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Reply to “Powerless Conservatives or Powerless Findings?”

Published online by Cambridge University Press:  19 January 2021

L. J Zigerell*
Affiliation:
Illinois State University
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Abstract

Type
Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association

Responding to Zigerell (Reference Zigerell2019), Utych (Reference Utych2020a, 5) suggested that “research about issues such as anti-man bias may not be published because it is difficult to show conclusive evidence that it exists or has an effect on the political world.” However, evidence of anti-man bias is available in publishable measures of bias against a group, such as negative stereotypes and experimental discrimination, as in the following surveys:

  • In a 2014 survey (N=1,835), 9% of US adults indicated that “intelligent” is more true of women than men (Pew Research Center 2015, 17).

  • In a 2018 survey (N=2,301), 31% of US adults indicated that women in high political offices are, in general, better than men in high political offices at being honest and ethical (Pew Research Center 2018, 36).

  • The Schwarz and Coppock (Reference Schwarz and Coppock2020) meta-analysis of candidate-choice survey experiments reported an on-average favoring of women candidate targets over men candidate targets.

Utych (Reference Utych2020a) reported an illustrative example to suggest that research on anti-man bias suffers from the file-drawer problem. In table 1, an individual-level five-point “perceptions of discrimination against men” measure of anti-man bias associated at p<0.05 with two Trump-related outcome variables, net of controls such as ideology, partisanship, authoritarianism, and egalitarianism. This measure of anti-man bias lost statistical significance in table 2 due to the addition of controls for perceived discrimination against majority groups (Whites and Christians) and perceived discrimination against minority groups (Blacks, Hispanics, and Muslims).

My analyses (Zigerell Reference Zigerell2020) indicated that the measure of anti-man bias retains statistical significance in table 1 analyses when table 1 “perceptions of discrimination” measures are coded 1 for indicating that the amount of discrimination in the United States today is “none at all” and 0 for other substantive responses. This might be a better measure of bias than the five-point coding because “none at all” is the only response that is negative and clearly untrue (see Edelman, Luca, and Svirsky Reference Edelman, Luca and Svirsky2017; Starr Reference Starr2015; Yavorsky Reference Yavorsky2019).

Properly concluding that a predictor suffers from the file-drawer problem requires application of no more rigor than is needed to publish. Thus, for this purpose, table 1 results are preferable because table 1 statistical control is more rigorous than the statistical control in some recent publications (e.g., Utych Reference Utych2020b) that have predicted candidate evaluations using a measure of anti-woman bias. Utych (Reference Utych2020b) did not control for attitudes about racial or religious groups, so for assessing whether research on anti-man bias suffers from the file-drawer problem, table 2 results would be informative only if authors or journal gatekeepers required more rigorous statistical control for the anti-man analyses in Utych (Reference Utych2020a) than for the anti-woman analyses in Utych (Reference Utych2020b).

Regardless, p-values are irrelevant for the Zigerell (Reference Zigerell2019, 720) complaint about “the dearth of gender-attitudes items about men.” Instead, the complaint is valid because measurement of attitudes about men is needed to produce a proper inference about the net effect of sexist attitudes. Research on sexist attitudes should incorporate measures of attitudes about men due to considerations about research design, not considerations about p-values.

ACKNOWLEDGMENT

I thank the PS editors for this opportunity to respond. Pew Research Center bears no responsibility for the analyses or interpretations of the data presented here. The opinions expressed, including any implications for policy, are those of the author and not of Pew Research Center.

DATA AVAILABILITY STATEMENT

Replication materials are available on Dataverse at https://doi.org/10.7910/DVN/CJQROH.

References

Edelman, Benjamin, Luca, Michael, and Svirsky, Dan. 2017. “Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment.” American Economic Journal: Applied Economics 9 (2): 122.Google Scholar
Pew Research Center. 2015. “Women and Leadership: Public Says Women Are Equally Qualified, but Barriers Persist.” Washington, DC. Available at www.pewsocialtrends.org/wp-content/uploads/sites/3/2015/01/2015-01-14_women-and-leadership.pdf.Google Scholar
Pew Research Center. 2018. “Women and Leadership 2018.” Washington, DC. Available at www.pewsocialtrends.org/wp-content/uploads/sites/3/2018/09/Gender-and-leadership-for-PDF_updated-10.1.pdf.Google Scholar
Schwarz, Susanne, and Coppock, Alexander. 2020. “What Have We Learned About Gender from Candidate Choice Experiments? A Meta-Analysis of 42 Factorial Survey Experiments.” Journal of Politics. Forthcoming.Google Scholar
Starr, Sonja B. 2015. “Estimating Gender Disparities in Federal Criminal Cases.” American Law and Economics Review 17 (1): 127–59.CrossRefGoogle Scholar
Utych, Stephen M. 2020a. “Powerless Conservatives or Powerless Findings?” PS: Political Science & Politics. First View.CrossRefGoogle Scholar
Utych, Stephen M. 2020b. “Sexism Predicts Favorability of Women in the 2020 Democratic Primary…and Men?” Electoral Studies. In press.CrossRefGoogle Scholar
Yavorsky, Jill E. 2019. “Uneven Patterns of Inequality: An Audit Analysis of Hiring-Related Practices by Gendered and Classed Contexts.” Social Forces 98 (2): 461–92.CrossRefGoogle Scholar
Zigerell, L. J. 2019. “Left Unchecked: Political Hegemony in Political Science and the Flaws It Can Cause.” PS: Political Science & Politics 52 (4): 720–23.Google Scholar
Zigerell, L. J. 2020. “Replication Data for: Reply to ‘Powerless Conservatives or Powerless Findings?’” Harvard Dataverse. doi.org/10.7910/DVN/CJQROH.CrossRefGoogle Scholar
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