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Forthcoming in the Journal of Politics is Peyton and Huber 2021 "Racial Resentment, Prejudice, and Discrimination". Study 1 estimated discrimination among White MTurk workers playing a game with a White proposer or a Black proposer. The abstract indicated that:

Study 1 used the Ultimatum Game (UG) to obtain a behavioral measure of racial discrimination and found whites engaged in anti-Black discrimination. Explicit prejudice explained which whites discriminated whereas resentment did not.

I didn't see an indication in the paper about a test for whether explicit prejudice predicted discrimination against Blacks better than racial resentment did. I think that the data had 173 workers coded non-White and and 20 workers with missing data on the race variable, but Peyton and Huber 2021 reported results for only White workers, so I'll stick with that and limit my analysis to reflect their analysis in Table S1.1, which is labeled in their code as "main analysis".

My analysis indicated that the discrimination against Black proposers was 2.4 percentage points among White workers coded as prejudiced (p=0.004) and 1.3 percentage points among White workers coded as high in racial resentment (p=0.104), with a p-value of p=0.102 for a test of whether these estimates differ from each other.

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The Peyton and Huber 2021 sorting into a prejudiced group or a not-prejudiced group based on responses to the stereotype scales permits assessment of whether the stereotype scales sorted workers by discrimination propensities, but I was also interested in the extent to which the measure of prejudice detected discrimination because the non-prejudiced comparison category included Whites who reported more negative stereotypes of Whites relative to Blacks, on net. My analysis indicated that point estimate for discrimination was:

* 2.4 percentage points against Blacks (p=0.001), among White workers who rated Blacks more negatively than Whites on net on the stereotype scales,

* 0.9 percentage points against Blacks (p=0.173), among White workers who rated Blacks equal to Whites on net on the stereotype scales, and

* 1.8 percentage points in favor of Blacks (p=0.147), among White workers who rated Blacks more positively than Whites on net on the stereotype scales.

The p-value for the difference between the 2.4 percentage point estimate and the 0.9 percentage point estimate is p=0.106, and the p-value for the difference between the 0.9 percentage point estimate and the -1.8 percentage point estimate is also p=0.106.

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NOTES

1. I have blogged about measuring "prejudice". The Peyton and Huber 2021 definition of prejudice is not bad:

Prejudice is a negative evaluation of another person based on their group membership, whereas discrimination is a negative behavior toward that person (Dovidio and Gaertner, 1986).

But I don't think that this is how Peyton and Huber 2021 measured prejudice. I think that instead a worker was coded as prejudiced for reporting a more negative evaluation about Blacks relative to Whites, on net for the four traits that workers were asked about. That's a *relatively* more negative perception of a *group*, not a negative evaluation of an individual person based on their group.

2. Peyton and Huber 2021 used an interaction term to compare discrimination among White workers with high racial resentment to discrimination among residual White workers, and used an interaction term to compare discrimination among White workers explicitly prejudiced against Blacks relative to Whites to discrimination among residual White workers.

Line 77 of the Peyton and Huber code tests whether, in a model including both interaction terms for the "Table S1.1, main analysis" section, the estimated discrimination gap differed between the prejudice categories and the racial resentment categories. The p-value was p=0.0798 for that test.

3. Data. Stata code for my analysis. Stata output for my analysis.

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1.

Abrajano and Lajevardi 2021 "(Mis)Informed: What Americans Know About Social Groups and Why it Matters for Politics" reported (p. 34) that:

We find that White Americans, men, the racially resentful, Republicans, and those who turn to Fox and Breitbart for news strongly predict misinformation about [socially marginalized] social groups.

But their research design is biased toward many or all of these results, given their selection of items for their 14-item set of misinformation items. I'll focus below on left/right political bias, and then discuss apparent errors in the publication.

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2.

Item #7 is a true/false item:

Most terrorist incidents on US soil have been conducted by Muslims.

This item will code as misinformed some participants who overestimate the percentage of U.S.-based terror attacks committed by Muslims, but won't code as misinformed any participants who underestimate that percentage.

It seems reasonable to me that persons on the political Left will be more likely than persons on the Right to underestimate the percentage of U.S.-based terror attacks committed by Muslims and that persons on the political Right will be more likely than persons on the Left to overestimate the percentage of U.S.-based terror attacks committed by Muslims, so I'll code this item as favoring the political Left.

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Four items (#11 to #14) ask about Black/White differences in receipt of federal assistance, but phrased so that Whites are the "primary recipients" of food stamps, welfare, and social security.

But none of these items measured misinformation about receipt of federal assistance as a percentage. So participants who report that the *number* of Blacks who receive food stamps is higher than the number of Whites who receive food stamps get coded as misinformed. But participants who mistakenly think that the *percentage* of Whites who receive food stamps is higher than the percentage of Blacks who receive food stamps do not get coded as misinformed.

Table 2 of this U.S. government report indicates that, in 2018, non-Hispanic Whites were 67% of households, 45% of households receiving SNAP (food stamps), and 70% of households not receiving SNAP. Respective percentages for Blacks were 12%, 27%, and 11% and for Hispanics were 13.5%, 22%, and 12%. So, based on this, it's correct that Whites are the largest racial/ethnic group that receives food stamps on a total population basis...but it's also true that Whites are the largest racial/ethnic group that does NOT receive food stamps on a total population basis.

It seems reasonable to me that the omission of percentage versions of these three public assistance items favors the political Left, in the sense that persons on the political Left are more likely to rate Blacks higher than Whites than are persons on the political Right, or, for that matter, Independents and moderates, so that these persons on the Left would presumably be more likely than persons on the Right to prefer (and thus guess) that Whites and not Blacks are the primary recipients of federal assistance. So, by my count, that's at least four items that favor the political Left.

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As far as I can tell, Abrajano and Lajevardi 2021 didn't provide citations to justify their coding of correct responses. But it seems to me that such citation should be a basic requirement for research that codes responses as correct, except for  obvious items such as, say, who the current Vice President is. A potential problem with this lack of citation is that it's not clear to me that some responses that Abrajano and Lajevardi 2021 coded as correct are truly correct or at least are the only responses that should be coded as correct.

Abrajano and Lajevardi 2021 coded "Whites" as the only correct response for the "primary recipients" item about welfare, but this government document indicates that, for 2018, the distribution of TANF recipients was 37.8% Hispanic, 28.9% Black, 27.2% White, 2.1% multi-racial, 1.9% Asian, 1.5% AIAN, and 0.6% NHOPI.

And "about the same" is coded as the only correct response for the item about the "primary recipients" of public housing (item #14), but Table 14 of this CRS Report indicates that, in 2017, 33% of public housing had a non-Hispanic White head of household and 43% had a non-Hispanic Black head of household. This webpage permits searching for "public housing" for different years (screenshot below), which, for 2016, indicates percentages of 45% for non-Hispanic Blacks and 29% for non-Hispanic Whites.

Moreover, it seems suboptimal to have the imprecise "about the same" response be the only correct response. Unless outcomes for Blacks and Whites are exactly the same, presumably selection of one or the other group should count as the correct response.

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Does a political bias in the Abrajano and Lajevardi 2021 research design matter? I think that the misinformation rates are close enough so that it matters: Figure A2 indicates that the Republican/Democrat misinformation gap is less than a point, with misinformed means of 6.51 for Republicans and 5.83 for Democrats.

Ironically, Abrajano and Lajevardi 2021 Table A1 indicates that their sample was 52% Democrat and 21% Republican, so -- on the "total" basis that Abrajano and Lajevardi 2021 used for the federal assistance items -- Democrats were the "primary" partisan source of misinformation about socially marginalized groups.

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NOTES

1. Abrajano and Lajevardi 2021 (pp. 24-25) refers to a figure that isn't in the main text, and I'm not sure where it is:

When we compare the misinformation rates across the five social groups, a number of notable patterns emerge (see Figure 2)...At the same time, we recognize that the magnitude of difference between White and Asian American's [sic] average level of misinformation (3.4) is not considerably larger than it is for Blacks (3.2), nor for Muslim American respondents, who report the lowest levels of misinformation.

Table A5 in the appendix indicates that Blacks had a lower misinformation mean than Muslims did, 5.583 compared to 5.914, so I'm not sure what the aforementioned passage refers to. The passage phrasing refers to a "magnitude of difference", but 3.4 doesn't seem to refer to a social group gap or to an absolute score for any of the social groups.

2. Abrajano and Lajevardi 2021 footnote 13 is:

Recall that question #11 is actually four separate questions, which brings us to a total of thirteen questions that comprise this aggregate measure of political misinformation.

Question 11 being four separate questions means that there are 14 questions, and Abrajano and Lajevardi 2021 refers to "fourteen" questions elsewhere (pp. 6, 17).

Abrajano and Lajevardi 2021 indicated that "...we also observe about 11% of individuals who provided inaccurate answers to all or nearly all of the information questions" (p. 24, emphasis in the original), and it seems a bit misleading to italicize "all" if no one provided inaccurate responses to all 14 items.

3. Below, I'll discuss the full set of 14 "misinformation" items. Feel free to disagree with my count, but I would be interested in an argument that the 14 items do not on net bias results toward the Abrajano and Lajevardi 2021 claim that Republicans are more misinformed than Democrats about socially marginalized groups.

For the aforementioned items, I'm coding items #7 (Muslim terror %), #11 (food stamps), #12 (welfare), and #14 (public housing) as biased in favor of the political Left, because I think that these items are phrased so that the items will catch more misinformation among the political Right than among the political Left, even though the items could be phrased to catch more misinformation among the Left than among the Right.

I'm not sure about the item about social security (#13) , so I won't code that item as politically biased. So by my count that's 4 in favor of the Left, plus 1 neutral.

Item #5 seems to be a good item, measuring whether participants know that Blacks and Latinos are more likely to live in regions with environmental problems. But it's worth noting that this item is phrased in terms of rates and not, as for the federal assistance items, as the total number of persons by racial/ethnic group. So by my count that's 4 in favor of the Left, plus 2 neutral.

Item #1 is about the number of undocumented immigrants in the United States. I won't code that item as politically biased. So by my count that's 4 in favor of the Left, plus 3 neutral.

The correct response for item #2 is that most immigrants in the United States are here legally. I'll code this item as favoring the political Left for the same reason as the Muslim terror % item: the item catches participants who overestimate the percentage of immigrants here illegally, but the item doesn't catch participants who underestimate that percentage, and I think these errors are more likely on the Right and Left, respectively. So by my count that's 5 in favor of the Left, plus 3 neutral.

Item #6 is about whether *all* (my emphasis) U.S. universities are legally permitted to consider race in admissions. It's not clear to me why it's more important that this item be about *all* U.S. universities instead of about *some* or *most* U.S. universities. I think that it's reasonable to suspect that persons on the political Right will overestimate the prevalence of affirmative action and that persons on the political Left will underestimate the prevalence of affirmative action, so by my count that's 6 in favor of the Left, plus 3 neutral.

I'm not sure that items #9 and #10 have much of a bias (number of Muslims in the United States, and the country that has the largest number of Muslims), other than to potentially favor Muslims, given that the items measure knowledge of neutral facts about Muslims. So by my count that's 6 in favor of the Left, plus 5 neutral.

I'm not sure what "social group" item #8 is supposed to be about, which is about whether Barack Obama was born in the United States. I'm guessing that a good percentage of "misinformed" responses for this item are insincere. Even if it were a good idea to measure insincere responses to test a hypothesis about misinformation, I'm not sure why it would be a good idea to not also include a corresponding item about a false claim that, like the Obama item, is known to be more likely to be accepted among the political Left, such as items about race and killings by police. So I'll up the count to 7 in favor of the Left, plus 5 neutral.

Item #4 might reasonably be described as favoring the political Right, in the sense that I think that persons on the Right would be more likely to prefer that Whites have a lower imprisonment rate than Blacks and Hispanics. But the item has this unusual element of precision ("six times", "more than twice") that isn't present in items about hazardous waste and about federal assistance, so that, even if persons on the Right stereotypically guess correctly that Blacks and Hispanics have higher imprisonment rates than Whites, these persons still might not be sure that the "six times" and "more than twice" are correct.

So even though I think that this item (#4) can reasonably be described as favoring the political Right, I'm not sure that it's as easy for the Right to use political preferences to correctly guess this item as it is for the Left to use political preferences to correctly guess the hazardous waste item and the federal assistance items. But I'll count this item as favoring the Right, so by my count that's 7 in favor of the Left, 1 in favor of the Right, plus 5 neutral.

Item #3 is about whether the U.S. Census Bureau projects ethnic and racial minorities to be a majority in the United States by 2042. I think that it's reasonable that a higher percentage of persons on the political Left than the political Right would prefer this projection to be true, but maybe fear that the projection is true might bias this item in favor of the Right. So let's be conservative and count this item as favoring the Right, so that my coding of the overall distribution for the 14 misinformation items is: seven items favoring the Left, two items favoring the Right, and five politically neutral items.

4. The ANES 2020 Time Series Study has similar biases in its set of misinformation items.

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Meta-Psychology has published my manuscript "Perceived Discrimination against Black Americans and White Americans".

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Norton and Sommers 2011 presented evidence for a claim that has become widely cited, that "Whites have now come to view anti-White bias as a bigger societal problem than anti-Black bias". I was skeptical of that claim, so I checked the data for the American National Election Studies 2012 Time Series Study, which was the most recent ANES Time Series Study available at the time. These ANES data contradicted that claim.

I preregistered an analysis of the then-upcoming ANES 2016 Time Series Study and then preregistered an analysis of an item on a 2017 survey that YouGov conducted for me with different wording for the measure of perceived discrimination.

Each of these three sets of data indicated that a higher percentage of Whites reported the perception that there is more discrimination in the United States today against Blacks than against Whites, compared to the percentage of Whites that reported the perception that there is more discrimination in the United States today against Whites than against Blacks. And it's not particularly close. Here is a new data point: in weighted analyses of data from the ANES 2020 Time Series Study, 63% of non-Hispanic Whites rated discrimination against Blacks as larger than discrimination against Whites, but only 8% of non-Hispanic Whites rated discrimination against Whites as larger than discrimination against Blacks.

The Meta-Psychology article has an explanation for why the Norton and Sommers 2011 claim appears to be incorrect.

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NOTE

1. Data source: American National Election Studies. 2021. ANES 2020 Time Series Study Preliminary Release: Combined Pre-Election and Post-Election Data [dataset and documentation]. March 24, 2021 version. www.electionstudies.org.

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The Journal of Academic Ethics published Kreitzer and Sweet‑Cushman 2021 "Evaluating Student Evaluations of Teaching: A Review of Measurement and Equity Bias in SETs and Recommendations for Ethical Reform".

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Kreitzer and Sweet‑Cushman 2021 reviewed "a novel dataset of over 100 articles on bias in student evaluations of teaching" (p. 1), later described as "an original database of more than 90 articles on evaluative bias constructed from across academic disciplines" (p. 2), but a specific size of the dataset/database is not provided.

I'll focus on the Kreitzer and Sweet‑Cushman 2021 discussion of evidence for an "equity bias".

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Footnote 4

Let's start with Kreitzer and Sweet‑Cushman 2021 footnote 4:

Research also finds that the role of attractiveness is more relevant to women, who are more likely to get comments about their appearance (Mitchell & Martin, 2018; Key & Ardoin, 2019). This is problematic given that attractiveness has been shown to be correlated with evaluations of instructional quality (Rosen, 2018)

Mitchell and Martin 2018 reported two findings about comments on instructor appearance. MM2018 Table 1 reported on a content analysis of official university course evaluations, which indicated that 0% of comments for the woman instructor and 0% of comments for the man instructor were appearance-related. MM2018 Table 2 reported on a content analysis of Rate My Professors comments, which indicated that 10.6% of comments for the woman instructor and 0% of comments for the man instructor were appearance-related, with p<0.05 for the difference between the 10.6% and the 0%.

So Kreitzer and Sweet‑Cushman 2021 footnote 4 cited the p<0.05 Rate My Professors finding but not the zero result for the official university course evaluations, even though official university course evaluations are presumably much more informative about bias in student evaluations as used in practice, compared to Rate My Professors comments that presumably are unlikely to be used for faculty tenure, promotion, and end-of-year evaluations.

Note also that Kreitzer and Sweet‑Cushman 2021 reported this Rate My Professors appearance-related finding without indicating the low quality of the research design: Mitchell and Martin 2018 compared comments about one woman instructor (Mitchell herself) to comments about one man instructor (Martin himself), from a non-experimental research design.

Moreover, the p<0.05 evidence for this "appearance" finding from Mitchell and Martin is based on an error by Mitchell and/or Martin. I blogged about the error in 2019, and MM2018 was eventually corrected (26 May 2020) to indicate that there is insufficient evidence (p=0.3063) to infer than the 10.6 percentage point gender difference in appearance-related comments is inconsistent enough with chance. However, Kreitzer and Sweet‑Cushman 2021 (accepted 27 Jan 2021) cited this "appearance" finding from the uncorrected version of the article.

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And I'm not sure what footnote 4 is referencing in Key and Ardoin 2019. The closest Key and Ardoin 2019 passage that I see is below:

Another telling point is whether students comment on the faculty member's teaching and expertise, or on such personal qualities as physical appearance or fashion choices. Among the students who'd received the bias statement, comments on female faculty were substantially more likely to be about the teaching.

But this Key and Ardoin 2019 passage is about a difference between groups in comments about female faculty (involving personal qualities and not merely comments on appearance), and does not compare comments about female faculty to comments about male faculty, which is what would be needed to support the Kreitzer and Sweet‑Cushman 2021 claim in footnote 4.

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And for the Kreitzer and Sweet‑Cushman 2021 claim that "the role of attractiveness is more relevant to women", consider this passage from Hamermesh and Parker (2005: 373):

The reestimates show, however, that the impact of beauty on instructors' course ratings is much lower for female than for male faculty. Good looks generate more of a premium, bad looks more of a penalty for male instructors, just as was demonstrated (Hamermesh & Biddle, 1994) for the effects of beauty in wage determination.

This finding is the *opposite* of the claim that "the role of attractiveness is more relevant to women".

Kreitzer and Sweet‑Cushman 2021 cited Hamermesh and Parker 2005 elsewhere, so I'm not sure why Kreitzer and Sweet‑Cushman 2021 footnote 4 claimed that "the role of attractiveness is more relevant to women" without at least noting the contrary evidence from Hamermesh and Parker 2005.

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"...react badly when those expectations aren't met"

From Kreitzer and Sweet‑Cushman 2021 (p. 4):

Students are also more likely to expect special favors from female professors and react badly when those expectations aren't met or fail to follow directions when they are offered by a woman professor (El-Alayli et al., 2018; Piatak & Mohr, 2019).

From what I can tell, neither Piatak and Mohr 2019 nor Study 1 of El-Alayli et al 2018 support the "react badly when those expectations aren't met" part of this claim. I think that this claim refers to the "negative emotions" measure of El-Alayli et al 2018 Study 2, but I don't think that the El-Alayli et al 2018 data support that inference.

El-Alayli et al 2018 *claimed* that there was a main effect of professor gender for the "negative emotions" measure, but I think that that claim is incorrect: the relevant means in El-Alayli et al 2018 Table 1 are 2.38 and 2.28, with a sample size of 121 across two conditions and corresponding standard deviations of 0.93 and 0.93, so that there is insufficient evidence of a main effect of professor gender for that measure.

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"...no discipline where women receive higher evaluative scores"

From Kreitzer and Sweet‑Cushman 2021 (p. 4):

Rosen (2018), using a massive (n = 7,800,000) Rate My Professor sample, finds there is no discipline where women receive higher evaluative scores.

I think that the relevant passage from Rosen 2018 is:

Importantly, out of all the disciplines on RateMyProfessors, there are no fields where women have statistically higher overall quality scores than men.

But this claim is based on an analysis limited to instructors rated "not hot", so Rosen 2018 doesn't support the Kreitzer and Sweet‑Cushman 2021 claim, which was phrased without that "not hot" caveat.

My concern with limiting the analysis to "not hot" instructors was that Rosen 2018 indicated that "hot" instructors on average received higher ratings than "not hot" instructors and that a higher percentage of women instructors than of men instructors received a "hot" rating. Thus, it seemed plausible to me that restricting the analysis to "not hot" instructors removed a higher percentage of highly-rated women than of highly-rated men.

I asked Andrew S. Rosen about gender comparisons by field for the Rate My Professors ratings for all professors and not limited to "not hot" professors, and he indicated that, of the 75 fields with the largest number of Rate My Professors ratings, men faculty had a higher mean overall quality rating at p<0.05 than women faculty did in many of these fields, but that, in exactly one of these fields (mathematics), women faculty had a higher mean overall quality rating at p<0.05 than men faculty did, with women faculty in mathematics also having a higher mean clarity rating and a higher mean helpfulness rating than men faculty in mathematics (p<0.05). Thanks to Andrew S. Rosen for the information.

By the way, the 7.8 million sample size cited by Kreitzer and Sweet‑Cushman 2021 is for the number of ratings, but I think that the more relevant sample size is the number of instructors who were rated.

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"designs", plural

From Kreitzer and Sweet‑Cushman 2021 (p. 4):

Experimental designs that manipulate the gender of the instructor in online teaching environments have even shown that students offered lower evaluations when they believed the instructor was a woman, despite identical course delivery (Boring et al., 2016; MacNell et al., 2015).

The plural "experimental designs" and the citation of two studies suggests that one of these studies replicated the other study, but, regarding this "believed the instructor was a woman, despite identical course delivery" research design, Boring et al. 2016 merely re-analyzed data from MacNell et al. 2015, so the two cited studies are not independent of each other such that a plural "experimental designs" would be justified.

And Kreitzer and Sweet‑Cushman 2021 reported the finding without mentioning shortcomings of the research design, such as a sample size small enough (N=43 across four conditions) to raise reasonable questions about the replicability of the result.

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Discussion

I think that it's plausible that there are unfair equity biases in student evaluations of teaching, but I'm not sure that Kreitzer and Sweet‑Cushman 2021 is convincing about that.

My reading of the literature on unfair bias in student evaluations of teaching is that the research isn't of consistently high enough quality that a credulous review establishes anything: a lot of the research designs don't permit causal inference of unfair bias, and a lot of the research designs that could permit causal inference have other flaws.

Consider the uncorrected Mitchell and Martin 2018: is it plausible that a respectable peer-reviewed journal would publish results from a similar research design that claimed no gender bias in student comments, in which the data were limited to a non-experimental comparison of comments about only two instructors? Or is it plausible that a respectable peer-reviewed journal would publish a four-condition N=43 version of MacNell et al. 2015 that found no gender bias in student ratings? I would love to see these small-N null-finding peer-reviewed publications, if they exist.

But maybe non-experimental "N=2 instructors" studies and experimental "N=43 students" studies that didn't detect gender bias in student evaluations of teaching exist, but haven't yet been published. If so, then did Kreitzer and Sweet‑Cushman try to find them? From what I can tell, Kreitzer and Sweet‑Cushman 2021 does not indicate that the authors solicited information about unpublished research through, say, posting requests on listservs or contacting researchers who have published on the topic.

I plan to tweet a link to this post tagging Dr. Kreitzer and Dr. Sweet‑Cushman, and I'm curious to see whether Kreitzer and Sweet‑Cushman 2021 is corrected or otherwise updated to address any of the discussion above.

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Racial resentment (also known as symbolic racism) is a common measure of racial attitudes in social science. See this post for items commonly used for racial resentment measures. For this post, I'll report plots about racial resentment, using data from the American National Election Studies 2020 Time Series Study.

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This first plot reports the percentage of respondents that rated Whites, Blacks, Hispanics, and Asians/Asian-Americans equally on 0-to-100 feeling thermometers, at each level of a 0-to-16 racial resentment index. Respondents at the lowest level of racial resentment had a lower chance of rating the included racial groups equally, compared to respondents at moderate levels of racial resentment or even compared to respondents at the highest level of racial resentment.

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This next plot reports the mean racial resentment for various groups. The top section is based on responses to 0-to-100 feeling thermometers about Whites, Blacks, Hispanics, and Asians/Asian-Americans. Respondents who rated all four included racial groups equally fell at about the middle of the racial resentment index, and respondents who reported isolated negative ratings about Whites (i.e., rated Whites under 50 but rated Blacks, Hispanics, and Asian/Asian-Americans at 50 or above) fell toward the low end of the racial resentment index.

The bottom two sections of the above plot report mean racial resentment based on responses to the "lazy" and "violent" stereotype items.

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So I think that the above plots indicate that low levels of racial resentment aren't obviously normatively good.

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Below is an update on how well racial resentment predicts attitudes about the environment and some other things that I don't expect to have a strong direct causal relationship with racial attitudes. The plot below reports OLS regression coefficients for racial resentment on a 0-to-1 scale, predicting the indicated outcomes on a 0-to-1 scale, with controls for gender, age group, education, marital status, income, partisanship, and ideology, all controlled for using categorical predictors.

The estimated effects of racial resentment on attitudes about federal spending on welfare and federal spending on crime (attitudes presumably related to race) are of similar "small to moderately small" size as the estimated effects of racial resentment on attitudes about greenhouse regulations, climate change causing severe weather, and federal spending on the environment.

Racial resentment had the ability to predict attitudes about the environment net of controls in ANES data from 1986.

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NOTES

1. Data source: American National Election Studies. 2021. ANES 2020 Time Series Study Preliminary Release: Combined Pre-Election and Post-Election Data [dataset and documentation]. March 24, 2021 version. www.electionstudies.org.

2. Stata and R code. Dataset for Plot 1. Dataset for Plot 2. Dataset for Plot 3.

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Sex Roles published El-Alayli et al 2018 "Dancing Backwards in High Heels: Female Professors Experience More Work Demands and Special Favor Requests, Particularly from Academically Entitled Students".

El-Alayli et al 2018 discussed their research design for Study 2 as follows (pp. 141-142):

The name of the professor, as well as the use of gendered pronouns in some of the subsequent survey questions, served as our professor gender manipulation, and participants were randomly assigned to one of the two experimental conditions...After reviewing the profile, participants were given seven scenarios that involved imagining special favor requests that could be asked of the professor...For each scenario, participants were first asked to indicate how likely they would be to ask for the special favor on a scale from 1 (Not at all likely) to 6 (Extremely likely). Using the same response scale, participants were then asked the likelihood that they would expect the professor to say "yes", ...

El-Alayli et al 2018 discussed the results for this item (p. 143, emphasis added):

There was a statistically significant main effect of professor gender on expectations, F(1, 117) = 5.68, p = .019 (b = −.80, SE = .34), such that participants were more likely to expect a "yes" response to the special favor requests when the professor had a woman's name than when the professor had a man's name. (Refer to Table 1 for condition means for all dependent measures.)

El-Alayli et al 2018 Table 1 reports that, for this "Expecting 'Yes'" item, the mean was 2.12 for the female professor and 2.05 for the male professor, with corresponding standard deviations of 0.80 and 0.66. The sample size was 121 total participants after exclusions (p. 141), so it wasn't clear to me how these data could produce a p-value of 0.019 or a b of -0.80 for the main effect of professor gender.

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I suspect that the -0.80 is not a main effect of professor gender but is instead the predicted effect of professor gender when the other term in the interaction (academic entitlement) is zero (with the lowest level of the academic entitlement scale being 1, see p. 142).

From El-Alayli et al 2018 (p. 143):

...the professor gender × academic entitlement interaction was statistically significant, F(1, 117) = 7.80, p = .006 (b = .38, SE = .14, ΔR2 = .06).

El-Alayli et al 2018 Table 1 indicates that the mean for academic entitlement is 2.27 for the male professor and 2.27 for the female professor, with corresponding standard deviations of 1.00 and 0.87. I'll ballpark 0.93 as the combined standard deviation.

From El-Alayli et al 2018 (p. 143):

Students had a stronger expectation of request approval from the female professor than from the male professor when they had a high level (+1 SD) of academic entitlement, t = 2.37, p = .020 (b = .42, SE = .18, 95% CI [.07, .78]), but not when they had average, t = .54, p = .590 (b = .07, SE = .13, 95% CI [−.18, .32]) or low (−1 SD) levels of entitlement, t = −1.61, p = .111 (b = −.29, SE = .18, 95% CI [−.64, .07]).

So the above passage provides three data points:

X1 = +1 SD = 2.27 + 0.93 = 3.20 || Y1 = 0.42

X2 = average = 2.27 || Y2 = 0.07

X3 = -1 SD = 2.27 - 0.93 = 1.34 || Y = -0.29

I used an OLS regression to predict these Ys using the Xs: to two decimal places, the X coefficient was 0.38 (which equals the coefficient on the interaction term), and the constant was -0.80 (which equals the purported "main effect"); however, in this regression -0.80 is the predicted value of Y when X (academic entitlement) is zero.

I'll add a highlight to the passage quoted above from El-Alayli et al 2018 (p. 143) to indicate what I think the main effect is:

Students had a stronger expectation of request approval from the female professor than from the male professor when they had a high level (+1 SD) of academic entitlement, t = 2.37, p = .020 (b = .42, SE = .18, 95% CI [.07, .78]), but not when they had average, t = .54, p = .590 (b = .07, SE = .13, 95% CI [−.18, .32]) or low (−1 SD) levels of entitlement, t = −1.61, p = .111 (b = −.29, SE = .18, 95% CI [−.64, .07]).

The b=0.07 is equal to the difference between the aforementioned means of 2.12 for the female professor and 2.05 for the male professor.

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The subscripts for El-Alayli et al 2018 Table 1 indicate that p<0.05 for the main effect of professor gender for four of the six items, but I don't think that p<0.05 for the main effect of professor gender any of those four items.

Moreover, I think that González-Morales 2019 incorrectly described the El-Alayli et al 2018 results as applying to all students with a stronger effect among academically entitled students, instead of the effect being detected only among academically entitled students:

In a recent experimental study, El-Alayli, Hansen-Brown, and Ceynar (2018) found that when students identified a fictitious professor as a woman, they expected that this professor would respond positively to requests for special favors or accommodations. This effect was stronger among academically entitled students.

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Social Science Quarterly recently published Cooper et al. 2021 "Heritage Versus Hate: Assessing Opinions in the Debate over Confederate Monuments and Memorials". The conclusion of the article notes that:

...we uncover significant evidence that the debate over Confederate monuments can be resoundingly summarized as "hate" over "heritage"

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In a prior post, I noted that:

...when comparing the estimated effect of predictors, inferences can depend on how well each predictor is measured, so such analyses should discuss the quality of the predictors.

Cooper et al. 2021 measured "heritage" with a dichotomous predictor and measured "hate" with a five-level predictor, and this difference in the precision of the measurements could have biased their research design toward a larger estimate for hate than for heritage. [See note 3 below for a discussion].

I'm not suggesting that the entire difference between their estimates for heritage and hate is due to the number of levels of the predictors, but I think that a better peer review would have helped eliminate that flaw in the research design, maybe by requiring the measure of hate to be dichotomized as close as possible to 70/30 like the measure of heritage was.

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Here is the lone measure of heritage used in Cooper et al. 2021:

"Do you consider yourself a Southerner, or not?"

Table 1 of the article indicates that 70% identified as a Southerner, so even if this were a face-valid measure of Southern heritage, the measure places into the highest level of Southern heritage persons at the 35th percentile of Southern heritage.

Maybe there is more recent data that undercuts this, but data from the Spring 2001 Southern Focus Poll indicated that only about 1 in 3 respondents who identified as a Southerner indicated that being a Southerner was "very important" to them. About 1 in 3 respondents who identified as a Southerner in that 2001 poll indicated that being a Southerner was "not at all important" or "not very important" to them, and I can't think of a good reason why, without other evidence, these participants belong in the highest level of a measure of Southern heritage.

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Wright and Esses 2017 had a more precise measure for heritage and found sufficient evidence to conclude that (p. 232):

Positive attitudes toward the Confederate battle flag were more strongly associated with Southern pride than with racial attitudes when accounting for these covariates.

How does Cooper et al. 2021 address the Wright and Esses 2017 result, which conflicts with the result from Cooper et al. 2021 and which used a related outcome variable and a better measure of heritage? The Cooper et al. 2021 article doesn't even mention Wright and Esses 2017.

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A better peer review might have caught the minimum age of zero years old in Table 1 and objected to the description of "White people are currently under attack in this country" as operationalizing "racial resentment toward blacks" (pp. 8-9), given that this item doesn't even mention or refer to Blacks. I suppose that respondents who hate White people would be reluctant to agree that White people are under attack regardless of whether that is true. But that's not the "hate" that is supposed to be measured.

Estimating the effect of "hate" for this type of research should involve comparing estimates net of controls for respondents who have a high degree of hate for Blacks to respondents who are indifferent to Blacks. Such estimates can be biased if the estimates instead include data from respondents who have more negative feelings about Whites than about Blacks. In a prior post, I discussed Carrington and Strother 2020, which measured hate with a Black/White feeling thermometer difference and thus permitted estimation of how much of the effect of hate is due to respondents rating Blacks higher than Whites on the feeling thermometers.

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Did Cooper et al. have access to better measures of hate than the item "White people are currently under attack in this country"? The Winthrop Poll site didn't list the Nov 2017 survey on its archived poll page for 2017. But, from what I can tell, this Winthrop University post discusses the survey, which included a better measure of racial resentment toward blacks. I don't know what information the peer reviewers of Cooper et al. 2021 had access to, but, generally, a journal reform that I would like to see for manuscripts reporting on a survey is for peer reviewers to be given access to the entire set of items for a survey.

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In conclusion, for a study that compares the estimated effects of heritage and hate, I think that at least three things are needed: a good measure of heritage, a good measure of hate, and the good measure of heritage being of similar quality to the good measure of hate. I don't think that Cooper et al. 2021 has any of those things.

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NOTES

1. The Spring 2001 Southern Focus Poll study was conducted by the Odum Institute for Research in Social Science of the University of North Carolina at Chapel Hill. Citation: Center for the Study of the American South, 2001, "Southern Focus Poll, Spring 2001", https://hdl.handle.net/1902.29/D-31552, UNC Dataverse, V1.

2. Stata output.

3. Suppose that mean support for leaving Confederate monuments as they are were 70% among the top 20 percent of respondents by Southern pride, 60% among the next 20 percent of respondents by Southern pride, 50% among the middle 20 percent, 40% among the next 20 percent, and 30% among the bottom 20 percent of respondents by Southern pride. And let's assume that these bottom 20 percent are indifferent about Southern pride and don't hate Southerners.

The effect of Southern pride could be estimated at 40 percentage points, which is the difference in support among the top 20 percent and bottom 20 percent by Southern pride. However, if we grouped the top 60 percent together and the bottom 40 percent together, the mean percentage support would respectively be 60% and 35%, for an estimated effect of 25 percentage points. In this illustration, the estimated effect for the five-level predictor is larger than the estimate for the dichotomous predictor, even with the same data.

Here is a visual illustration:

The above is a hypothetical to illustrate the potential bias in measuring one predictor with five levels and another predictor with two levels. I have no idea whether this had any effect on the results reported in Cooper et al. 2021. But, with a better peer review, readers would not need to worry about this type of bias in the Cooper et al. 2021 research design.

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