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By Posted on Posted in Race No Comments

Study 1 of Strickler and Lawson 2020 "Racial conservatism, self-monitoring, and perceptions of police violence" in Politics, Groups, and Identities was an experiment in which participants rated how justified a police shooting was. The experiment had a control condition, a "stereotype" condition in which the officer was White and the suspect Black, and a "counterstereotype" condition in which the officer was Black and the suspect White.

The article indicates that:

And while racial resentment did not moderate how whites responded to treatment in the White Officer/Black Victim condition, it did impact response to treatment in the Black Officer/White Victim condition. As Table 3 and Figure 4 demonstrate, for whites, those with higher levels of racial resentment are significantly less likely to view shooting as justified if it involves a black officer and a white victim.

However, the 95% confidence interval in the aforementioned Figure 4 crosses zero at high levels of racial resentment. I emailed lead author Ryan Strickler for the data and code, which he provided.

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Instead of using a regression to estimate the outcome at higher levels of racial resentment, I'll estimate the outcome for only participants at given ranges of racial resentment (see Hainmueller et al. 2019). This way, inferences about particular groups are based on data for only those groups.

Plots below report point estimates and 95% confidence intervals from tests comparing the outcome across conditions, at various ranges of racial resentment, among all White respondents or among Whites who responded correctly to manipulation checks about the officer's race and the suspect's race. Racial resentment was coded from 1 through 17.

The outcome for the first four plots was whether the participant indicated that the officer's actions were justified.

In the top left plot, the top estimate is for White participants at the highest observed level of racial resentment. The estimate is positive 0.06, which indicates that high racial resentment participants in the stereotypic condition were 6 percentage points more likely to rate the shooting as justified, compared to high racial resentment participants in the counterstereotypic condition; however, the 95% confidence interval crosses zero. The next lower estimate compared outcomes for White participants at a racial resentment of 16 and 17. The bottom estimate (RR>=1) is for all White participants, and the negative point estimate for this bottom estimate indicates that White participants in the counterstereotypic shooting condition were more likely to rate the shooting as justified, compared to White participants in the stereotypic shooting condition.

The evidence for bias among Whites high in racial resentment is a bit stronger in the right panels, which compared the counterstereotypic condition to the control condition, but the 95% confidence intervals still overlap zero. There is an exception among White participants who scored 14 or higher on the racial resentment scale, when excluding participants who did not pass the post-treatment manipulation check, but it's not a good idea to exclude participants after the treatment.

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Tables in the main text of Strickler and Lawson 2020 reported results for a dichotomous outcome coded 1 if, for the first item of the branching, the respondent indicated that the officer's actions were justified. But tables in the appendix used ratings of the extent to which the shooting was justified, measured using branched items that placed respondents into nine levels, from "a great deal certain" that the shooting was not justified to "a great deal certain" that the shooting was justified.

The plots below report results from tests that compared conditions for this ordinal measure of justification, placed on a 0-to-1 scale. Evidence in the right panel is a bit stronger using this outcome, compared to the dichotomous outcome. Like before, the top estimate is for White participants at the highest observed level of racial resentment. Middle estimates (RR>=1 and RR<=17) are for all Whites; below that, estimates are for more extreme levels of low racial resentment, ending with RR==1, for White participants at the lowest observed level of racial resentment.

Results for Whites who passed the manipulation checks are in the output file.

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NOTES

1. Thanks to Ryan Strickler for sending me data and code for the article.

2. Stata code and R code for my analyses. Data for the first four plots. Data for the final two plots.

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By Posted on Posted in Race No Comments

The Ellis and Faricy 2020 Political Behavior article "Race, Deservingness, and Social Spending Attitudes: The Role of Policy Delivery Mechanism" discussed results from Figure 2:

This graph illustrates that while the mean support for this program does not differ significantly by spending mode, racial attitudes strongly affect the type of spending that respondents would prefer: those lowest in symbolic racism are expected to prefer the direct spending program to the tax expenditure program, while those high in symbolic racism are expected to prefer the opposite (p. 833).

Data for Study 2 indicated that, based on a linear regression using symbolic racism to predict nonBlack participant support for the programs, controlling for party identification, income, trust, egalitarianism, White race, and male, as coded in the Ellis and Faricy 2020 analyses, the predicted level of support at the lowest level of symbolic racism with other predictors at their means was 3.37 for the tax expenditure program and 3.87 for the direct spending program, but the predicted level of support at the highest level of symbolic racism was 3.44 for the tax expenditure program and 3.24 for the direct spending program.

However, linear regression can misestimate treatment effects. Below is a plot of the treatment effect estimated at individual levels of symbolic racism, with no controls (left panel) and with the aforementioned controls (right panel).

There does not appear to be much evidence in these data that participants high in symbolic racism preferred one program to the other. For example, in the left panel, at the highest level of symbolic racism, the estimated support was 2.76 for the tax expenditure program and was 2.60 for the direct spending program (p=0.41 for the difference). Moreover, the p-value for the difference did not drop under p=0.4 if participants from adjacent high levels of symbolic racism are included (7 and 8, or 6 through 8, or 5 through 8, or 4 through 8), with or without the controls.

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NOTES

1. Code for my analyses and plot. Data for the plot.

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By Posted on Posted in Sex No Comments

Brian Schaffner posted a paper ("How Political Scientists Should Measure Sexist Attitudes") that engaged my critique in this symposium entry about the gender asymmetry in research on gender attitudes. This post provides comments on the part of the paper that engages with my critiques.

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Schaffner placed men as the subject of five hostile sexism items, used responses to these items to construct a male-oriented hostile sexism scale, placed that scale into a regression alongside a female-oriented hostile sexism scale, and discussed results, such as (p. 39):

...including this scale in the models of candidate favorability or issue attitudes does not alter the patterns of results for the hostile sexism scale. The male-oriented scale demonstrates no association with gender-related policies, with coefficients close to zero and p-values above .95 in the models asking about support for closing the gender pay gap and relaxing Title IX.

The hostile sexism items include "Most men interpret innocent remarks or acts as being sexist" and "Many men are actually seeking special favors, such as hiring policies that favor them over women, under the guise of asking for equality".

These items reflect negative stereotypes about women, and it's not clear to me that these items should be expected to perform as well measuring "hostility towards men" (p. 39) as the items perform measuring hostility against women when women are the target of the items. I discussed in this prior post Schaffner 2019 Figure 2, which indicated that participants at low levels of hostile sexism discriminated against men; so the Schaffner 2019 data have participants who prefer women to men, but the male-oriented version of hostile sexism doesn't sort them sufficiently well.

If a male-oriented hostile sexism scale is to compete in a regression against a female-oriented hostile sexism scale, then interpretation of the results needs to be informed by how well each scale measures sexism against its target. I think an implication of the Schaffner 2019 results is that placing men as the target of hostile sexism items doesn't produce a good measure of sexism against men.

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The male-oriented hostile sexism might be appropriate as a "differencer" in the way that stereotype scale responses about Whites can be used to better measure stereotype scale responses about Blacks. For example, for the sexism items, a sincerely-responding participant who strongly agrees that people in general are too easily offended would be coded as a hostile sexist by the woman-oriented hostile sexism item but would be coded as neutral by a "differenced" hostile sexism item.

I don't know that this differencing should be expected to overturn inferences, but I think that it is plausible that this differencing would improve the sorting of participants by levels of sexism.

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Schaffner 2019 Figure A.1 indicates that the marginal effect of hostile sexism reduced the favorability ratings of female candidates Warren and Harris and increased the favorability ratings of Trump; see Table A.4 for more on this, and see Table A.5 for associations with policy preferences. However, given that low hostile sexism associates with sexism against men, I don't think that these associations in isolation are informative about whether sexism against women causes such support for political candidates or policies.

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If I analyze the Shaffner 2019 data, here are a few things that I would like to look for:

[1] Comparison of the coefficient for the female-oriented hostile sexism scale to the coefficient for a "differenced" hostile sexism scale, predicting Trump favorability ratings.

[2] Assessment of whether responses to certain items predict discrimination by target sex in the conjoint experiment, such as for participants who strongly supported or strongly opposed the pay gap policy item or participants with relatively extreme ratings of Warren, Harris, and Trump (say, top 25% and bottom 25%).

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In this post, I discussed the possibility that "persons at the lower levels of hostile sexism are nontrivially persons who are sexist against men". Brian Schaffner provides more information on this possibility, in the paper "How Political Scientists Should Measure Sexist Attitudes". I'll place Figure 2 from the paper below:

From the paper discussion of Figure 2 (p. 14):

The plot on the right shows the modest influence of hostile sexism on moderating the gender treatment in the politician conjoint. Subjects in the bottom third of the hostile sexism distribution were about 10 points more likely to select the female profile, a difference that is statistically significant (p=.005). However, the treatment effect was small and not statistically significant among those in the middle and top terciles.

From what I can tell, this evidence suggests that the proper interpretation of the hostile sexism scale is not as a measure of sexism against women but as a measure of male/female preference, with participants who prefer men sorted to high levels of the measure and participants who prefer women sorted to low levels of the measure. If hostile sexism were a proper linear measure of sexism against women, low values of hostile sexism would predict equal treatment of men and women and higher levels would predict favoritism of men over women.

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This post discusses three unpublished studies that I don't expect to be working on in the foreseeable future.

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1.

I posted at APSA Preprints "The Yuge Effect of Racist Resentment on Support for Donald Trump and…Attitudes about Automobile Fuel Efficiency Requirements?". This paper reports evidence indicating that a published measure of "racist resentment" does a remarkably good job predicting non-racial outcome variables such as environmental policy preferences. Sample results are at this prior post.

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2.

I posted at OSF a write-up of results for a preregistered study "Belief in Genetic Differences and Support for Efforts to Reduce Inequality". I reported these data in an unaccepted proposal for a short study with the Time-sharing Experiments for the Social Science.

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3.

Data for the second study and this third study are from a 2017 YouGov survey that I had conducted using funds from Illinois State University New Faculty Start-up Support and the Illinois State University College of Arts and Sciences. My initial plan was to run a version of my 2014 TESS proposal, but I saw the Carney and Enos paper (current version) in the 2015 MPSA program and realized that their experiments were similar to my plan, so I changed the survey.

Here is an early version of the planned survey.

One element of the new survey was an experiment involving attitudes about food stamps. I planned for the final three slides to each include an item about poor Americans, with the third item being randomly assigned to be about either poor White Americans or poor Black Americans. The third item was:

Most [randomize: poor Black Americans/poor White Americans] who receive government welfare could get along without it if they tried.

Carney and Enos had done something similar with the traditional racial resentment items, but these traditional racial resentment items aren't particularly good at measuring resentment (such as "Over the past few years, blacks have gotten less than they deserve"). The "could get along without it if they tried" is an old racial resentment item that wasn't included on the traditional four-item battery, but I think it does a nice job of capturing resentment.

I posted at OSF a write-up of results from this "unnecessary welfare experiment". I submitted to a journal a more extensive analysis and discussion, but the manuscript was rejected in peer review.

The "unnecessary welfare experiment" research design might have caused the estimated differences to be underestimates, given that the prior two items had the same response scale and were about poor Americans in general. Nonetheless, the results provide evidence that, in 2017, non-Hispanic White conservatives and non-Hispanic Whites in general reported more agreement that most poor Black Americans who receive government welfare could get along without it if they tried, compared to their reported agreement to the same statement about poor White Americans who receive government welfare.

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By Posted on Posted in Sex No Comments

This post discusses whether the lowest levels of hostile sexism are properly understood as indicating the lowest measured levels of sexism.

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Barnes et al. 2018 "Sex and Corruption: How Sexism Shapes Voters' Responses to Scandal" in Politics, Groups, and Identities (ungated) reported results from an experiment that had an outcome variable with four levels, from "very unlikely" to "very likely", in which participants could indicate how likely the participant would be to vote for a hypothetical representative in the next election. Treatments were the representative's sex (man or woman) and the type of scandal that the representative had been involved in (corruption or sex).

Hostile sexism (Glick and Fiske 1996) was measured with three items:

  1. Women are too easily offended
  2. Most women fail to appreciate all that men do for them
  3. Women exaggerate their problems

Below is the hostile sexism panel for the sex scandal condition, from Barnes et al. 2018 Figure 2. The right side of the panel suggests that participants at the highest levels of hostile sexism were biased against women. But the left side of the panel suggests that participants at lowest levels of hostile sexism were biased against men.That low levels of hostile sexism do not indicate the absence of sexism seems plausible given that, in the article, the lowest level of hostile sexism for participants responding to all hostile sexism items required participants to disagree as much as possible on a 7-point scale with mildly negative statements about women, such as the statement that "Most women fail to appreciate all that men do for them". Strong disagreement with this statement is equivalent to expressing the view that most women appreciate all that men do for them, and it seems at least possible that persons with such a positive view of women might be unfairly biased in favor of women. Another way to think of it is that persons unfairly biased in favor of women must fall somewhere on the hostile sexism measure, and it seems plausible that these persons would place themselves at or toward the lower end of the measure.

"Sex and Corruption" co-author Emily Bacchus sent me data and code for the article, and these data indicate that the patterns for the dichotomous "very unlikely" outcome variable in the above plot hold when the outcome variable is coded with all four measured levels of vote likelihood, as in the plot below, in which light blue dots are for the male candidate and pink dots are for the female candidate:

Further analysis suggested that, in the sex scandal plot, much or all of the modeled discrimination against men at the lower levels of hostile sexism is due to the linear model and a relatively large discrimination against women at higher levels of hostile sexism. For example, for levels of hostile sexism from 0.75 through 1, there is a 0.75 discrimination against women (Ns of 20 and 32, p<0.01); for levels of hostile sexism from 0 through 0.25, there is a 0.20 discrimination against men (Ns of 95 and 94, p=0.07); for levels of hostile sexism at 0, there is a 0.09 discrimination against men (Ns of 35 and 28, p=0.70). Only 4 participants scored a 1 for hostile sexism. For levels of hostile sexism from 0.25 through 0.75, there is a 0.05 discrimination against men (Ns of 169 and 155, p=0.57).

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Recent political science that I am familiar with that has used a hostile sexism measure has I think at least implied that lower levels of hostile sexism are normatively good. For example, the Barnes et al. 2018 article discussed "individuals who hold sexist attitudes" (p. 14, implying that some participants did not hold sexist attitudes), and a plot in Luks and Schaffner 2019 labeled the low end of a hostile sexism measure as "least sexist". However, it is possible that persons at the lower levels of hostile sexism are nontrivially persons who are sexist against men. I don't think that this possibility can be conclusively accepted or rejected based on the Barnes et al. 2018 data, but I do think that it matters whether the proper labeling of the low end of hostile sexism is "least sexist" or is "most sexist against men", to the extent that such unambiguous labels can be properly used for the lower end of the hostile sexism measure.

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NOTES

Thanks to Emily Bacchus and her co-authors for comments and sharing data and code, and thanks for Peter Glick and Susan Fiske for comments.

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By Posted on Posted in Sex No Comments

On October 27, 2019, U.S. Representative Katie Hill announced her resignation from Congress after her involvement in a sex scandal, claiming that she was leaving "because of a double standard".

There is a recently published article that reports on an experiment that can be used to assess such a double standard among the public, at least with an MTurk sample of over 1,000, with women about 45% of the sample: Barnes et al. 2018 "Sex and corruption: How sexism shapes voters' responses to scandal" in Politics, Groups, and Identities (ungated). Participants in the Barnes et al. 2018 experiment indicated on a four-point scale how likely they would be to vote for a representative in the next election; the experiment manipulated the hypothetical U.S. Representative's sex (man or woman) and the type of scandal that the representative had been involved in (corruption or sex).

Results in Barnes et al. 2018 Figure 1 indicated that, compared to the reported vote likelihoods for the male representative among participants assigned to the male representative involved in the sex scandal, participants assigned to the female representative involved in the sex scandal were not less likely to vote for the female representative.

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The Monkey Cage published a post by Michael Tesler, entitled "Was Rep. Katie Hill held to a higher standard than men in Congress? This research suggests she was". The post did not mention the Barnes et al. 2018 experiment.

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Mischiefs of Faction published a post by Gregory Koger and Jeffrey Lazarus that did mention the Barnes et al. 2018 experiment, but the Koger/Lazarus post did not mention the null finding across the full sample. The post instead mentioned the finding of a correlate of relative disfavoring of the female candidate (links omitted in the quoted passage below):

One answer is that there is sexist double standard for female politicians. One recently published article (ungated) by Tiffany Barnes, Emily Beaulieu, and Gregory Saxton finds that citizens are more likely to disapprove of a sex scandal by a female politician if they a) generally disapprove of women "usurping men's power," or b) see themselves as protectors of women, with protection contingent upon conformity to traditional gender roles. Both dynamics help explain why alleged House-rule-breaker Hill is resigning, while alleged federal-lawbreaker Hunter was reelected in 2018 and shows no interest in resigning.

The Koger/Lazarus post doesn't explain why these correlates are more important than the result among all participants or, for that matter, more important than the dynamic in Barnes et al. 2018 Figure 2 among participants with low hostile sexism scores.

The Koger/Lazarus post suggests that the Barnes et al. 2018 experiment detected a correlation between relative disfavoring of the female politician involved in a sex scandal and participant responses to a benevolent sexism scale (the "b" part of the passage quoted above). I don't think that is a correct description of the results: see Barnes et al. 2018 Table 1, Barnes et al. 2018 Figure 2, and/or the Barnes et al. 2018 statement that "Participants are thus unlikely to differentiate between the sex of the representative when responding to allegations about the representative's involvement in a sex scandal, regardless of the participant's level of benevolent sexism" (p. 13).

For what it's worth, the Barnes et al. 2018 abstract can be read as suggesting that the experiment did detect a bias among persons with high scores on a benevolent sexism scale.

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Barnes et al. 2018 is a recently published large-sample experiment that found that, in terms of vote likelihood, participants assigned to a hypothetical female U.S. Representative involved in a sex scandal treated that female representative remarkably similar to the way in which participants assigned to the hypothetical male representative involved in a sex scandal treated that male representative. This result is not mentioned in two political science blog posts discussing the claim of a gender double standard made by a female U.S. Representative involved in a sex scandal.

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