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Month: October 2020

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The American National Election Studies Time Series Cumulative Data File (1948-2016) contains data for feeling thermometer measures for Whites and for Blacks, collected in face-to-face or telephone interviews, for each U.S. presidential election year from 1964 to 2016.

Feeling thermometers range from 0 to 100, with higher values indicating warmer or more favorable feelings about a group. The ANES Cumulative Data File and some early individual year ANES Time Series files collapse responses of 97 through 100 into a response of 97. This means that a respondent who selected 97 for Whites and 100 for Blacks would have the same "difference" value as a respondent who selected 100 for Whites and 97 for Blacks. Therefore, I placed respondents with a substantive value for the feeling thermometer about Whites and the feeling thermometer about Blacks into one of three categories:

  • rated Whites more than 3 units above Blacks
  • rated Whites within 3 units of Blacks, and
  • rated Blacks more than 3 units above Whites.

Abrajano and Alvarez (2019) reported evidence from ANES Time Series Studies that responses to racial feeling thermometers differed between the non-internet mode and the internet mode, so my reported results do not include results from the internet mode, which do not go back to 1964.

Below is a plot of how Whites Americans (left) and Black Americans (right) fell into each of the three categories, not including the respondents in the cumulative data file who did not report a substantive response to the items, which ranged from 1% to 8% (see the Notes). Documentation for the cumulative data file indicated that in 1964 and 1968 a response was recorded as 50 for a "don't know" response or if the participant indicated that the participant did not know too much about a group.

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The plot below indicates how these thermometer ratings associated with two-party vote choice, among White participants:

The right panel indicates a steep drop in two-party vote for the Republican presidential candidate among Whites who rated Blacks more than 3 units higher than Whites, which seems to be consistent with evidence of a "Great Awokening" (see, e.g., Yglesias 2019 and Goldberg 2019, and this image linked to in Goldberg 2019).

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The plot below is the plot above, but with columns grouped by year:

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NOTES

1. Percentage non-responses to one or both thermometer items, by year: 3% (1964), 4% (1968), 8% (1972), 5% (1976), 5% (1980), 7% (1984), 5% (1988), 4% (1992), 4% (1996), 8% (2000), 3% (2004), 3% (2008), 1% (2012), 2% (2016).

2. Code for my analyses and black-and-white plots.

3. Feeling thermometer ratings about Chicanos/Hispanics and about Asians are not available in ANES Time Series Cumulative Data File until 1976 and 1992, respectively.

4. A color version of the first plot, for comparison:

5. A color version with a black line divider:

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The Adida et al. 2020 PS: Political Science & Politics article "Broadening the PhD Pipeline: A Summer Research Program for HBCU Students" claimed that (p. 727):

The US academy today is overwhelmingly white, with only 8% to 9% of full-time science and engineering faculty as underrepresented minorities (DePass and Chubin 2008, 6).

This evidence to support the claim that the "US academy" is "overwhelmingly white" is the percentage of a *subset* of the U.S. academy (science and engineering) that is not White *and not Asian*, given that Asians were not considered underrepresented minorities in the calculation of the percentage. Moreover, the cited publication is more than a decade old, and the data might be even older than that.

Below is a plot of data from 2018, for the U.S. academy as a whole, of data from the National Center for Educational Statistics. The light areas indicate the percentage White for each rank and overall, compared to the total White, Black, Hispanic, Asian, Pacific Islander, and persons of two or more races; the percentage does not include persons with an unknown race/ethnicity and does not include non-resident aliens.

Overall, in Fall 2018, about 76% of U.S. full time faculty at U.S. degree-granting postsecondary institutions were White, which matches a calculation in this Pew study or Fall 2017. So, if you randomly selected four of these faculty, one of them would be expected to be non-White. I'm not sure whether that counts as being "overwhelmingly white".

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NOTES

1. R code for the plot.

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The average eighth grade math score on the 2019 National Assessment of Educational Progress (NAEP) was 310 for Asian/Pacific Islander students, 292 for White students, 268 for Hispanic students, and 260 for Black students. This pattern has been consistent for many years, for fourth grade students (Figure 3), for eighth grade students (Figure 4), and for twelfth grade students (Figure 5).

However, before inferring that Asian/Pacific Islander students are better in math on average than are White students and Hispanic students and Black students, be aware that this inference could be labeled "prejudice" in peer-reviewed research such as Piston 2010 and Hopkins and Washington 2020, which measured "prejudice" as a difference in ratings of groups on stereotype scales for certain characteristics.

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Piston 2010 conceptualized "prejudice" with "an etymological perspective":

An assessment that one racial group possesses a negative attribute relative to another racial group is a "pre-judgment"; it precedes, but may or may not influence, the evaluation of an individual member of that group, such as Barack Obama.

So, if you make a good faith interpretation of NAEP scores and/or SAT scores and infer that Asian/Pacific Islander students are better on average in math than are White students and Hispanic students and Black students, that would be "prejudice" by the analysis in Piston 2010.

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Your responses might not be "prejudice" based on Hopkins 2019:

We define prejudice as a standing, negative predisposition toward a social group held in the face of contradictory information.

Based on this, Hopkins 2019 seems to require evidence that Asian/Pacific Islander students are not better on average in math than are White students and Hispanic students and Black students ("contradictory information") before labeling that belief as "prejudice".

I asked Dan Hopkins in a tweet what "contradictory information" he was referring to for his use of "prejudice", and, perhaps as a consequence, Hopkins and Washington 2020 removed the "held in the face of contradictory information" restriction. From Hopkins and Washington 2020:

'Prejudice' refers to a standing, negative predisposition toward a social group.

So, by Hopkins and Washington 2020, it would be "prejudice" to have a justified standing, negative predisposition toward a hate group that regularly commits terrorism. That might be a proper conceptualization of "prejudice", but I would be interested in seeing Hopkins or Washington use "prejudice" in that way.

Hopkins and Washington 2020 used stereotype scale differences as measures of "prejudice", but it seems possible to perceive that members of one group perform better on average on some measure than members of another group, without having a "standing, negative predisposition" toward either group, especially because nothing in these traditional stereotype scales indicates that the scales measure belief about innate or genetic characteristics.

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From what I can tell, the belief that U.S. Asian/Pacific Islander students are better in math on average than are White students and Hispanic students and Black students would be "prejudice" under the conceptualizations in Piston 2010 and Hopkins and Washington 2020, even though I think that this belief can result from a good faith interpretation of high quality evidence. I thus think that use of the conceptualizations of "prejudice" in Piston 2010 or Hopkins and Washington 2020 has the potential to be misleading and to corrode public discourse.

The potential to mislead is because I think that "prejudice" has a negative connotation in everyday language, and I don't think that a good faith interpretation of high quality evidence should have a label that has a negative connotation. I am not aware of anything that prevents researchers from labeling such stereotype scale responses as "stereotype scale differences" or something similar that would more precisely describe the phenomenon being measured.

The potential to corrode public discourse is the potential that fear of application of the "prejudice" label can make people less likely to express beliefs that have been derived from a good faith interpretation of high quality evidence, and I don't think that, barring some compelling reason otherwise, people should be discouraged from expressing a belief that has been derived from a good faith interpretation of high quality evidence.

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