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Year: 2015

By Posted on Posted in Race 4 Comments

The post is here.

Data for the Hutchings and Walton study are here and code is here.

Data for the Southern Focus Poll are here and code is here.

Here are factor analysis results for the Hutchings and Walton study and for the Southern Focus Poll.

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UPDATE (July 7, 2015)

Corrected the code link for the Southern Focus Poll.

The Monkey Cage post is discussed in a scatterplot post.

More code to support this claim about Southern choice for words to describe food, as mentioned here.

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By Posted on Posted in Methods No Comments

This periodically-updated page is to acknowledge researchers who have shared data and/or code and/or have answered questions about their research. I tried to acknowledge everyone who provided data, code, or information, but let me know if I missed anyone who should be on the list. The list is chronological based on the date that I first received data and/or code and/or information.

Aneeta Rattan for answering questions about and providing data used in "Race and the Fragility of the Legal Distinction between Juveniles and Adults" by Aneeta Rattan, Cynthia S. Levine, Carol S. Dweck, and Jennifer L. Eberhardt.

Maureen Craig for code for "More Diverse Yet Less Tolerant? How the Increasingly Diverse Racial Landscape Affects White Americans' Racial Attitudes" and for "On the Precipice of a 'Majority-Minority' America", both by Maureen A. Craig and Jennifer A. Richeson.

Michael Bailey for answering questions about his ideal point estimates.

Jeremy Freese for answering questions and conducting research about past studies of the Time-sharing Experiments for the Social Sciences program.

Antoine Banks and AJPS editor William Jacoby for posting data for "Emotional Substrates of White Racial Attitudes" by Antoine J. Banks and Nicholas A. Valentino.

Gábor Simonovits for data for "Publication Bias in the Social Sciences: Unlocking the File Drawer" by Annie Franco, Neil Malhotra, and Gábor Simonovits.

Ryan Powers for posting and sending data and code for "The Gender Citation Gap in International Relations" by Daniel Maliniak, Ryan Powers, and Barbara F. Walter. Thanks also to Daniel Maliniak for answering questions about the analysis.

Maya Sen for data and code for "How Judicial Qualification Ratings May Disadvantage Minority and Female Candidates" by Maya Sen.

Antoine Banks for data and code for "The Public's Anger: White Racial Attitudes and Opinions Toward Health Care Reform" by Antoine J. Banks.

Travis L. Dixon for the codebook for and for answering questions about "The Changing Misrepresentation of Race and Crime on Network and Cable News" by Travis L. Dixon and Charlotte L. Williams.

Adam Driscoll for providing summary statistics for "What's in a Name: Exposing Gender Bias in Student Ratings of Teaching" by Lillian MacNell, Adam Driscoll, and Andrea N. Hunt.

Andrei Cimpian for answering questions and providing more detailed data than available online for "Expectations of Brilliance Underlie Gender Distributions across Academic Disciplines" by Sarah-Jane Leslie, Andrei Cimpian, Meredith Meyer, and Edward Freeland.

Vicki L. Claypool Hesli for providing data and the questionnaire for "Predicting Rank Attainment in Political Science" by Vicki L. Hesli, Jae Mook Lee, and Sara McLaughlin Mitchell.

Jo Phelan for directing me to data for "The Genomic Revolution and Beliefs about Essential Racial Differences A Backdoor to Eugenics?" by Jo C. Phelan, Bruce G. Linkb, and Naumi M. Feldman.

Spencer Piston for answering questions about "Accentuating the Negative: Candidate Race and Campaign Strategy" by Yanna Krupnikov and Spencer Piston.

Amanda Koch for answering questions and providing information about "A Meta-Analysis of Gender Stereotypes and Bias in Experimental Simulations of Employment Decision Making" by Amanda J. Koch, Susan D. D'Mello, and Paul R. Sackett.

Kevin Wallsten and Tatishe M. Nteta for answering questions about "Racial Prejudice Is Driving Opposition to Paying College Athletes. Here's the Evidence" by Kevin Wallsten, Tatishe M. Nteta, and Lauren A. McCarthy.

Hannah-Hanh D. Nguyen for answering questions and providing data for "Does Stereotype Threat Affect Test Performance of Minorities and Women? A Meta-Analysis of Experimental Evidence" by Hannah-Hanh D. Nguyen and Ann Marie Ryan.

Solomon Messing for posting data and code for "Bias in the Flesh: Skin Complexion and Stereotype Consistency in Political Campaigns" by Solomon Messing, Maria Jabon, and Ethan Plaut.

Sean J. Westwood for data and code for "Fear and Loathing across Party Lines: New Evidence on Group Polarization" by Sean J. Westwood and Shanto Iyengar.

Charlotte Cavaillé for code and for answering questions for the Monkey Cage post "No, Trump won't win votes from disaffected Democrats in the fall" by Charlotte Cavaillé.

Kris Byron for data for "Women on Boards and Firm Financial Performance: A Meta-Analysis" by Corrine Post and Kris Byron.

Hans van Dijk for data for "Defying Conventional Wisdom: A Meta-Analytical Examination of the Differences between Demographic and Job-Related Diversity Relationships with Performance" by Hans van Dijk, Marloes L. van Engen, and Daan van Knippenberg.

Alexandra Filindra for answering questions about "Racial Resentment and Whites' Gun Policy Preferences in Contemporary America" by Alexandra Filindra and Noah J. Kaplan.

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By Posted on Posted in Race No Comments

Here are four items typically used to measure symbolic racism, in which respondents are asked to indicate their level of agreement with the statements:

1. Irish, Italians, Jewish and many other minorities overcame prejudice and worked their way up. Blacks should do the same without any special favors.

2. Generations of slavery and discrimination have created conditions that make it difficult for blacks to work their way out of the lower class.

3. Over the past few years, blacks have gotten less than they deserve.

4. It's really a matter of some people not trying hard enough; if blacks would only try harder they could be just as well off as whites.

These four items are designed such that an antiblack racist would tend to respond the same way as a non-racist principled conservative. Many researchers realize this conflation problem and make an effort to account for this conflation. For example, here is an excerpt from Rabinowitz, Sears, Sidanius, and Krosnick 2010, explaining how responses to symbolic racism items might be influenced in part by non-racial values:

Adherence to traditional values—without concomitant racial prejudice—could drive Whites' responses to SR [symbolic racism] measures and their opinions on racial policy issues. For example, Whites' devotion to true equality may lead them to oppose what they might view as inherently inequitable policies, such as affirmative action, because it provides advantages for some social groups and not others. Similarly affirmative action may be perceived to violate the traditional principle of judging people on their merits, not their skin color. Consequently, opposition to such policies may result from their perceived violation of widely and closely held principles rather than racism.

However, this nuance is sometimes lost. Here is an excerpt from the Pasek, Krosnick, and Tompson 2012 manuscript that was discussed by the Associated Press shortly before the 2012 presidential election:

Explicit racial attitudes were gauged using questions designed to measure "Symbolic Racism" (Henry & Sears, 2002).

...

The proportion of Americans expressing explicit anti-Black attitudes held steady between 47.6% in 2008 and 47.3% in 2010, and increased slightly and significantly to 50.9% in 2012.

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See here and here for a discussion of the Pasek et al. 2012 manuscript.

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By Posted on Posted in Race No Comments

From the abstract of Bucolo and Cohn 2010 (gated, ungated):

'Playing the race card' reduced White juror racial bias as White jurors' ratings of guilt for Black defendants were significantly lower when the defence attorney's statements included racially salient statements. White juror ratings of guilt for White defendants and Black defendants were not significantly different when race was not made salient.

The second sentence reports that white mock juror ratings of guilt were not significantly different for black defendants and white defendants when race was not made salient, but the first sentence claims that "playing the race card" reduced white juror racial bias. But if the data can't support the inference that there is bias without the race card ("not significantly different"), then how can the data support the inference that "playing the race card" reduced bias?

For the answer, let's look at the Results section (p. 298). Guilt ratings were reported on a scale from -5 (definitely not guilty) to +5 (definitely guilty):

A post hoc t test (t(75) = .24, p = .81) revealed that ratings of guilt for a Black defendant (M = 1.10, SD = 2.63) were not significantly different than ratings of guilt for a White defendant (M = .95, SD = 2.92) when race was not made salient. When race was made salient, a post hoc t test (t(72) = 3.57, p =.001) revealed that ratings of guilt were significantly lower for a Black defendant (M = -1.32, SD = 2.91) than a White defendant (M = 1.31, SD = 2.96).

More simply, when race was not made salient, white mock jurors rated the black defendant roughly 5% of a standard deviation more guilty than the white defendant, which is a difference that would often fall within the noise created by sampling error (p=0.81). However, when race was made salient by playing the race card, white mock jurors rated the black defendant roughly 90% of a standard deviation less guilty than the white defendant, which is a difference that would often not fall within the noise created by sampling error (p=0.001).

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Here is how Bucolo and Cohn 2010 was described in a 2013 statement from the Peace Psychology division of the American Psychological Association:

Ignoring race often harms people of color, primarily because biases and stereotypes go unexamined. A study by Donald Bucolo and Ellen Cohn at the University of New Hampshire found that the introduction of race by the defense attorney of a hypothetical Black client reduced the effects of racial bias compared to when race was not mentioned (Bucolo & Cohn, 2010). One error in the state's approach in the George Zimmerman murder trial may have been the decision to ignore issues of race and racism.

But a change from 5% of a standard deviation bias against black defendants to 90% of a standard deviation bias against white defendants is not a reduction in the effects of racial bias.

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Note that the point of this post is not to present Bucolo and Cohn 2010 as representative of racial bias in the criminal justice system. There are many reasons to be skeptical of the generalizability of experimental research on undergraduate students acting as mock jurors at a university with few black students. Rather, the point of the post is to identify another example of selective concern in social science.

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By Posted on Posted in Race No Comments

Jeffrey A. Segal and Albert D. Cover developed the Segal-Cover scores that are widely used to proxy the political ideology of Supreme Court nominees. Segal-Cover scores are described here (gated) and here (ungated). The scores are based on the coding of newspaper editorials, with each paragraph in the editorial coded as liberal, conservative, moderate, or not applicable (p. 559).

Segal and Cover helpfully provided examples of passages that would cause a paragraph to be coded as liberal, conservative, or moderate. Here is Segal and Cover's first example of a passage that would cause a paragraph to be coded liberal:

Scarcely more defensible were the numerous questions about Judge Harlan's affiliation with the Atlantic Union. The country would have a sorry judiciary indeed, if appointees were to be barred for belonging to progressive and respectable organizations.

Here is Segal and Cover's first example of a passage that would cause a paragraph to be coded conservative:

Judge Carswell himself admits to some amazement now at what he said in that 1948 speech. He should, for his were the words of pure and simple racism.

I can't think of a better example of conservatism than that.

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By Posted on Posted in Sex No Comments

Looks like #addmaleauthorgate is winding down. I tried throughout the episode to better understand when, if ever, gender diversity is a good idea. I posted and tweeted and commented because I perceived a tension between (1) the belief that gender diversity produces benefits, and (2) the belief that it was sexist for a peer reviewer to suggest that gender diversity might produce benefits for a particular manuscript on gender bias.

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I posted a few comments at Dynamic Ecology as I was starting to think about #addmaleauthorgate. The commenters there were nice, but I did not get much insight about how to resolve the conflict that I perceived.

I posted my first blog post on the topic, which WT excerpted here in a comment. JJ, Ph.D posted a reply comment here that made me think, but on reflection I thought that the JJ, Ph.D comment was based on an unnecessary assumption. One of the comments at that blog post did lead to my second #addmaleauthorgate blog post.

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I received a comment on my first blog post, from Marta, which specified Marta's view of the sexism in the review:

Suggesting getting male input to fix the bias is sexist - the reviewer implies that the authors would not have come to the same conclusions if a male had read the paper.

That's a perfectly defensible idea, but its generalization has implications, such as it being sexist to suggest that a woman be placed on a team investigating gender bias; after all, the implication in suggesting gender diversity in that case would be that an all-male team is unable to draft a report on gender bias without help from a woman.

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The most dramatic interaction occurred on Twitter. After that, I figured that it was a good time to stop asking questions. However, I subsequently received two additional substantive responses. First, Zuleyka Zevallos posted a comment at Michael Eisen's blog that began:

Gender diversity is a term that has a specific meaning in gender studies – it comes out of intersectional feminist writing that demonstrates how cis-gender men, especially White men, are given special privileges by society and that the views, experiences and interests of women and minorities should be better represented.

Later that day, Karen James tweeted:

...diversity & inclusion are about including traditionally oppressed or marginalized groups. Men are not one of those groups.

Both comments refer to the asymmetry-in-treatment explanation that I referred to in note 4 of my first #addmaleauthorgate post. That is certainly a way to reconcile the two beliefs that I mentioned at the top of this post.

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Some more housekeeping. My comments here and here and here did not get very far in terms of attracting responses that disagreed with me. I followed up on a tweet characterizing the "whole review" by asking for the whole review to be made public, but that went nowhere; it seems suboptimal that there is so much commentary about a peer review that has been selectively excerpted.

A writer for Science Insider wrote an article indicating that Science Insider had access to the whole review. I asked for the writer to post the whole review, but the writer tweeted that I should contact the authors for this particular newsworthy item. I don't think that is how journalism is supposed to work.

I replied to a post on the topic in Facebook and might have posted comments elsewhere online. I make no claim about the exhaustiveness of the above links. The links aren't chronological, either.

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One more larger point. It seems that much of the negative commentary on this peer review mischaracterizes the peer review. This mischaracterization is another method by which to make it easier to dismiss thoughtful consideration of ideas that one does not want to consider.

Here is a description of the peer review:

...that someone would think it was OK to submit a formal review of a paper that said "get a male co-author"

Very strange use of quotes in that case, given that the quoted passage did not appear in the public part of the review. Notice also the generalization to "paper" instead of "paper on gender bias" and the more forceful description of "get" as opposed to "It would probably also be beneficial."

Here is more coverage of the peer review:

A scientific journal sparked a Twitter firestorm when it rejected two female scientists' work partly because the paper they submitted did not have male co-authors.

If there is any evidence that the same manuscript would not have been rejected or would have had a lesser chance of being rejected if the manuscript had male co-authors, please let me know.

One more example, from a radio station:

This week the dishonour was given to academic journal PLos One for rejecting a paper written by two female researchers on the basis that they needed to add a male co-author to legitimize their work.

I would be interested in understanding which part of the review could be characterized with the word "needed" and "legitimize." Yes, it would be terribly sexist if the reviewer wrote that the female researchers "needed to add a male co-author to legitimize their work"; however, that did not happen.

that someone would think it was OK to submit a formal review of a paper that said “get a male co-author” - See more at: http://www.michaeleisen.org/blog/?p=1700#sthash.o0RkigoR.dpuf
that someone would think it was OK to submit a formal review of a paper that said “get a male co-author” - See more at: http://www.michaeleisen.org/blog/?p=1700#sthash.o0RkigoR.dpuf
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By Posted on Posted in Sex No Comments

My previous post on #AddMaleAuthorGate did not focus on the part of the peer review that discussed possible sex differences. However, that part of the peer review has since been characterized as harassment, so I thought that a closer look would be of value. I have placed the relevant part of the public part of the peer review below.

"...perhaps it is not so surprising that on average male doctoral students co-author one more paper than female doctoral students, just as, on average, male doctoral students can probably run a mile race a bit faster than female doctoral students.
... ...
As unappealing as this may be to consider, another possible explanation would be that on average the first-authored papers of men are published in better journals than those of women, either because of bias at the journal or because the papers are indeed of a better quality, on average ... And it might well be that on average men publish in better journals ... perhaps simply because men, perhaps, on average work more hours per week than women, due to marginally better health and stamina."

Below, I'll gloss the passage, with notes that characterize as charitably as possible what the reviewer might have been thinking when writing the passage. Here goes:

"...perhaps it is not so surprising that on average male doctoral students co-author one more paper than female doctoral students,..." = This finding from the manuscript might not be surprising.

"...just as, on average, male doctoral students can probably run a mile race a bit faster than female doctoral students." = There might be an explanation for the finding that reflects something other than bias against women. Let me use an obvious example to illustrate this: men and women are typically segregated by sex in track races, and this might not be due to bias against women. Of course, I believe that there is overlap in the distribution of running speed, so I will toss in an "on average" and a "probably" to signal that I am not one of those sexists who think that men are better than women in running a mile race on average. I'll even use the caveat "a bit faster" to soften the proposed suggestion.

"... ..." = I wrote something here, but this passage was redacted before my review was posted on Twitter. That double ellipsis is unusual.

"As unappealing as this may be to consider..." = I know that this next part of the review might come across as politically incorrect. I'm just trying to signal that this is only something to consider.

"...another possible explanation would be that..." = I'm just proposing this as a possibility.

"...on average..." = I understand the overlap in the distribution.

"...the first-authored papers of men are published in better journals than those of women..." = I understand this finding from the manuscript.

"...either because of bias at the journal..." = That finding might actually be due to journals being biased against women. I realize this possibility, and I am not excluding it as an explanation. I even mentioned this hypothesis first, so that no one will think that I am discounting the manuscript's preferred explanation.

"...or because the papers are indeed of a better quality, on average..." = This is the most reasonable alternate explanation that I can think of. I am NOT saying that every paper by a man is necessarily of a better quality, so I'll mention the "on average" part again because I understand that there is overlap in the distribution. However, if we measure the quality of papers by men and the quality of papers by women and then compare the two measures, it might be possible that the difference in means between the two measures is not 0.00. I hope that no one forgot that this sentence began with a set of caveats about how this is a possible explanation that might be unappealing.

"..." = I wrote something else here, but this passage was also redacted before my review was posted on Twitter.

"And it might well be that on average men publish in better journals..." = Just restating a finding from the manuscript. I remembered the "on average" caveat. That's my fifth  "on average" so far in this short passage, by the way. I hope that my I'm-not-a-sexist signals are working.

"..." = I wrote something else here, too, but this passage was also redacted before my review was posted on Twitter; this ellipsis is mid-sentence, which is a bit suspicious.

"..perhaps simply because men, perhaps.." = This is just a possibility. I used the word "perhaps" twice, so that no one misses the "perhaps"s that I used to signal that this is just a possibility.

"...on average work more hours per week than women..." = This is what it means when the male-female wage gap is smaller when we switch from weekly pay to hourly pay, right?

"...due to marginally better health and stamina." = I remember reading a meta-analysis that found that men score higher than women on tests of cardiovascular endurance; I'm pretty sure that's a plausible proxy for stamina. I hope that no one interprets "health" as life expectancy or risk of a heart attack because the fact that men die on average sooner than women or might be more likely to have a heart attack is probably not much of a factor in the publishing of academic articles by early-career researchers.

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In my voice again. Some caveats of my own:

I am not making the claim that the review or the reviewer is not sexist or that the reviewer would have made the equivalent review if the researchers were all men. The purpose of this exercise was to try to gloss as charitably as possible the part of the review that discussed sex differences. If you do not think that we should interpret the review as charitably as possible, I would be interested in an explanation why.

The purpose of this exercise was not to diminish the bias that women face in academia and elsewhere. This post makes no claim that it is inappropriate for the female researchers in this episode -- or anyone else -- to interpret the review as reflecting the type of sexism that has occurred and has continued to occur.

Rather, the purpose of this exercise was to propose the possibility that our interpretation of the review reflects some assumptions about the reviewer and that our interpretation is informed by our experiences, which might color the review in a certain way for some people and in a certain way for other people. These assumptions are not necessarily invalid and might accurately reflect reality; but I wanted to call attention to their status as assumptions.

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