Social Science Quarterly recently published Cooper et al. 2021 "Heritage Versus Hate: Assessing Opinions in the Debate over Confederate Monuments and Memorials". The conclusion of the article notes that:

...we uncover significant evidence that the debate over Confederate monuments can be resoundingly summarized as "hate" over "heritage"

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In a prior post, I noted that:

...when comparing the estimated effect of predictors, inferences can depend on how well each predictor is measured, so such analyses should discuss the quality of the predictors.

Cooper et al. 2021 measured "heritage" with a dichotomous predictor and measured "hate" with a five-level predictor, and this difference in the precision of the measurements could have biased their research design toward a larger estimate for hate than for heritage. [See note 3 below for a discussion].

I'm not suggesting that the entire difference between their estimates for heritage and hate is due to the number of levels of the predictors, but I think that a better peer review would have helped eliminate that flaw in the research design, maybe by requiring the measure of hate to be dichotomized as close as possible to 70/30 like the measure of heritage was.

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Here is the lone measure of heritage used in Cooper et al. 2021:

"Do you consider yourself a Southerner, or not?"

Table 1 of the article indicates that 70% identified as a Southerner, so even if this were a face-valid measure of Southern heritage, the measure places into the highest level of Southern heritage persons at the 35th percentile of Southern heritage.

Maybe there is more recent data that undercuts this, but data from the Spring 2001 Southern Focus Poll indicated that only about 1 in 3 respondents who identified as a Southerner indicated that being a Southerner was "very important" to them. About 1 in 3 respondents who identified as a Southerner in that 2001 poll indicated that being a Southerner was "not at all important" or "not very important" to them, and I can't think of a good reason why, without other evidence, these participants belong in the highest level of a measure of Southern heritage.

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Wright and Esses 2017 had a more precise measure for heritage and found sufficient evidence to conclude that (p. 232):

Positive attitudes toward the Confederate battle flag were more strongly associated with Southern pride than with racial attitudes when accounting for these covariates.

How does Cooper et al. 2021 address the Wright and Esses 2017 result, which conflicts with the result from Cooper et al. 2021 and which used a related outcome variable and a better measure of heritage? The Cooper et al. 2021 article doesn't even mention Wright and Esses 2017.

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A better peer review might have caught the minimum age of zero years old in Table 1 and objected to the description of "White people are currently under attack in this country" as operationalizing "racial resentment toward blacks" (pp. 8-9), given that this item doesn't even mention or refer to Blacks. I suppose that respondents who hate White people would be reluctant to agree that White people are under attack regardless of whether that is true. But that's not the "hate" that is supposed to be measured.

Estimating the effect of "hate" for this type of research should involve comparing estimates net of controls for respondents who have a high degree of hate for Blacks to respondents who are indifferent to Blacks. Such estimates can be biased if the estimates instead include data from respondents who have more negative feelings about Whites than about Blacks. In a prior post, I discussed Carrington and Strother 2020, which measured hate with a Black/White feeling thermometer difference and thus permitted estimation of how much of the effect of hate is due to respondents rating Blacks higher than Whites on the feeling thermometers.

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Did Cooper et al. have access to better measures of hate than the item "White people are currently under attack in this country"? The Winthrop Poll site didn't list the Nov 2017 survey on its archived poll page for 2017. But, from what I can tell, this Winthrop University post discusses the survey, which included a better measure of racial resentment toward blacks. I don't know what information the peer reviewers of Cooper et al. 2021 had access to, but, generally, a journal reform that I would like to see for manuscripts reporting on a survey is for peer reviewers to be given access to the entire set of items for a survey.

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In conclusion, for a study that compares the estimated effects of heritage and hate, I think that at least three things are needed: a good measure of heritage, a good measure of hate, and the good measure of heritage being of similar quality to the good measure of hate. I don't think that Cooper et al. 2021 has any of those things.

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NOTES

1. The Spring 2001 Southern Focus Poll study was conducted by the Odum Institute for Research in Social Science of the University of North Carolina at Chapel Hill. Citation: Center for the Study of the American South, 2001, "Southern Focus Poll, Spring 2001", https://hdl.handle.net/1902.29/D-31552, UNC Dataverse, V1.

2. Stata output.

3. Suppose that mean support for leaving Confederate monuments as they are were 70% among the top 20 percent of respondents by Southern pride, 60% among the next 20 percent of respondents by Southern pride, 50% among the middle 20 percent, 40% among the next 20 percent, and 30% among the bottom 20 percent of respondents by Southern pride. And let's assume that these bottom 20 percent are indifferent about Southern pride and don't hate Southerners.

The effect of Southern pride could be estimated at 40 percentage points, which is the difference in support among the top 20 percent and bottom 20 percent by Southern pride. However, if we grouped the top 60 percent together and the bottom 40 percent together, the mean percentage support would respectively be 60% and 35%, for an estimated effect of 25 percentage points. In this illustration, the estimated effect for the five-level predictor is larger than the estimate for the dichotomous predictor, even with the same data.

Here is a visual illustration:

The above is a hypothetical to illustrate the potential bias in measuring one predictor with five levels and another predictor with two levels. I have no idea whether this had any effect on the results reported in Cooper et al. 2021. But, with a better peer review, readers would not need to worry about this type of bias in the Cooper et al. 2021 research design.

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I received a few questions and comments about my use of 83.4% confidence intervals on the plot in my prior post, so I thought I would post an explanation that I can refer to later.

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Often, political scientists use a p-value of p=0.05 as a threshold for sufficient evidence of an association, such that only p-values under p=0.05 indicate sufficient evidence. Plotting 95% confidence intervals can help readers assess whether the evidence indicates that a given estimate differs from a given value.

For example, in unweighted data from the ANES 2020 Time Series Study, the 95% confidence interval for Black respondents' mean rating about Whites is [63.0, 67.0]. The number 62 falls outside the 95% confidence interval, so that indicates that there is sufficient evidence at p=0.05 that Black respondents' mean rating about Whites is not 62. However, the number 64 falls inside the 95% confidence interval, so that indicates that there is not sufficient evidence at p=0.05 that the mean rating about Whites among Black respondents is not 64.

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But suppose that we wanted to assess whether two estimates differ *from each other*. Below is a plot of 95% confidence intervals for Black respondents' mean rating about Whites and about Asians, in unweighted data. For a test of the null hypothesis that the estimates differ from each other, the p-value is p=0.04, indicating sufficient evidence of a difference. However, the 95% confidence intervals overlap quite a bit.

The 95% confidence intervals in this case don't do a good job of permitting readers to assess differences between estimates at the p=0.05 level.

But below is a plot that instead uses 83.4% confidence intervals. The ends of the 83.4% confidence intervals come close to each other but do not overlap. If using confidence interval touching as an approximation to p=0.05 evidence of a difference, that closeness without overlapping is what we would expect from a p-value of p=0.04.

Based on whether 83.4% confidence intervals overlap, readers can often get a good sense whether estimates differ at p=0.05. So my current practice is to plot 95% confidence intervals when the comparison of interest is of an estimate to a given number and to plot 83.4% confidence intervals when the comparison of interest is of one estimate to another estimate.

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NOTES

1. Data source: American National Election Studies. 2021. ANES 2020 Time Series Study Preliminary Release: Combined Pre-Election and Post-Election Data [dataset and documentation]. March 24, 2021 version. www.electionstudies.org.

2. R code for the plots.

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