Improving peer review
Research involves a lot of decisions, which in turn provides a lot of opportunities for research to be incorrect or substandard, such as mistakes in recoding a variable, not using the proper statistical method, or not knowing unintuitive elements of statistical software such as how Stata treats missing values in logical expressions.
Peer and editorial review provides opportunities to catch flaws in research, but some journals that publish political science don't seem to be consistently doing a good enough job at this. Below, I'll provide a few examples that I happened upon recently and then discuss potential ways to help address this.
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Feinberg et al 2022
PS: Political Science & Politics published Feinberg et al 2022 "The Trump Effect: How 2016 campaign rallies explain spikes in hate", which claims that:
Specifically, we established that the words of Donald Trump, as measured by the occurrence and location of his campaign rallies, significantly increased the level of hateful actions directed toward marginalized groups in the counties where his rallies were held.
After Feinberg et al published a similar claim in the Monkey Cage in 2019, I asked the lead author about the results when the predictor of hosting a Trump rally is replaced with a predictor of hosting a Hillary Clinton rally.
I didn't get a response from Ayal Feinberg, but Lilley and Wheaton 2019 reported that the point estimate for the effect on the count of hate-motivated events is larger for hosting a Hillary Clinton rally than for hosting a Donald Trump rally. Remarkably, the Feinberg et al 2022 PS article does not address the Lilley and Wheaton 2019 claim about Clinton rallies, even though the supplemental file for the Feinberg et al 2022 PS article discusses a different criticism from Lilley and Wheaton 2019.
The Clinton rally counterfactual is an obvious way to assess the claim that something about Trump increased hate events. Even if the reviewers and editors for PS didn't think to ask about the Clinton rally counterfactual, that counterfactual analysis appears in the Reason magazine criticism that Feinberg et al 2022 discusses in its supplemental files, so the analysis was presumably available to the reviewers and editors.
Will May has published a PubPeer comment discussing other flaws of the Feinberg et al 2022 PS article.
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Christley 2021
The impossible "p < .000" appears eight times in Christley 2021 "Traditional gender attitudes, nativism, and support for the Radical Right", published in Politics & Gender.
Moreover, Christley 2021 indicates that (emphasis added):
It is also worth mentioning that in these data, respondent sex does not moderate the relationship between gender attitudes and radical right support. In the full model (Appendix B, Table B1), respondent sex is correlated with a higher likelihood of supporting the radical right. However, this finding disappears when respondent sex is interacted with the gender attitudes scale (Table B2). Although the average marginal effect of gender attitudes on support is 1.4 percentage points higher for men (7.3) than it is for women (5.9), there is no significant difference between the two (Figure 5).
Table B2 of Christley 2021 has 0.64 and 0.250 for the logit coefficient and standard error for the "Male*Gender Scale" interaction term, with no statistical significance asterisks; the 0.64 is the only table estimate without results reported to three decimal places, so it's not clear to me from the table if the asterisks are missing or is the estimate should be, say, 0.064 instead of 0.64. The sample size for the Table B2 regression is 19,587, so a statistically significant 1.4-percentage-point difference isn't obviously out of the question, from what I can tell.
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Hua and Jamieson 2022
Politics, Groups, and Identities published Hua and Jamieson 2022 "Whose lives matter? Race, public opinion, and military conflict".
Participants were assigned to a control condition with no treatment, to a placebo condition with an article about baseball gloves, or to an article about a U.S. service member being killed in combat. The experimental manipulation was the name of the service member, intended to signal race: Connor Miller, Tyrone Washington, Javier Juarez, Duc Nguyen, and Misbah Ul-Haq.
Inferences from Hua and Jamieson 2022 include:
When faced with a decision about whether to escalate a conflict that would potentially risk even more US casualties, our findings suggest that participants are more supportive of escalation when the casualties are of Pakistani and African American soldiers than they are when the deaths are soldiers from other racial–ethnic groups.
But, from what I can tell, this inference of participants being "more supportive" depending on the race of the casualties is based on differences in statistical significance when each racial condition is compared to the control condition. Figure 5 indicates a large enough overlap between confidence intervals for the racial conditions for this escalation outcome to prevent a confident claim of "more supportive" when comparing racial conditions to each other.
Figure 5 seems to plot estimates from the first column in Table C.7. The largest racial gap in estimates is between the Duc Nguyen condition (0.196 estimate and 0.133 standard error) and the Tyrone Washington condition (0.348 estimate and 0.137 standard error). So this difference in means is 0.152, and I don't think that there is sufficient evidence to infer that these estimates differ from each other. 83.4% confidence intervals would be about [0.01, 0.38] and [0.15, 0.54].
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Walker et al 2022
PS: Political Science & Politics published Walker et al 2022 "Choosing reviewers: Predictors of undergraduate manuscript evaluations", which, for the regression predicting reviewer ratings of manuscript originality, interpreted a statistically significant -0.288 OLS coefficient for "White" as indicating that "nonwhite reviewers gave significantly higher originality ratings than white reviewers". But the table note indicates that the "originality" outcome variable is coded 1 for yes, 2 for maybe, and 3 for no, so that the "higher" originality ratings actually indicate lower ratings of originality.
Moreover, Walker et al 2022 claims that:
There is no empirical linkage between reviewers' year in school and major and their assessment of originality.
But Table 2 indicates p<0.01 evidence that reviewer major associates with assessments of originality.
And the "a", "b", and "c" notes for Table 2 are incorrectly matched to the descriptions; for example, the "b" note about the coding of the originality outcome is attached to the other outcome.
The "higher originality ratings" error has been corrected, but not the other errors. I mentioned only the "higher" error in this tweet, so maybe that explains that. It'll be interesting to see if PS issues anything like a corrigendum about "Trump rally / hate" Feinberg et al 2022, given that the flaw in Feinberg et al 2022 seems a lot more important.
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Fattore et al 2022
Social Science Quarterly published Fattore et al 2022 "'Post-election stress disorder?' Examining the increased stress of sexual harassment survivors after the 2016 election". For a sample of women participants, the analysis uses reported experience being sexually harassed to predict a dichotomous measure of stress due to the 2016 election, net of controls.
Fattore et al 2022 Table 1 reports the standard deviation for a presumably multilevel categorical race variable that ranges from 0 to 4 and for a presumably multilevel categorical marital status variable that ranges from 0 to 2. Fattore et al 2022 elsewhere indicates that the race variable was coded 0 for white and 1 for minority, but indicates that the marital status variable is coded 0 for single, 1 for married/coupled, and 2 for separated/divorced/widowed, so I'm not sure how to interpret regression results for the marital status predictor.
And Fattore et al 2022 has this passage:
With 95 percent confidence, the sample mean for women who experienced sexual harassment is between 0.554 and 0.559, based on 228 observations. Since the dependent variable is dichotomous, the probability of a survivor experiencing increased stress symptoms in the post-election period is almost certain.
I'm not sure how to interpret that passage: Is the 95% confidence interval that thin (0.554, 0.559) based on 228 observations? Is the mean estimate of about 0.554 to 0.559 being interpreted as almost certain? Here is the paragraph that that passage is from.
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Hansen and Dolan 2022
Political Behavior published Hansen and Dolan 2022 "Cross‑pressures on political attitudes: Gender, party, and the #MeToo movement in the United States".
Table 1 of Hansen and Dolan 2022 reported results from a regression limited to 694 Republican respondents in a 2018 ANES survey, which indicated that the predicted feeling thermometer rating about the #MeToo movement was 5.44 units higher among women than among men, net of controls, with a corresponding standard error of 2.31 and a statistical significance asterisk. However, Hansen and Dolan 2022 interpreted this to not provide sufficient evidence of a gender gap:
In 2018, we see evidence that women Democrats are more supportive of #MeToo than their male co-partisans. However, there was no significant gender gap among Republicans, which could signal that both women and men Republican identifiers were moved to stand with their party on this issue in the aftermath of the Kavanaugh hearings.
Hansen and Dolan 2022 indicated that this inference of no significant gender gap is because, in Figure 1, the relevant 95% confidence interval for Republican men overlapped with the corresponding 95% confidence interval for Republican women.
Footnote 9 of Hansen and Dolan 2022 noted that assessing statistical significance using overlap of 95% confidence intervals is a "more rigorous standard" than using a p-value threshold of p=0.05 in a regression model. But Footnote 9 also claimed that "Research suggests that using non-overlapping 95% confidence intervals is equivalent to using a p < .06 standard in the regression model (Schenker & Gentleman, 2001)", and I don't think that this "p < .06" claim is correct or at least not misleading.
My Stata analysis of the data for Hansen and Dolan 2022 indicated that the p-value for the gender gap among Republicans on this item is p=0.019, which is about what would be expected given data in Table 1 of a t-statistic of 5.44/2.31 and more than 600 degrees of freedom. From what I can tell, the key evidence from Schenker and Gentleman 2001 is Figure 3, which indicates that the probability of a Type 1 error using the overlap method is about equivalent to p=0.06 only when the ratio of the two standard errors is about 20 or higher.
This discrepancy in inferences might have been avoided if 83.4% confidence intervals were more commonly taught and recommended by editors and reviewers, for visualizations in which the key comparison is between two estimates.
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Footnote 10 of Hansen and Dolan 2022 states:
While Fig. 1 appears to show that Republicans have become more positive towards #MeToo in 2020 when compared to 2018, the confidence bounds overlap when comparing the 2 years.
I'm not sure what that refers to. Figure 1 of Hansen and Dolan 2022 reports estimates for Republican men in 2018, Republican women in 2018, Republican men in 2020, and Republican women in 2020, with point estimates increasing in that order. Neither 95% confidence interval for Republicans in 2020 overlaps with either 95% confidence interval for Republicans in 2018.
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Other potential errors in Hansen and Dolan 2022:
[1] The code for the 2020 analysis uses V200010a, which is a weight variable for the pre-election survey, even though the key outcome variable (V202183) was on the post-election survey.
[2] Appendix B Table 3 indicates that 47.3% of the 2018 sample was Republican and 35.3% was Democrat, but the sample sizes for the 2018 analysis in Table 1 are 694 for the Republican only analysis and 1001 for the Democrat only analysis.
[3] Hansen and Dolan 2022 refers multiple times to predictions of feeling thermometer ratings as predicted probabilities, and notes for Tables 1 and 2 indicate that the statistical significance asterisk is for "statistical significance at p > 0.05".
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Conclusion
I sometimes make mistakes, such as misspelling an author's name in a prior post. In 2017, I preregistered an analysis that used overlap of 95% confidence intervals to assess evidence for the difference between estimates, instead of a preferable direct test for a difference. So some of the flaws discussed above are understandable. But I'm not sure why all of these flaws got past review at respectable journals.
Some of the flaws discussed above are, I think, substantial, such as the political bias in Feinberg et al 2022 not reporting a parallel analysis for Hillary Clinton rallies, especially with the Trump rally result being prominent enough to get a fact check from PolitiFact in 2019. Some of the flaws discussed above are trivial, such as "p < .000". But even trivial flaws might justifiably be interpreted as reflecting a review process that is less rigorous than it should be.
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I think that peer review is valuable at least for its potential to correct errors in analyses and to get researchers to report results that they otherwise wouldn't report, such as a robustness check suggested by a reviewer that undercuts the manuscript's claims. But peer review as currently practiced doesn't seem to do that well enough.
Part of the problem might be that peer review at a lot of political science journals combines [1] assessment of the contribution of the manuscript and [2] assessment of the quality of the analyses, often for manuscripts that are likely to be rejected. Some journals might benefit from having a (or having another) "final boss" who carefully reads conditionally accepted manuscripts only for assessment [2], to catch minor "p < .000" types of flaws, to catch more important "no Clinton rally analysis" types of flaws, and to suggest robustness checks and additional analyses.
But even better might be opening peer review to volunteers, who collectively could plausibly do a better job than a final boss could do alone. I discussed the peer review volunteer idea in this symposium entry. The idea isn't original to me; for example, Meta-Psychology offers open peer review. The modal number of peer review volunteers for a publication might be zero, but there is a good chance that I would have raised the "no Clinton rally analysis" criticism had PS posted a conditionally accepted version of Feinberg et al 2022.
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Another potentially good idea would be for journals or an organization such as APSA to post at least a small set of generally useful advice, such as reporting results for a test for differences between estimates if the manuscript suggests a difference between estimates. More specific advice could be posted by topic, such as, for count analyses, advice about predicting counts in which the opportunity varies by observation: Lilley and Wheaton 2019 discussed this page, but I think that this page has an explanation that is easier to understand.
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NOTES
1. It might be debatable whether this is a flaw per se, but Long 2022 "White identity, Donald Trump, and the mobilization of extremism" reported correlational results from a survey experiment but, from what I can tell, didn't indicate whether any outcomes differed by treatment.
2. Data for Hansen and Dolan 2022. Stata code for my analysis:
desc V200010a V202183
svyset [pw=weight]
svy: reg metoo education age Gender race income ideology2 interest media if partyid2=="Republican"
svy: mean metoo if partyid2=="Republican" & women==1
3. The journal Psychological Science is now publishing peer reviews. Peer reviews are also available for the journal Meta-Psychology.
4. Regarding the prior post about Lacina 2022 "Nearly all NFL head coaches are White. What are the odds?", Bethany Lacina discussed that with me on Twitter. I have published an update at that post.
5. I emailed or tweeted to at least some authors of the aforementioned publications discussing the planned comments or indicating at least some of the criticism. I received some feedback from one of the authors, but the author didn't indicate that I had permission to acknowledge the author.