I saw a discussion on Twitter about claims of asymmetric polarization in the U.S. Congress, and I figured it was worth updating a prior analysis using Bailey ideal points:
Plot without disaggregation of Democrats:
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NOTES
1. Michael Bailey's data page, with a discussion of his method
In a prior post, I criticized the questionnaire for the ANES 2020 Time Series Study, so I want to use this post to praise the questionnaire for the ANES 2022 Pilot Study, plus add some other comments.
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1. The pilot questionnaire has items that ask participants to rate men and women on 0-to-100 feeling thermometers, which will permit assessment of the association for negative attitudes about women and men, presuming that some of the planned 1500 respondents express such negative attitudes.
2. The pilot questionnaire has items in which response options permit underestimation of the frequency of certain types of vote fraud, with a "Never" option for items about how often in the respondent's state [1] a voter casts more than one ballot and [2] votes are cast on behalf of dead people. That happened at least once recently in Arizona (see also https://www.heritage.org/
But it doesn't seem like a good idea to phrase the vote fraud item about the respondent's state, so that coding a response as a misperception requires checking evidence in 50 states. And I don't think there is an obvious threshold for overestimating how often, say, a voter casts more than one ballot. "Rarely" seems like an appropriate response for Arizona residents, but is "Occasionally" incorrect?
3. The pilot questionnaire has an item about the genuineness of emails on Hunter Biden's laptop in which Hunter Biden "contacted representatives of foreign governments about business deals". So I guess that can be a misinformation item that liberals are more likely to be misinformed about.
4. The pilot questionnaire has items about whether being White/Black/Hispanic/Asian "comes with advantages, disadvantages, or doesn't it matter". Based on the follow up item, these items might not permit respondents to select both "advantages" and "disadvantages", and, if so, it might be better to differentiate respondents who think that, for instance, being White has only advantages from respondents who think that being White has on net more advantages than disadvantages.
5. The pilot questionnaire permits respondents to report the belief that Black and Hispanic Americans have lower socioeconomic status than White Americans because of biological differences, but respondents can't report the belief that particular less positive outcomes for White Americans relative to another group are due to biological differences (e.g., average White American K12 student math performance relative to average Asian American K12 student math performance).
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Overall, the 2022 pilot seems like an improvement. For one thing, the pilot questionnaire, like is common for the ANES, has feeling thermometers about Whites, Blacks, Hispanics, and Asians, so that it's possible to construct a measure of negative attitudes about each included racial/ethnic group. And the feeling thermometers for men and women permit construction of a measure of negative attitudes about men and women. For another thing, respondents can report misperceptions that are presumably more common among persons on the political left. That's more than what is permitted by a lot of similar surveys.
Political Psychology recently published Chalmers et al 2022 "The rights of man: Libertarian concern for men's, but not women's, reproductive autonomy". The basis for this claim about libertarians' selective concern is indicated in the abstract as:
Libertarianism was associated with opposition to abortion rights and support for men's right both to prevent women from having abortions (male veto) and to withdraw financial support for a child when women refuse to terminate the pregnancy (financial abortion).
The above passage represents a flawed inferential method that I'll explain below.
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The lead author of Chalmers et al 2022 quickly responded to my request about the availability of data, code, and codebooks, with replication materials now public at the OSF site. I'll use data from Study 2 and run a simple analysis to illustrate the inferential flaw.
The only predictor that I'll use is a 0-to-6 "Libert" variable that I renamed "Libertarianism" and recoded to range from 0 to 1 for responses to the item "To what extent would you describe your political persuasion as libertarian?", with 0 for "Not at all" to 1 "Very much".
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In the OLS linear regression below, the abSINGLE outcome variable has eight levels, from 0 for "Not at all" to 1 for "Very much", for an item about whether the respondent thinks that a pregnant woman should be able to obtain a legal abortion if she is single and does not want to marry the man.
The linear regression output below (N=575) indicates that, on average, respondent libertarianism is negatively correlated with support for permitting a woman to have an abortion if she is single and does not want to marry the man.
. reg abSINGLE Libertarianism
---------------------------------
abSINGLE | Coef. p-value
---------------+-----------------
Libertarianism | -0.30 0.000
intercept | 0.89 0.000
---------------------------------
In the OLS linear regression below, the maleVETO outcome variable has six levels, from 0 for "Strongly disagree" to 1 for "Strongly agree", for an item about whether the respondent thinks that a woman should not be allowed to have an abortion if the man involved really wants to keep his unborn child.
The linear regression output below (N=575) indicates that, on average, respondent libertarianism is positively correlated with support for prohibiting a woman from having an abortion if the man involved really wants to keep his unborn child.
. reg maleVETO Libertarianism
--------------------------------
maleVETO | Coef. p-value
---------------+----------------
Libertarianism | 0.26 0.000
intercept | 0.13 0.000
--------------------------------
So what's the flaw in combining results from these two regressions to infer that libertarians have a concern for men's reproductive autonomy but not for women's reproductive autonomy?
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The flaw is that the linear regressions above include data from non-libertarians, and patterns among non-libertarians might account for the change in the sign of the coefficient on Libertarianism.
Note, for example, that, based on the OLS regression output, the predicted support among respondents highest in libertarianism will be 0.89 + -0.30, or 0.69, for women's right to an abortion on the 0-to-1 abSINGLE item, but will be 0.13 + 0.26, or 0.39, for men's right to an abortion veto on the 0-to-1 maleVETO item.
But let's forget these linear regression results, because the appropriate method for assessing whether a group is inconsistent is to analyze data only from that group. So here are respective means, for respondents at 6 on the 0-to-6 "Libert" variable (N=18):
0.45 on abSINGLE
0.49 on maleVETO
And here are respective means, for respondents at 5 or 6 on the 0-to-6 "Libert" variable (N=46):
0.53 on abSINGLE
0.42 on maleVETO
I wouldn't suggest interpreting these results to mean that libertarians are on net consistent about women's reproductive autonomy and men's reproductive autonomy or, for that matter, that libertarians favor women's reproductive autonomy over men's. But I think that the analyses illustrate the flaw in making inferences about a group based on a linear regression involving people who aren't in that group.
The Stata log file has output of my analyses above and additional analyses, but Chalmers et al 2022 had two datasets and multiple measures for key items, so the analyses aren't exhaustive.
Politics & Gender published Deckman and Cassese 2021 "Gendered nationalism and the 2016 US presidential election", which, in 2022, shared an award for the best article published in Politics & Gender the prior year.
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1.
So what is gendered nationalism? From Deckman and Cassese 2021 (p. 281):
Rather than focus on voters' sense of their own masculinity and femininity, we consider whether voters characterized American society as masculine or feminine and whether this macro-level gendering, or gendered nationalism as we call it, had political implications in the 2016 presidential election.
So how is this characterization of American society as masculine or feminine measured? The Deckman and Cassese 2021 online appendix indicates that gendered nationalism is...
Measured with a single survey item asking whether "Society as a whole has become too soft and feminine." Responses were provided on a four-point Likert scale ranging from strongly disagree to strongly agree.
So the measure of "whether voters characterized American society as masculine or feminine" (p. 281) ranged from the characterization that American society is (too) feminine to the characterization that American society is...not (too) feminine. The "(too)" is because I suspect that respondents might interpret the "too" in "too soft and feminine" as also applying to "feminine", but I'm not sure it matters much.
Regardless, there are at least three potential relevant characterizations: American society is feminine, masculine, or neither feminine nor masculine. It seems like a poor research design to combine two of these characterizations.
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2.
Deckman and Cassese 2021 also described gendered nationalism as (p. 278):
Our project diverges from this work by focusing on beliefs about the gendered nature of American society as a whole—a sense of whether society is 'appropriately' masculine or has grown too soft and feminine.
But disagreement with the characterization that "Society as a whole has become too soft and feminine" doesn't necessarily indicate a characterization that society is "appropriately" masculine, because a respondent could believe that society is too masculine or that society is neither feminine nor masculine.
Omission of a response option indicating a belief that American society is (too) masculine might have made it easier for Deckman and Cassese 2021 to claim that "we suppose that those who rejected gendered nationalism were likely more inclined to vote for Hillary Clinton" (p. 282), as if only the measured "too soft and feminine" characterization is acceptance of "gendered nationalism" and not the unmeasured characterization that American society is (too) masculine.
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3.
Regression results in Table 2 of Deckman and Cassese 2021 indicate that gendered nationalism predicts a vote for Trump over Clinton in 2016, net of controls for political party, a single measure of political ideology, and demographics such as class, race, and education.
Gendered nationalism is the only specific belief in the regression, and Deckman and Cassese 2021 reports no evidence about whether "beliefs about the gendered nature of American society as a whole" has any explanatory power above other beliefs about gender, such as gender roles and animus toward particular genders.
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4.
Deckman and Cassese 2021 reported on four categories of class: lower class, working class, middle class, and upper class. Deckman and Cassese 2021 hypothesis H2 is that:
Gendered nationalism is more common among working-class men and women than among men and women with other socioeconomic class identifications.
For such situations, in which the hypothesis is that one of four categories is distinctive, the most straightforward approach is to omit from the regressions the hypothesized distinctive category, because then the p-values and coefficients for each of the three included categories will provide information about the evidence that that included category differs from the omitted category.
But the regressions in Deckman and Cassese 2021 omitted middle class, and, based on the middle model in Table 1, Deckman and Cassese 2021 concluded that:
Working-class Democrats were significantly more likely to agree that the United States has grown too soft and feminine, consistent with H2.
But the coefficients and standard errors were 0.57 and 0.26 for working class and 0.31 and 0.40 for lower class, so I'm not sure that the analysis in Table 1 contained enough evidence that the 0.57 estimate for working class differs from the 0.31 estimate for lower class.
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5.
I think that Deckman and Cassese 2021 might have also misdescribed the class results in the Conclusions section, in the passage below, which doesn't seem limited to Democrat participants. From p. 295:
In particular, the finding that working-class voters held distinctive views on gendered nationalism is compelling given that many accounts of voting behavior in 2016 emphasized support for Donald Trump among the (white) working class.
For that "distinctive" claim, Deckman and Cassese 2021 seemed to reference differences in statistical significance (p. 289, footnote omitted):
The upper- and lower-class respondents did not differ from middle-class respondents in their endorsement of gendered nationalism beliefs. However, people who identified as working class were significantly more likely to agree that the United States has grown too soft and feminine, though the effect was marginally significant (p = .09) in a two-tailed test. This finding supports the idea that working-class voters hold a distinctive set of beliefs about gender and responded to the gender dynamics in the campaign with heightened support for Donald Trump’s candidacy, consistent with H2.
In the Table 1 baseline model predicting gendered nationalism without interactions, ologit coefficients are 0.25 for working class and 0.26 for lower class, so I'm not sure that there is sufficient evidence that working class views on gendered nationalism were distinctive from lower class views on gendered nationalism, even though the evidence is stronger that the 0.25 working class coefficient differs from zero than the 0.26 lower class coefficient differs from zero.
Looks like the survey's pre-election wave had at least twice as many working class respondents as lower class respondents. If that ratio was similar for the post-election wave, that would explain the difference in statistical significance and explain why the standard error was smaller for the working class (0.15) than for the lower class (0.23). Search for "class" at the PRRI site and use the PRRI/The Atlantic 2016 White Working Class Survey.
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6.
At least Deckman and Cassese 2021 interpreted the positive coefficient on the interaction of college and Republican as an estimate of how the association of college and the outcome among Republicans differed from the association of college and the outcome among the omitted category.
But I'm not sure of the justification for "largely" in Deckman and Cassese 2021 (p. 293):
Thus, in accordance with our mediation hypothesis (H5), gender differences in beliefs that the United States has grown too soft and feminine largely account for the gender gap in support for Donald Trump in 2016.
Inclusion of the predictor for gendered nationalism pretty much only halves the logit coefficient for "female", from 0.80 to 0.42, and, in Figure 3, the gender gap in predicted probability of a Trump vote is pretty much only cut in half, too. I wouldn't call about half "largely", especially without addressing the obvious confound of attitudes about men and women that have nothing to do with "gendered nationalism".
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7.
Deckman and Cassese 2021 was selected for a best article award by the editorial board of Politics & Gender. From my prior posts on publications in Politics & Gender: p < .000, misinterpreted interaction terms, and an example of the difference in statistical signifiance being used to infer an difference in effect.
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NOTES
1. Prior post mentioning Deckman and Cassese 2021.
2. Prior post on deviations from a preregistration plan, for Cassese and Barnes 2017.
3. "Gendered nationalism" is an example of use of a general term when a better approach would be specificity, such as a measure that separates "masculine nationalism" from "feminine nationalism". Another example is racial resentment, in which a general term is used to describe only the type of racial resentment directed at Blacks. Feel free to read through participant comments in the Kam and Burge survey, in which plenty of comments from respondents who score low on the racial resentment scale indicate resentment directed at Whites.
The Journal of Social and Political Psychology recently published Young et al 2022 "'I feel it in my gut:' Epistemic motivations, political beliefs, and misperceptions of COVID-19 and the 2020 U.S. presidential election", which reported in its abstract that:
Results from a US national survey from Nov-Dec 2020 illustrate that Republicans, conservatives, and those favorable towards President Trump held greater misperceptions about COVID and the 2020 election.
Young et al 2022 contains two shortcomings of too much social science: bias and error.
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1.
In Young et al 2022, the selection of items measuring misperceptions is biased toward things that the political right is more likely than the political left to indicate a misperception about, so that the most that we can conclude from Young et al 2022 is that the political right more often reported misperceptions about things that the political right is more likely to report misperceptions about.
Young et al 2022 seems to acknowledge this research design flaw in the paragraph starting with:
Given the political valence of both COVID and election misinformation, these relationships might not apply to belief in liberal-serving misinformation.
But it's not clear to me why some misinformation about covid can't be liberal-serving. At least, there are misperceptions about covid that are presumably more common among the political left than among the political right.
For example, the eight-item Young et al 2022 covid misperceptions battery contains two items that permit respondents to underestimate the seriousness of covid-19: "Coronavirus (COVID-19 is a hoax" [sic for the unmatched parenthesis], and "The flu is more lethal than coronavirus (COVID-19)". But the battery doesn't contain corresponding items that permit respondents to overestimate the seriousness of covid-19.
Presumably, a higher percentage of the political left than the political right overestimated the seriousness of covid-19 at the time of the survey in late 2020, given that, in a different publication, a somewhat different Young et al team indicated that:
Results from a national survey of U.S. adults from Nov-Dec 2020 suggest that Trump favorability was...negatively associated with self-reported mask-wearing.
Another misperception measured in the survey is that "Asian American people are more likely to carry the virus than other people", which was not a true statement at the time. But, from what I can tell, at the time of the survey, covid rates in the United States were higher among Hispanics than among Whites, which presumably means that Hispanic Americans were more likely to carry the virus than White Americans. It's not clear to me why misinformation about the covid rate among Asians should be prioritized over misinformation about the covid rate among Hispanics, although, if someone wanted to bias the research design against the political right, that priority would make sense.
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Similar flaw with the Young et al 2022 election 2020 misperceptions battery, which had an item that permits overestimation of the detected voter fraud ("There was widespread voter fraud in the 2020 Presidential election"), but had no item that would permit underestimation of voter fraud in 2020 (e.g., "There was no voter fraud in the 2020 Presidential election"), which is the type of error that the political left would presumably be more likely to make.
For another example, Young et al 2022 had a reverse-coded misperceptions item for "We can never be sure that Biden's win was legitimate", but had no item about whether we can be sure that Trump's 2016 win was legitimate, which would be an obvious item to pair with the Biden item to assess whether the political right and the political left are equally misinformed or at least equally likely to give insincere responses to surveys that have items such as "The coronavirus (COVID-19) vaccine will be used to implant people with microchips".
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So I think it's less, as Young et al 2022 suggested, that "COVID misinformation and election misinformation both served Republican political goals", and more that the selection of misinformation items in Young et al 2022 was biased toward a liberal-serving conclusion.
Of course, it's entirely possible that the political right is more misinformed than the political left in general or on selected topics. But it's not clear to me how Young et al 2022 can provide a valid inference about that.
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2.
For error, Young et al 2022 Table 3 has an unstandardized coefficient for Black race, indicating that, in the age 50 and older group, being Black corresponded to higher levels of Republicanism. I'm guessing that this coefficient is missing a negative sign, given that there is a negative sign on the standardized coefficient...The Table 2 income predictor for the age 18-49 group has an unstandardized coefficient of .04 and a standard error of .01, but no statistical significance asterisk, and has a standardized coefficient of .00, which I think might be too low...And the appendix indicates that "The analysis yielded two factors with Eigenvalues < 1.", but I think that should be a greater than symbol.
None of those potential errors are particularly important, except perhaps for inferences about phenomena such as the rigor of the peer and editorial review that Young et al 2022 went through.
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NOTES
1. Footnotes 3 and 4 of Young et al 2022 indicate that:
Consistent with Vraga and Bode (2020), misperceptions were operationalized as COVID-related beliefs that contradicted the "best available evidence" and/or "expert consensus" at the time data were gathered.
If the purpose is to assess whether "I feel it in my gut" people are incorrect, then the perceptions should be shown to be incorrect and not merely in contradiction to expert consensus or, for that matter, in contradiction to the best available evidence.
2. The funding statement for Young et al 2022 indicates that the study was funded by the National Institute of Aging.
3. Prior posts on politically biased selection of misinformation items, in Abrajano and Lajevardi 2021 and in the American National Election Studies 2020 Time Series Study.
4. After I started drafting the above post, Social Science Quarterly published Benegal and Motta 2022 "Overconfident, resentful, and misinformed: How racial animus motivates confidence in false beliefs", which used the politically biased ANES misinformation items, in which, for example, respondents who agree that "World temperatures have not risen on average over the last 100 years" get coded as misinformed (an error presumably more common on the political right) but respondents who wildly overestimate the amount of climate change over the past 100 years don't get coded as misinformed (an error presumably more common on the political left).
5. I might be crazy, but I think that research about the correlates of misperceptions should identify respondents who have correct perceptions instead of merely identifying respondents who have particular misperceptions.
And I don't think that researchers should place particular misperceptions into the same category as the correct perception, such as by asking respondents merely whether world temperatures have risen on average over the last 100 years, any more than researchers should ask respondents merely whether world temperatures have risen on average by at least 3 degrees Celsius over the last 100 years, for which agreement would be the misperception.
I reached ten new publications to comment on that I didn't think were worth a separate blog post, so here goes:
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1.
The Twitter account for the journal Politics, Groups, and Identities retweeted R.G. Cravens linking to two of his articles in Politics, Groups, and Identities. I blogged about one of these articles, discussing, among other things, the article's erroneous interpretation of interaction terms. The other article that R.G. Cravens linked to in that tweet ("The view from the top: Social acceptance and ideological conservatism among sexual minorities") also misinterpreted an interaction term:
However, the coefficient estimate for the interaction term between racial minority identity and racial identity group consciousness (β = −.312, p = .000), showing the effect of racial identity group consciousness only among racial minority respondents, indicates a negative relationship between racial minority group consciousness and conservatism at the 99% confidence level.
The corresponding Table 1 coefficient for RI Consciousness is 0.117, indicating the estimated effect of racial identity consciousness when the "Racial minority" variable is set to zero. The -0.312 interaction term indicates how much the estimated effect of racial identity consciousness *differs* between non-racial minorities and racial minorities, so that the estimated effect of racial identity consciousness among racial minorities is 0.117 plus -0.312, which is -0.195.
Two articles by one author in the same journal within three years, and each article misinterpreted an interaction term.
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2.
PS: Political Science & Politics published another article about student evaluations of teaching: Foster 2022 "Instructor name preference and student evaluations of instruction". The key finding seems plausible, that "SEIs were higher for instructors who preferred going by their first name...than for instructors who preferred going by 'Dr. Moore'" (p. 4).
But a few shortcomings about the reporting on the experiment in Study 2, which manipulated the race of an instructor, the gender of the instructor, and the instructor's stated preference for using his/her first name versus using his/her title and last name:
* Hypothesis 5 is about conservative Republicans:
Moderated mediation: We predict that female instructors who express a preference for going by "Dr. Moore" will have lower teacher ratings through decreased perceived immediacy, but only for students who identify as conservative and Republican.
But, as far as I can tell, the article doesn't report any data about Hypothesis 5.
* Table 2 indicates a positive p<0.05 correlation between the race of the instructor and SEIs (student evaluations of instruction) and a positive p<0.05 correlation between the race of the instructor and course evaluations. But, as far as I can tell, the article doesn't report how the race variable was coded, so it's not clear whether the White instructors or the Black instructors had the higher SEIs and course evaluations.
* The abstract indicates that:
Study 2 found the highest SEIs for Black male instructors when instructors asked students to call them by their first name, but there was a decrease in SEI scores if they went by their professional title.
But, as far as I can tell, the article doesn't report sufficient evidence about whether the estimated influence of the name preference among the Black male instructor targets differed from the estimated influence of the name preference among any of the comparison instructors. The p-value being under p=0.05 for the Black male instructor targets and not being under p=0.05 for the other instructor targets isn't enough evidence to infer at p<0.05 that participants treated the Black male instructor targets differently than participants treated the comparison instructor targets, so that the article doesn't report sufficient evidence to permit an inference of racial discrimination.
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3.
The Journal of Race, Ethnicity, and Politics published Enders and Thornton 2022 "Biased interviewer assessments of respondent knowledge based on perceptions of skin tone", which used data from the 2012 American National Election Studies Time Series Study, which seems really similar to the analysis in Hannon 2015 "White colorism". From Hannon 2015:
Using data from the 2012 American National Election Study, an example is presented on white interviewers' perceptions of minority respondent skin tone and intelligence (N = 223). Results from ordinal logistic regression analyses indicate that African American and Latino respondents with the lightest skin are several times more likely to be seen by whites as intelligent compared with those with the darkest skin.
Enders and Thornton 2022 cited three publications that had Hannon as a co-author: Hannon L and Deffna R 2014, Hannon L and DeFina R 2016, and Hannon L, Defina R and Bruch S 2013. But no citation to Hannon 2015.
And if it seems coincidental that Hannon would have "Deffna R", "DeFina R", and "Defina R" as co-authors, these all seem to be Robert DeFina.
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4.
From the abstract of Evans et al 2022 "Regions of discrimination: felony records, race, and expressed college admissions policies", in the Journal of Crime and Justice, about an audit experiment:
Findings indicate that admissions departments are more likely to tell an interested applicant with a stereotypical Black name and a non-violent felony record that their criminal histories will be considered in the application process compared to another prospective applicant with a stereotypical White name and non-violent felony record.
But coded responses in Table 3 indicate that 41% of responses to "Tyrone" indicated that a felony conviction is not considered and that 41% of responses to "Christopher" indicated that a felony conviction is not considered.
Table 4 indicates that, in an analysis with statistical control, "Tyrone" was more likely than "Christopher" to be told that a felony conviction is considered in a holistic review than to be told that a felony conviction is not considered, but that Table 4 analysis split the coding into three categories, of "Not considered", "Considered in a holistic review", and "Considered in a discretionary review", so that analysis doesn't seem to support the general abstract claim about "considered in the application process".
And I'm not sure that there is an important distinction between holistic and discretionary. Here is an example that Evans et al 2022 indicates is "discretionary":
Thank you for your interest and for reaching out. ____ is a residential college and students are required to live on campus all four years. We must ensure the safety of all of our constituents so we do take into account criminal history when reviewing for admission (coded as discretionary)
So it's not like "discretionary" means only that the criminal history might be taken into account.
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5.
I wasn't the only person to notice this next one (see tweets from Tom Pepinsky and Brendan Nyhan), but Politics & Gender recently published Forman-Rabinovici and Mandel 2022 "The prevalence and implications of gender blindness in quantitative political science research", which indicated that:
Our findings show that gender-sensitive analysis yields more accurate and useful results. In two out of the three articles we tested, gender-sensitive analysis indeed led to different outcomes that changed the ramifications for theory building as a result.
But the inferential technique in the analysis reflected a common error.
For the first of the three aforementioned articles (Gandhi and Ong 2019), Table 1a of Forman-Rabinovici and Mandel 2022 reported results with a key coefficient that was -.308 across the sample, was -.294 (p=.003) among men in the sample, and was -.334 (p=.154) among women in the sample. These estimates are from a linear probability model predicting a dichotomous "Support PH" outcome, so the point estimates were 29 percentage points among men and 33 percentage points among women.
The estimate was more extreme among women than among men, but the estimate was less precise among women than among men, at least partly because the sample size among men (N=1902) was about three times the sample size among women (N=652).
Figure 1 of Forman-Rabinovici and Mandel 2022 described these results as:
Male voters leave PH coalition
Female voters continue to vote for PH coalition
But, in my analysis of the data, the ends of the 95% confidence interval for the estimate among women indicated an 82 percentage point decrease and a 15 percentage point increase [-0.82, +0.15], so that's not nearly enough evidence to infer a lack of an effect among women.
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6.
Politics & Gender published another article that has at least a misleading interpretation of interaction terms: Kreutzer 2022 "Women's support shaken: A study of women's political trust after natural disasters".
Table 1 reports results for three multilevel mixed-effects linear regressions, with coefficients on a "Number of Disasters Present" predictor of 0.017, 0.009, and 0.022. The models have a predictor for "Female" and an interaction of "Female" and "Number of Disasters Present" with interaction coefficients of –0.001, –0.002, and –0.001. So the combination of coefficients indicates that the associations of "Number of Disasters Present" and the "trust" outcomes are positive among women, but not as positive as the associations are among men.
Kreutzer 2022 discusses this correctly in some places, such as indicating that the interaction term "allows a comparison of how disasters influence women's political trust compared with men's trust" (p. 15). But in other places the interpretation is, I think, incorrect or at least misleading, such as in the abstract (emphasis added):
I investigate women's trust in government institutions when natural disasters have recently occurred and argue that because of their unique experiences and typical government responses, women's political trust will decline when there is a natural disaster more than men's. I find that when there is a high number of disasters and when a larger percentage of the population is affected by disasters, women's political trust decreases significantly, especially institutional trust.
Or from page 23:
I have demonstrated that natural disasters create unique and vulnerable situations for women that cause their trust in government to decline.
And discussing Figure 5, referring to a different set of three regressions (reference to footnote 12 omitted):
The figure shows a small decline in women's trust (overall, institutional, organizational) as the percentage of the population affected by disasters in the country increases. The effect is significantly different from 0, but the percentage affected seems not to make a difference.
That seems to say that the percentage of the population affected has an effect that is simultaneously not zero and does not seem to make a difference. I think Figure 5 marginal effects plots indicate that women have lower trust than men (which is why each point estimate line falls in the negative range), but that this gender difference in trust does not vary much by the percentage of the population affected (which is why the each point estimate line is pretty much flat).
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The "Women's Political Empowerment Index" coefficient and standard error are –0.017 and 0.108 in Model 4, so maybe the ** indicating a two-tailed p<0.01 is an error.
Tweet to the author (Oct 3). No reply yet.
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7, 8.
Let's return to Politics, Groups, and Identities, for Ditonto 2019 "Direct and indirect effects of prejudice: sexism, information, and voting behavior in political campaigns". From the abstract:
I also find that subjects high in sexism search for less information about women candidates...
At least in the reported analyses, the comparison for "less" is to participants low in sexism instead of to male candidates. So we get this result discussing Table 2 (pp. 598-599):
Those who scored lowest in sexism are predicted to look at approximately 13 unique information boxes for the female candidate, while those who scored highest are predicted to access about 10 items, or almost 1/3 less.
It should be obvious to peer reviewers and any editors that a comparison to the male candidates in the experiment would be a more useful comparison for assessing the effect of sexism, because, for all we know, respondents high in sexism might search for less information than respondents low in sexism search for, no matter the gender of the candidate.
Ditonto has another 2019 article in a different journal (Political Psychology) based on the same experiment: "The mediating role of information search in the relationship between prejudice and voting behavior". From that abstract:
I also find that subjects high in prejudice search for less information about minority candidates...
But, again, Table 2 in that article merely indicates that symbolic racism negatively associates with information search for a minority candidate, with no information provided about information search for a non-minority candidate.
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And I think that the Ditonto 2019 abstracts include claims that aren't supported by results reported in the article. The PGI abstract claims that "I find that subjects with higher scores on items measuring modern sexism...rate female candidates more negatively than their male counterparts", and the PP abstract claims that "I find that subjects higher in symbolic racism...rate minority candidates more negatively than their white counterparts".
By the way, claims about respondents high in sexism or racism should be assessed using data only from respondents high in sexism or racism, because the association of a sexism or racism measure with an outcome might be completely due to respondents low in sexism or racism.
Tweet to the author (Oct 9). No reply yet.
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9.
Below is a passage from "Lower test scores from wildfire smoke exposure", by Jeff Wen and Marshall Burke, published in 2022 in Nature Sustainability:
When we consider the cumulative losses over all study years and across subgroups (Fig. 4b), we estimate the net present value of lost future income to be roughly $544 million (95% CI: −$999 million to −$100 million) from smoke PM2.5 exposure in 2016 for districts with low economic disadvantage and low proportion of non-White students. For districts with high economic disadvantage and high proportion of non-White students, we estimate cumulative impacts to be $1.4 billion (95% CI: −$2.3 billion to −$477 million) from cumulative smoke PM2.5 exposure in 2016. Thus, of the roughly $1.7 billion in total costs during the smokiest year in our sample, 82% of the costs we estimate were borne by economically disadvantaged communities of colour.
So, in 2016, the lost future income was about $0.5 billion for low economic disadvantage / low non-White districts and $1.4 billion for high economic disadvantage / high non-White districts; that gets us to $1.9 billion, without even including the costs from low/high districts and high/low districts. But total costs were cited as roughly $1.7 billion.
From what I can tell from Figure 4b, the percentage of total costs attributed to economically disadvantaged communities of color (the high / high category) is 59%. It's not a large inferential difference from 82%, in that both estimates are a majority, but it's another example of an error that could have been caught by careful reading.
Tweet to the authors about this (Oct 17). No reply yet.
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10.
Political Research Quarterly published "Opening the Attitudinal Black Box: Three Dimensions of Latin American Elites' Attitudes about Gender Equality", by Amy Alexander, Asbel Bohigues, and Jennifer M. Piscopo.
I was curious about the study's measurement of attitudes about gender equality, and, not unexpectedly, the measurement was not good, using items such as "In general, men make better political leaders than women", in which respondents can agree that men make better political leaders, can disagree that men make better political leaders, and can be neutral about the claim that men make better political leaders...but respondents cannot report the belief that, in general, women make better political leaders than men do.
I checked the data, in case almost no respondent disagreed with the statement that "In general, men make better political leaders than women", in which case presumably no respondent would think that women make better political leaders than men do. But disagreement with the statement was pretty high, with 69% strongly disagreeing, another 15% disagreeing, and another 11% selecting neither agree nor disagree.
I tweeted a question about this to some of the authors (Oct 21). No reply yet.
Some research includes measures of attitudes about certain groups but not about obvious comparison groups, such as research that includes attitudes about Blacks but not Whites or includes attitudes about women but not men. Feeling thermometers can help avoid this, which I'll illustrate with data from the Democracy Fund Voter Study Group's Views of the Electorate Research (VOTER) Survey.
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The outcome is this item, from the 2018 wave of the VOTER survey:
Do you approve or disapprove of football players protesting by kneeling during the national anthem?
I coded responses 1 for strongly approve and somewhat approve and 0 for somewhat disapprove, strongly disapprove, don't know, and skipped. The key predictor was measured in 2017 and is based on 0-to-100 feeling thermometer ratings about Blacks and Whites, coded into six categories:
* Rated Whites equal to Blacks
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* Rated Whites under 50 and Blacks at 50 or above
* Residual ratings of Whites lower than Blacks
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* Rated Blacks under 50 and Whites at 50 or above
* Residual ratings of Blacks lower than Whites
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* Did not rate Whites and/or Blacks
The plot below controls for only participant race measured in 2018, with error bars indicating 83.4% confidence intervals and survey weights applied.
The plot suggests that attitudes about anthem protests associated with negative attitudes about Blacks and with negative attitudes about Whites. These are presumably obvious results, but measures such as racial resentment probably won't be interpreted as suggesting both results.
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NOTE
1. Stata code and output. The output reports results that had more extensive statistical control.