality Science   62   70
58   Political Psychology   75   69
59   Cognitive Psychology   74   69
60   Organizational Behavior and Human Decision Processes   69   69
61   Appetite   69   69
62   Motivation and Emotion   69   69
63   Sex Roles   68   69
64   Journal of Experimental Psychology: Applied   68   69
65   Journal of Applied Psychology   67   69
66   Behavioral Neuroscience   67   69
67   Psychological Science   67   68
68   Emotion   67   68
69   Developmental Psychobiology   66   68
70   European Journal of Social Psychology   65   68
71   Biological Psychology   65   68
72   British Journal of Social Psychology   64   68
73   JPSP: Attitudes & Social Cognition   62   68
74   Animal Behavior   69   67
75   Psychophysiology   67   67
76   Journal of Child Psychology and Psychiatry and Allied Disciplines   66   67
77   Journal of Research on Adolescence   75   66
78   Journal of Educational Psychology   74   66
79   Clinical Psychological Science   69   66
80   Consciousness and Cognition   69   66
81   The Journal of Positive Psychology   65   66
82   Hormones & Behavior   64   66
83   Journal of Clinical Child and
Adolescence Psychology
  62   66
84   Journal of Gerontology: Series B   72   65
85   Psychological Medicine   66   65
86   Personalit and Social Psychology
Bulletin
  64   64
87   Infancy   61   64
88   Memory   75   63
89   Law and Human Behavior   70   63
90   Group Processes & Intergroup Relations   70   63
91   Journal of Social and Personal Relationships   69   63
92   Cortex   67   63
93   Journal of Abnormal Psychology   64   63
94   Journal of Consumer Psychology   60   63
95   Psychology of Violence   71   62
96   Psychoneuroendocrinology   63   62
97   Health Psychology   68   61
98   Journal of Experimental Social
Psychology
  59   61
99   JPSP: Interpersonal Relationships
and Group Processes
  60   60
100   Social Cognition   65   59
101   Journal of Consulting and Clinical Psychology   63   58
102   European Journal of Personality   72   57
103   Journal of Family Psychology   60   57
104   Social Development   75   55
105   Annals of Behavioral Medicine   65   54
106   Self and Identity   63   54
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Replicability-Report for PSYCHOLOGY & AGING

PSYCHOLOGY & AGING is published by the American Psychological Association (APA). The journal started publishing articles in 1986.

SCImago ranks PSYCHOLOGY & AGING as #81 of all psychology journals with an SJR-Impact-Factor of 1.8 in 2014. At present, the replicability-report is based on articles published from 2000 to 2015. During this time, PSYCHOLOGY & AGING published 1334 articles. The replicability-report is based on 1017 articles that reported one or more t or F-test in the text of the results section (results reported in Figures or Tables are not included).  The test-statistic was converted into z-scores to estimate post-hoc-power.  The analysis is based on 7,390 z-scores in the range from 2 (just above the 1.96 criterion value for p < .05 (two-tailed)) to 4.

PHP-Curve PsyAge

Based on the distribution of z-scores in the range between 2 and 4, the average power for significant results in this range is estimated to be 68% with the homogeneous model that is currently being used for the replicability rankings. The heterogeneous model fits the actual data better and produces an estimate of 61% power in this range.  Power for all significant results is estimated to be 74%.  A power estimate of 68% implies that 68% of the published significant results in this range are predicted to produce a significant results in an exact replication study with the same sample size and power (results with z > 4 are expected to replicate with nearly 100%).

The same method was used to estimate power for individual years.

PHP-Trend PsyAgeThe time trend shows  a slight decline in power over time. Due to the large number of observations in each year, annual averages are close to the trend line, indicating that annual estimates are fairly precise.  The average for the years 2010-2014 is 68%, which places Psychology and Aging above average in the replicabilty rankings of psychological journals.  However, in the past three years power was below the historic average, indicating that the journal has not yet responded to the replicability crisis in psychology.  Psychology and Aging should aim to increase power in the future.