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Month: July 2019

By Posted on Posted in Race No Comments

Racial resentment and symbolic racism are terms used to describe a set of measures used in racial attitudes research, including statements such as "Irish, Italians, Jewish and many other minorities overcame prejudice and worked their way up. Blacks should do the same without any special favors". This item and at least some of the other racial resentment items confound racism and nonracial ideology; in this "special favors" item, an individualist who believes that everyone should work their way up without special favors would select a response on the same side of the scale as an antiBlack racist who believes that only Blacks should work their way up without special favors.

Feldman and Huddy (2005) concluded that "racial resentment is an inadequate measure of prejudice because it confounds prejudice and political ideology" (p. 181), which is consistent with factor analysis of racial resentment items (Sears and Henry 2003: 271). Some research has addressed this confounding with what Feldman and Huddy (2005: 171) call the multivariate approach, in which the analysis includes statistical control for related ideological values. The logic of this multivariate approach is that racial resentment confounds ideology and antiBlack animus so that controlling for ideology should permit the residual association of racial resentment to be interpreted as the association due to antiBlack animus.

The analysis below approaches from the opposite direction: racial resentment confounds ideology and antiBlack animus so that controlling for antiBlack animus should permit the residual association of racial resentment to be interpreted as the association due to ideology. Moreover, if controls for ideology and for antiBlack animus are both included, then the association of racial resentment with an outcome variable should be zero. But this is not even close to being true, as illustrated below in a figure that reports the association of racial resentment with racial or possibly racialized outcome variables, using different sets of statistical control.

In each panel above, the top estimate indicates the association of racial resentment with the outcome variable controlling for only demographics. The second and third estimates respectively indicate the association of racial resentment with outcome variables after controls for demographics and racial attitudes and after controls for demographics and ideology. The fourth and fifth estimates respectively indicate the association of racial resentment with outcome variables after controls for demographics, ideology, and racial attitudes and after controls for demographics, ideology, and racial animus. The key comparison is between the third estimate and the fourth and fifth estimates: the measures of racial attitudes and racial animus had relatively little impact on the racial resentment estimate once the controls for ideology were included in the analysis. For example, in the top left panel, the coefficient for racial resentment was 0.51 controlling for demographics and ideology, was 0.48 controlling for demographics, ideology, and racial attitudes, and was 0.52 controlling for demographics, ideology, and racial animus. In a common racial resentment association analysis, the 0.51 coefficient controlling for demographics and ideology would be assigned to antiBlack animus, but the addition of seven racial attitudes controls accounted for only 0.03 of the 0.51 coefficient and the inclusion of six antiBlack animus controls did not even reduce the 0.51 coefficient. (see the Notes below for more description on the measurements).

A reasonable critique of the above analysis is that racial resentment taps a form of antiBlack racism that is not captured or is not well captured in the included measures of racial attitudes and racial animus. But, from what I can tell, that is an equally valid criticism of analyses that control for ideology: the nonracial ideology captured in racial resentment measures is not captured or not well captured in the included measures of ideology.

NOTES

1. The sample for the analysis was the 3,261 non-Hispanic Whites who completed face-to-face or online the pre- and post-election surveys, conducted between 8 September 2012 and 24 January 2013, and who were not listwise deleted from a model due to missing data for a variable. Each variable in the analysis was coded to range from 0 to 1. Linear regressions without weights were used to predict values of the outcome variables.

The racial resentment measure summed responses to the four ANES 2012 racial resentment items. Models included demographic controls for participant sex, marital status, age, education level, and household family income. Ideological controls were self-reported partisanship, self-reported ideology, an item about guaranteed jobs, an index of attitudes about the role of government, a moral traditionalism index, an authoritarianism index, and an egalitarianism index.

One set of models included seven controls for racial attitudes: a feeling thermometer difference of ratings of Whites and ratings of Blacks, a rating difference for Blacks and for Whites in general on a laziness stereotype scale, a rating difference for Whites and for Blacks in general on an intelligence stereotype scale, an item rating admiration of Blacks, an item rating sympathy for Blacks, an item measuring the perceived political influence of Blacks relative to Whites, and a difference in ratings of the level of discrimination in the United States today against Whites and against Blacks. Another set of models included six dichotomous controls that attempted to isolate antiBlack animus: a more than 20-point feeling thermometer rating difference in which Whites were rated higher than Blacks and with Whites rated at or above 50 and Blacks rated below 50, a rating of Blacks as lazier in general than Whites, a rating of Whites as more intelligent in general than Blacks, an indication of never feeling sympathy for Blacks, an indication that Blacks have too much influence in American politics but Whites don't, and an indication that there is no discrimination against Blacks in the United States today but that there is discrimination against Whites in the United States today.

2. Code for the analysis is here.

3. Results for the 2016 ANES are below:

4. Code for the 2016 ANES analysis is here.

5. Citations:

American National Election Studies (ANES). 2016. ANES 2012 Time Series Study. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2016-05-17. https://doi.org/10.3886/ICPSR35157.v1.

American National Election Studies, University of Michigan, and Stanford University. 2017. ANES 2016 Time Series Study. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2017-09-19. https://doi.org/10.3886/ICPSR36824.v2.

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By Posted on Posted in Sex No Comments

I drafted a manuscript entitled "Six Things Peer Reviewers Can Do To Improve Political Science". It was rejected once in peer review, so I'll post at least some of the ideas to my blog. This first blog post is about comments on the Valentino et al. 2018 "Mobilizing Sexism" Public Opinion Quarterly article. I sent this draft of the manuscript to Valentino et al. on June 11, 2018, limited to the introduction and parts that focus on Valentino et al. 2018; the authors emailed me back comments on June 12, 2018, which Dr. Valentino asked me to post and that I will post after my discussion.

1. Unreported tests for claims about group differences

Valentino et al. (2018) report four hypotheses, the second of which is:

Second, compared to recent elections, the impact of sexism should be larger in 2016 because an outwardly feminist, female candidate was running against a male who had espoused disdain for women and the feminist project (pp. 219-220).

Here is the discussion of their Study 2 results in relation to that expectation:

The pattern of results is consistent with expectations, as displayed in table 2. Controlling for the same set of predispositions and demographic variables as in the June 2016 online study, sexism was significantly associated with voting for the Republican candidate only in 2016 (b = 1.69, p < .05) (p.225).

However, as Gelman and Stern 2006 observed, "comparisons of the sort, 'X is statistically significant but Y is not,' can be misleading" (p. 331). In Table 2 of Valentino et al. 2018, the sexism predictor in the 2016 model had a logit coefficient of 1.69 and a standard error of 0.81, and the p-value under .05 for this sexism predictor provides information about only whether the 2016 sexism coefficient differs from zero; this p-value under .05 does not indicate whether, at p<.05, the 2016 sexism coefficient differs from the imprecisely estimated sexism coefficients of 0.23, 0.94, and 0.34 for 2012, 2008, and 2004. That difference in coefficients between sexism in 2016 and sexism in the other years is what would be needed to test the second hypothesis about the impact of sexism being larger in 2016.

2. No summary statistics reported for a regression-based inference about groups

Valentino et al. 2018 Table 2 indicates that, compared to lower levels of participant modern sexism, higher levels of participant modern sexism associate with a greater probability of a participant reported vote for Donald Trump in 2016. But the article does not report the absolute mean levels of modern sexism among Trump voters or Clinton voters. These absolute mean levels are in the figure below, limited to participants in face-to-face interviews (per Valentino et al. 2019 footnote 8):

Results in the above image indicate that the mean response across Trump voters represented beliefs:

  • that the news media should pay the same amount of attention to discrimination against women that they have been paying lately;
  • that, when women complain about discrimination, they cause more problems than they solve less than half the time;
  • and that, when women demand equality these days, less than half of the time they are actually seeking special favors.

These don't appear to be obviously sexist beliefs in the sense that I am aware of evidence that the beliefs incorrectly or unfairly disadvantage or disparage women or men, but comments are open below if you know of evidence or have an argument that the mean Trump voter response is sexist for any of these three items. Moreover, it's not clear to me that sexism can be inferred based on measures about only one sex; if, for instance, a participant believes that, when women complain about discrimination, they cause more problems than they solve, and the participant also believes that, when men complain about discrimination, they cause more problems than they solve, then it does not seem reasonable to code that person as a sexist, without more information.

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Response from Valentino et al.

Here is the response that I received from Valentino et al.

1) Your first concern was that we did not discuss one of the conditions in our MTurk study, focusing on disgust. The TESS reference is indeed the same study. However, we did not report results from the disgust condition because we did not theorize about disgust in this paper. Our theory focuses on the differential effects of fear vs. anger. We are in fact quite transparent throughout, indicating where predicted effects are non-significant. We also include a lengthy appendix with several robustness checks, etc. 

2) We never claim all Trump voters are sexist. We do claim that in 2016 gender attitudes are a powerful force, and more conservative scores on these measures significantly increase the likelihood of voting for Trump. The evidence from our work and several other studies supports this simple claim handsomely. Here is a sample of other work that replicates the basic finding in regarding the power of sexism in the 2016 election. Many of these studies use ANES data, as we do, but there are also several independent replications using different datasets. You might want to reference them in your paper. 

Blair, K. L. (2017). Did Secretary Clinton lose to a ‘basket of deplorables’? An examination of Islamophobia, homophobia, sexism and conservative ideology in the 2016 US presidential election. Psychology & Sexuality, 8(4), 334-355. 

Bock, J., Byrd-Craven, J., & Burkley, M. (2017). The role of sexism in voting in the 2016 presidential election. Personality and Individual Differences, 119, 189-193. 

Bracic, A., Israel-Trummel, M., & Shortle, A. F. (2018). Is sexism for white people? Gender stereotypes, race, and the 2016 presidential election. Political Behavior, 1-27.

Cassese, E. C., & Barnes, T. D. (2018). Reconciling Sexism and Women's Support for Republican Candidates: A Look at Gender, Class, and Whiteness in the 2012 and 2016 Presidential Races. Political Behavior, 1-24. 

Cassese, E., & Holman, M. R. Playing the woman card: Ambivalent sexism in the 2016 US presidential race. Political Psychology. 

Frasure-Yokley, L. (2018). Choosing the Velvet Glove: Women Voters, Ambivalent Sexism, and Vote Choice in 2016. Journal of Race, Ethnicity and Politics, 3(1), 3-25. 

Ratliff, K. A., Redford, L., Conway, J., & Smith, C. T. (2017). Engendering support: Hostile sexism predicts voting for Donald Trump over Hillary Clinton in the 2016 US presidential election. Group Processes & Intergroup Relations, 1368430217741203. 

Schaffner, B. F., MacWilliams, M., & Nteta, T. (2018). Understanding white polarization in the 2016 vote for president: The sobering role of racism and sexism. Political Science Quarterly, 133(1), 9-34. 

3) We do not statistically compare the coefficients across years, but neither do we claim to do so. We claim the following:

"Controlling for the same set of predispositions and demographic variables as in the June 2016 online study, sexism was significantly associated with voting for the Republican candidate only in 2016 (b = 1.69, p < .05). ...In conclusion, evidence from two nationally representative surveys demonstrates sexism to be powerfully associated with the vote in the 2016 election, for the first time in at least several elections, above and beyond the impact of other typically influential political predispositions and demographic characteristics."

Therefore, we predict (and show) sexism was a strong predictor in 2016 but not in other years. Our test is also quite conservative, since we include in these models all manner of predispositions that are known to be correlated with sexism. In Table 2, the confidence interval around our 2016 estimate for sexism in these most conservative models contains the estimate for 2008 in that analysis, and is borderline for 2004 and 2012, where the impact of sexism was very close to zero. However, the bivariate logit relationships between sexism and Trump voting are much more distinct, with 2016 demonstrating a significantly larger effect than the other years. These results are easy to produce with ANES data.

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Regarding the response from Valentino et al.:

1. My concern is that the decision about what to focus on in a paper is influenced by the results of the study. If a study has a disgust condition, then a description of the results of that disgust condition should be reported when results of that study are reported; otherwise, selective reporting of conditions could bias the literature.

2. I'm not sure that anything in their point 2 addresses anything my manuscript.

3. I realize that Valentino et al. 2018 did not report or claim to report results for a statistical test comparing the sexism coefficient in 2016 to sexism coefficients in prior years. But that reflects my criticism: that, for the hypothesis that "compared to recent elections, the impact of sexism should be larger in 2016…" (Valentino et al. 2018: 219-220), the article should have reported a statistical test to assess the evidence that the sexism coefficient in 2016 was different than than the sexism coefficient in prior recent elections.

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NOTE

Code for the figure.

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