In the past five years, it has become apparent that many classic and important findings in social psychology fail to replicate (Schimmack, 2016). The replication crisis is often considered a new phenomenon, but failed replications are not entirely new. Sometimes these studies have simply been ignored. These studies deserve more attention and need to be reevaluated in the context of the replication crisis in social psychology.
In the past, failed replications were often dismissed because seminal articles were assumed to provide robust empirical support for a phenomenon, especially if an article presented multiple studies. The chance of reporting a false positive results in a multiple study article is low because the risk of a false positive decreases exponentially (Schimmack, 2012). However, the low risk of a false positive is illusory if authors only publish studies that worked. In this case, even false positives can be supported by significant results in multiple studies, as demonstrated in the infamous ESP study by Bem (2011). As a result, publication bias undermines the reporting of statistical significance as diagnostic information about the risk of false positives (Sterling, 1959) and many important theories in social psychology rest on shaky empirical foundations that need to be reexamined.
Research on stereotype threat and women’s performance on math tests is one example where publication bias undermines the findings in a seminal study that produced a large literature of studies on gender differences in math performance. After correcting for publication bias, this literature shows very little evidence that stereotype threat has a notable and practically significant effect on women’s math performance (Flore & Wicherts, 2014).
Another important line of research has examined the contribution of stereotype threat to differences between racial groups on academic performance tests. This blog post examines the strength of the empirical evidence for stereotype threat effects in the seminal article by Steele and Aronson (1995). This article is currently the 12th most cited article in the top journal for social psychology, Journal of Personality and Social Psychology (2,278 citations so far).
According to the abstract, “stereotype threat is being at risk of confirming, as self-characteristic, a negative stereotype about one’s group.” Studies 1 and 2 showed that “reflecting the pressure of this vulnerability, Blacks underperformed in relation to Whites in the ability-diagnostic condition but not in the nondiagnostic condition (with Scholastic Aptitude Tests controlled).” “Study 3 validated that ability-diagnosticity cognitively activated the racial stereotype in these participants and motivated them not to conform to it, or to be judged by it.” “Study 4 showed that mere salience of the stereotype could impair Blacks’ performance even when the test was not
The results of Study 4 motivated Stricker and colleagues to examine the influence of stereotype-treat on test performance in a real-world testing situation. These studies had large samples and were not limited to students at Stanford. One study was reported in a College Board Report (Stricker and Ward, 1998). Another two studies were published in the Journal of Applied Social Psychology (Stricker & Ward, 2004). This article received only 52 citations, although it reported two studies with an experimental manipulation of stereotype threat in a real assessment context. One group of participants were asked about their gender or ethnicity before the text, the other group did not receive these questions. As noted in the abstract, neither the inquiry about race, nor about gender, had a significant effect on test performance. In short, this study failed to replicate Study 4 of the classic and widely cited article by Steele and Aronson.
Stricker and Ward’s Abstract
Steele and Aronson (1995) found that the performance of Black research participants on
ability test items portrayed as a problem-solving task, in laboratory experiments, was affected adversely when they were asked about their ethnicity. This outcome was attributed to stereotype threat: Performance was disrupted by participants’ concerns about fulfilling the negative stereotype concerning Black people’s intellectual ability. The present field experiments extended that research to other ethnic groups and to males and females taking operational tests. The experiments evaluated the effects of inquiring about ethnicity and gender on the performance of students taking 2 standardized tests-the Advanced Placement Calculus AB Examination, and the Computerized Placement Tests-in actual test administrations. This inquiry did not have any effects on the test performance of Black, female, or other subgroups of students that were both statistically and practically significant.
The article also mentions a personal communication with Steele, in which Steele mentions an unpublished study that also failed to demonstrate the effect under similar conditions.
“In fact, Steele found in an unpublished pilot study that inquiring about ethnicity did not affect Black participants’ performance when the task was described as diagnostic of their ability (C. M. Steele, personal communication, May 2 1, 1997), in contrast to the
substantial effect of inquiring when the task was described as nondiagnostic.”
A substantive interpretation of this finding is that inquires about race or gender do not produce stereotype threat effects when a test is diagnostic because a diagnostic test already activates stereotype threat. However, if this were a real moderator, it would be important to document this fact and it is not clear why this finding obtained in an earlier study by Steele remained unpublished. Moreover, it is premature to interpret the significant result in the published study with a non-diagnostic task and the non-significant result in an unpublished study with a diagnostic task as evidence that diagnosticity moderates the effect of the stereotype-threat manipulation. A proper test of this moderator hypothesis would require the demonstration of a three-way interaction between race, inquiry about race, and diagnosticity. Absent this evidence, it remains possible that diagnosticity is not a moderator and that the published result is a false positive (or a positive result with an inflated effect size estimate). In contrast, there appears to be consistent evidence that inquiries about race or gender before a real assessment of academic performance does not influence performance. This finding is not widely publicized, but is important for a better understanding of performance differences in real world settings.
The best way to examine the replicability of Steele and Aronson’s seminal finding with non-diagnostic tasks would be to conduct an exact replication study. However, exact replication studies are difficult and costly. An alternative is to examine the robustness of the published results by taking a closer look at the strength of the statistical results reported by Steele and Aronson, using modern statistical tests of publication bias and statistical power like the R-Index (Schimmack, 2014) and the Test of Insufficient Variance (TIVA, Schimmack, 2014).
Replicability Analysis of Steele and Aronson’s four studies
Study 1. The first study had a relatively large sample of N = 114 participants, but it is not clear how many of the participants were White or Black. The study also had a 2 x 3 design, which leaves less than 20 participants per condition. The study produced a significant main effect of condition, F(2, 107) = 4.74, and race, F(1,107) = 5.22, but the critical condition x race interaction was not significant (reported as p > .19). However, a specific contrast showed significant differences between Black participants in the diagnostic condition and the non-diagnostic condition, t(107) = 2.88, p = .005, z = 2.82. The authors concluded “in sum, then, the hypothesis was supported by the pattern of contrasts, but when tested over the whole design, reached only marginal significance” (p. 800). In other words, Study 1 provided only weak support for the stereotype threat hypothesis.
Study 2. Study 2 eliminated one of the three experimental conditions. Participants were 20 Black and 20 White participants. This means there were only 10 participants in each condition of a 2 x 2 design. The degrees of freedom further indicate that the actual sample size was only 38 participants. Given the weak evidence in Study 1, there is no justification for a reduction in the number of participants per cell, although the difficulty of recruiting Black participants at Stanford may explain this inadequate sample size. Nevertheless, the study showed a significant interaction between race and test description, F(1,35) = 8.07, p = .007. The study also replicated the contrast from Study 1 that Black participants in the diagnostic condition performed significantly worse than Black participants in the non-diagnostic group, t(35) = 2.38, p = .023, z = 2.28.
Studies 1 and 2 are close replications of each other. The consistent finding across the two studies that supports stereotype-treat theory is the finding that merely changing the description of an assessment task changes Black participants performance, as revealed by significant differences between the diagnostic and non-diagnostic condition in both studies. The problem is that both studies had small numbers of Black participants and that small samples have low power to produce significant results. As a result, it is unlikely that a pair of studies would produce significant results in both studies.
Observed power in the two studies is .81 and .62 with median observed power of .71. Thus, the actual success rate of 100% (2 out of 2 significant results) is 29 percentage points higher than the expected success rate. Moreover, when inflation is evident, median observed power is also inflated. To correct for this inflation, the Replicability-Index (R-Index) subtracts inflation from median observed power, which yields an R-Index of 42. Any value below 50 is considered unacceptably low and I give it a letter grade F, just like students at American Universities receive an F for exams with less than 50% correct answers. This does not mean that stereotype threat is not a valid theory or that there was no real effect in this pair of studies. It simply means that the evidence in this highly cited article is insufficient to make strong claims about the causes of Black’s performance on academic tests.
The Test of Insufficient Variance (TIVA) provides another way to examine published results. Test statistics like t-values vary considerably from study to study even if the exact same study is conducted twice (or if one larger sample is randomly split into two sub-samples). When test-statistics are converted into z-scores, sampling error (the random variability from sample to sample) follows approximately a standard normal distribution with a variance of 1. If the variance is considerably smaller than 1, it suggests that the reported results represent a selected sample. Often the selection is a result of publication bias. Applying TIVA to the pair of studies, yields a variance of Var(z) = 0.15. As there are only two studies, it is possible that this outcome occurred by chance, p = .300, and it does not imply intentional selection for significance or other questionable research practices. Nevertheless, it suggests that future replication studies will be more variable and produce some non-significant results.
In conclusion, the evidence presented in the first two studies is weaker than we might assume if we focused only on the fact that both studies produced significant contrasts. Given publication bias, the fact that both studies reported significant results provides no empirical evidence because virtually all published studies report significant results. The R-Index quantifies the strength of evidence for an effect while taking the influence of publication bias into account and it shows that the two studies with small samples provide only weak evidence for an effect.
Study 3. This study did not examine performance. The aim was to demonstrate activation of stereotype threat with a sentence completion task. The sample size of 68 participants (35 Black, 33 White) implied that only 11 or 12 participants were assigned to one of the six cells in a 2 (race) by 3 (task description) design. The study produced main effects for race and condition, but most importantly it produced a significant interaction effect, F(2,61) = 3.30, p = .044. In addition, Black participants in the diagnostic condition had more stereotype-related associations than Black participants in the non-diagnostic condition, t(61) = 3.53,
Study 4. This study used inquiry about race to induce stereotype-threat. Importantly, the task was described as non-diagnostic (as noted earlier, a similar study produced no significant results when the task was described as diagnostic). The design was a 2 x 2 design with 47 participants, which means only 11 or 12 participants were allocated to the four conditions. The degrees of freedom indicated that cell frequencies were even lower. The study produced a significant interaction effect, F(1,39) = 7.82, p = .008. The study also produced a significant contrast between Blacks in the race-prime condition and the no-prime condition, t(39) = 2.43, p = .020.
The contrast effect in Study 3 is strong, but it is not a performance measure. If stereotype threat mediates the effect of task characteristics and performance, we would expect a stronger effect on the measure of the mediator than on the actual outcome of interest, task performance. The key aim of stereotype threat theory is to explain differences in performance. With a focus on performance outcomes, it is possible to examine the R-Index and TIVA of Studies 1, 2, and 4. All three studies reported significant contrasts between Black students randomly assigned to two groups that were expected to show performance differences (Table 1).
|Study 1||t(107) = 2.88||0.005||2.82||0.81|
|Study 4||t(39) = 2.43||0.020||2.33||0.64|
Median observed power is 64 and the R-Index is well below 50, 64 – 36 = 28 (F). The variance in z-scores is Var(z) = 0.09, p = .086. These results cast doubt about the replicability of the performance effects reported in Steele and Aronson’s seminal stereotype threat article.
Racial stereotypes and racial disparities are an important social issue. Social psychology aims and promises to contribute to the understanding of this issue by conducting objective, scientific studies that can inform our understanding of these issues. In order to live up to these expectations, social psychology has to follow the rules of science and listen to the data. Just like it is important to get the numbers right to send men and women into space (and bring them back), it is important to get the numbers right when we use science to understand women and men on earth. Unfortunately, social psychologists have not followed the examples of astronomers and the numbers do not add up.
The three African American women, features in this years movie “Hidden Figures”***, Katherine Johnson, Dorothy Vaughan, and Mary Jackson might not approve of the casual way social psychologists use numbers in their research, especially the wide-spread practice of hiding numbers that do not match expectations. No science that wants to make a real-world contribution can condone this practice. It is also not acceptable to simply ignore published results from well-conducted studies with large samples that challenge a prominent theory.
Surely, the movie Hidden Figures dramatized some of the experiences of Black women at NASA, but there is little doubt that Katherine Johnson, Dorothy Vaughan, and Mary Jackson encountered many obstacles that might be considered stereotype threatening situations. Yet, they prevailed and they paved the way for future generations of stereotyped groups. Understanding racial and gender bias and performance differences remains an important issue and that is the reason why it is important to shed a light on hidden numbers and put simplistic theories under the microscope. Stereotype threat is too often used as a simple explanation that avoids tackling deeper and more difficult issues that cannot be easily studied in a quick laboratory experiment with undergraduate students at top research universities. It is time for social psychologists to live up to its promises by tackling real world issues with research designs that have real world significance that produce real evidence using open and transparent research practices.
*** If you haven’t seen the movie, I highly recommend it.
15 thoughts on “Hidden Figures: Replication Failures in the Stereotype Threat Literature”
They were basically glorified calculators. The top scientific minds that devised the science and math at NASA were white men.
Sorry, it’s true.
Was the woman who did the calculations for the first moon landing a “glorified calculator” too? Let me guess if the same job was done by a man you would call him a genius for it.
Re-examining the Steele and Aronson (1995) article. Apparently, Study 1, they had an equal number of people in each cell (p. 799), so the sample must have been 50% African American and 50% White.
Dr. Ulrich Schimmack,
For several years now, I have been trying to replicate these two studies:
Moè, A., Cadinu, M., & Maass, A. (2015). Women Drive Better If Not Stereotyped. Accident Analysis & Prevention, 85, 199–206. https://doi.org/10.1016/j.aap.2015.09.021
Yeung, N. C. J., & von Hippel, C. (2008). Stereotype threat increases the likelihood that female drivers in a simulator run over jaywalkers. Accident Analysis & Prevention, 40(2), 667–674. https://doi.org/10.1016/j.aap.2007.09.003
Do you have any thoughts on my likelihood to succeed?
I have tested three times as many participants as in their experiment, and I don`t see the same results.
The original results are probably not trustworthy. Are the p-values between .05 and .005 or below .005? Please make sure to publish your results. I recommend Meta-Psychology as one potential outlet.
Thank you for the recommendation! Meta Psychology sounds like a great outlet. I don’t think my experiments would fit though, because they are neither entirely original, nor exact preregistered replications — I started with an attempt to reproduce the effect of stereotype threat in driving, since it is described so confidently in the above two publications. My plan was to look at the mechanisms that underlie the effect in further studies. So the two studies that I refer to as replication of the studies in the articles above are studies that were designed as a foundation, almost a pilot, for my own purposes, for future studies. But then the pilot was not working out, and I kept adding participants to increase the power, and I attempted a self-replication with a second study with an enhanced stereotype threat induction and larger N, and still nothing. Both of the studies that I quoted above show gender stereotype threat in driving, and when I also attempt to show gender stereotype in driving, I refer to it as a replication — a conceptual replication, I guess.
I might have to submit to Meta Psychology in the File Drawer category…
I am just trying to understand, with my limited knowledge as a graduate student, how could I have known, by looking at the studies above, that the effect is not going to be there. What did I miss? I now spent years and hundreds of participant hours trying to get to the effect. I tested 362 women, two hours each.
The authors were just so confident in their conclusions about the effect of ST, and even postulate the mechanisms that would explain it. I was impressed and wanted to discover more about it.
They should have known this: `before attempting to explain the phenomenon, make sure it exists`.
I think an article that also mentions the process (and a bias analysis of the original studies) would be a great contribution to MP, file-drawer category or failed replication category is not important.
Bias analysis, https://replicationindex.com/2015/05/13/the-test-of-insufficient-variance-tiva-a-new-tool-for-the-detection-of-questionable-research-practices-2/
OK. Sounds good. I just need to wrap my head around — and make the decision — of the best way of presenting this information to the research community. The most useful way for everyone.