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The Intelligence Gap between Black and White Survey Workers on the Prolific Platform

Authors:
  • Ulster Institute for Social Research

Abstract

This brief report analyzes data from a series of studies carried out by Bates and Gignac (2022), collected from paid survey takers on the Prolific platform (total n = 3357). In this UK sample, Black-White gap sizes on cognitive tests were substantial with an overall effect size d of 0.99 standard deviations adjusted for unreliability (unadjusted means = 0.84 d). Testing for measurement invariance via differential item functioning found either no bias or bias of trivial magnitude. We conclude that the Black-White intelligence gap seen in Prolific workers is of similar magnitude to the gap seen elsewhere in America.
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The Intelligence Gap between Black and White Survey
Workers on the Prolific Platform
Emil O.W. Kirkegaard*
Ulster Institute for Social Research, London
* Address for correspondence: the.dfx@gmail.com
This brief report analyzes data from a series of studies carried out by
Bates and Gignac (2022), collected from paid survey takers on the Prolific
platform (total n = 3357). In this UK sample, Black-White gap sizes on
cognitive tests were substantial with an overall effect size d of 0.99
standard deviations adjusted for unreliability (unadjusted means = 0.84 d).
Testing for measurement invariance via differential item functioning found
either no bias or bias of trivial magnitude. We conclude that the Black-
White intelligence gap seen in Prolific workers is of similar magnitude to
the gap seen elsewhere in America.
Key Words: Black-White gap, Race, Intelligence, Cognitive ability,
Differential item functioning, Measurement invariance, Test bias, Survey,
Questionnaire, Prolific
Ethnic/racial groups vary in their average levels of intelligence. The
differences are generally, but not entirely, consistent across time and place (Lynn,
2008, 2015). Of particular interest is the difference between Africans and
Europeans, as these groups are present in large numbers in the United States,
Canada, United Kingdom and increasingly elsewhere. Historically, this is also the
best studied ethnic difference, with studies dating back over more than a century
in the United States. Overall, the gap size is about 15 IQ points in the United
States and has been stable since it was first measured (Roth et al., 2001). This
difference was still present in multiple large, broadly representative samples in
recent years, showing that claims of substantial narrowing were mistaken (Frisby
& Beaujean, 2015; Fuerst et al., 2021; Kirkegaard et al., 2019; Lasker et al.,
2019). This is in contrast to findings and predictions by Dickens and Flynn (2006).
Recently, Murray (2021) computed the Black (African American) and White
(European American) IQ gaps for various occupations. His findings are
reproduced in Table 1.
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Table 1. Mean occupation IQs by race in the U.S., copied from Murray (2021).
100 is defined as the population mean, not the White mean. Gaps within
occupations are in Cohen’s d values using the standard deviations of those
subjects, so they reflect restriction of variance inside groups. Data derived from
NLSY79 and NLSY97.
Occupation
European
African
Latino
Euro-African
d
Euro-Latino
d
Accountants
111
100
104
0.96
0.60
K12 teachers
110
95
101
1.35
0.76
Registered nurses
109
94
93
0.93
1.09
Social workers
105
95
93
0.93
1.09
Retail sales
workers
102
89
93
1.17
0.80
Child care
workers
102
83
85
1.55
1.34
Secretaries & AAs
102
90
93
0.96
0.72
Vehicle
mechanics
94
83
87
0.85
0.57
Janitors &
cleaners
92
79
82
1.10
0.78
Median
102
90
93
1.10
0.76
Mean
103
90
94
1.15
0.79
Social science increasingly relies on survey data gathered online. It is
possible that the use of online surveys induces selection bias towards smarter
subjects. A recent study found that, compared to a representative norming
sample, paid survey workers at Amazon’s MTurk platform
(https://www.mturk.com/) had an average IQ (100) very close to the norming
sample (Merz et al., 2022). As far as we are aware, there is no published study
of whether the well-known ethnic gaps in intelligence are also present on the
Prolific platform, a rival to Amazon’s MTurk that is marketed at Academic
Research (https://www.prolific.co/; Palan & Schitter, 2018). This is an academic-
focused platform for buying and selling survey data. Subjects can join the platform
and participate in ongoing survey studies. Researchers can similarly run studies
on the platform. Prior research has found that data from online samples work
similarly to traditional student samples, but are more representative. Prolific offers
nationally representative data in terms of characteristics such as age, sex, race,
KIRKEGAARD, E.O.W. BLACK/WHITE INTELLIGENCE GAP
81
and education. The purpose of the present study was to examine the Black-White
gap on data derived from this platform.
Data
We used data from a recent study of the effect of motivation on intelligence
test scores (Bates & Gignac, 2022), with research subjects from the United
Kingdom (T. Bates, personal communication). The study had multiple
subsamples, as described in the study:
(studies 1a b)
Subjects in all studies were recruited from Prolific Academic, a crowd
sourcing online platform to recruit human subjects for research purposes.
For study 1a, we recruited 1001 adult subjects (age M = 28.41, SD = 6.04;
range: 18 to 39 years, 499 male and 499 female, 2 did not answer this
item). For study 1b, we recruited 1000 adult subjects (age M = 34.49, SD
= 11.75; range: 18 to 76 years) from Prolific Academic (497 male and 503
female). The sample was predominantly white (White = 89.7%; Asian =
4.5%; Black = 1.8%; South-East Asian = 1.4%; Other = 2.6%). For study
1c, we recruited 1006 adult subjects (age M = 24.31, SD = 4.79; range: 18
to 39 years) from Prolific Academic (502 male and 504 female). The
sample composition was: White = 41.5%; Asian = 0.9%; Black = 35.3%;
South-East Asian = 0.4%; Native American = 0.9%; Other = 21.1%.
(study 2)
We recruited 400 adult subjects (age M = 29.75, SD = 5.90; range: 18 to
40 years) from Prolific Academic (202 male and 198 female). The sample
was predominantly white (White = 92.5%; Asian = 3.0%; Black = 1.3%;
South East Asian = 0.5%; Other = 2.8%).
(study 3)
We recruited 801 adult subjects (age M = 36.11, SD = 12.89; range: 18 to
76 years) from Prolific Academic (402 male and 399 female). The sample
was predominantly white (White = 89.0%; Asian = 4.6%; Black = 2.2%;
South-East Asian = 1.0%; Other = 3.1%).
(study 4)
We recruited an additional 150 adult subjects (age M = 28.83, SD = 6.24;
range: 18 to 39 years) from Prolific Academic (75 male and 75 female).
The sample was predominantly white (White = 85.3%; Asian = 7.3%; Black
= 3.3%; South-East Asian = 0.7%; Other = 3.1%).
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The first sample (1a) did not include any questions about race, so we were unable
to use the data from that study. Sample 1c oversampled Blacks to reach a
substantial percentage (35.3%), whereas 1b, 2, 3, and 4 sampled freely from
adults on the platform, resulting in very small percentages of Blacks (1.8% to
3.3%). The different studies used different, abbreviated tests (10 to 32 items):
(studies 1a c)
Study 1b used the test of Single Word Comprehension (Warrington et al.,
1998). This test consists of 52 target words, each presented with two
potential response words arranged below them, and for each target must
select the word which is the best synonym (e.g., MARQUEE: Tent;
Palace). Half are concrete and half abstract. Based on item-level data, we
created a short form with 13 concrete items and 12 abstract items.
Coefficient ω in our sample was 0.62. Study 1C used Form A (10-items)
of the 20-item Visual Paper Folding test (Ekstrom et al., 1976). Dating
back in form not only to the work of Thurstone, but at least as early as
Binet (1905/1916), this scale consists of illustrations depicting a square
sheet of paper being folded two or three times and a hole punched in it.
The task is to select which of 5 graphical response options depicts how
the holes would appear if the sheet was unfolded. Matched versions are
provided as part of the Kit of Factor-Referenced Cognitive Tests. Each
block consisted of 10 items with a 3-minute time limit. Coefficient ω in our
sample was 0.68.
(studies 2 4)
Intelligence was measured using Form A and Form B of the Visual Paper
Folding test (Ekstrom et al., 1976). Each form includes 10 items (3-minute
time limit) and the forms are calibrated as approximately equally difficult.
For further details, see Study 1. For the total sample, coefficient ω was
estimated: Form A = 0.688; Form B = 0.627. Effort was measured using
the 10-item Sundre and Thelk (2007) Student Opinion Scale Internal
consistency reliability was estimated at ω = 0.81 and 0.80 for the pre- and
post-effort conditions. The reliabilities for each experimental condition are
included in Table 2.
As studies 2 through 4 used the same intelligence test items, they were
combined into a single dataset (by the original users of the data). This left us with
3 samples to examine: 1b, 1c, and the combined 2 4. The data was published
in the source article’s repository (https://osf.io/5uesw/?view_only=
705617acaf734286844b1521ed87afdc), which is where we obtained it. The data
KIRKEGAARD, E.O.W. BLACK/WHITE INTELLIGENCE GAP
83
and R code from the present study can be found at https://osf.io/urx8f/, and the R
notebook can also be found at https://rpubs.com/EmilOWK/black_white_Prolific.
Analysis
Our approach was the same across datasets:
1. Fit an item response model to the full dataset (including subjects who were
neither Black nor White).
2. Compute the g factor scores based on a g-only model. Standardize the scores
to the White subset.
3. Test the items for differential item functioning using the approach outlined by
Chalmers (2015), which was previously used in Dutton and Kirkegaard (2022),
Kirkegaard (2021), and Lasker et al. (2021). This was done using the mirt
package (Chalmers et al., 2020).
4. Compute the Black-White gap size in White standard deviations. Additionally,
compute the reliability-adjusted gap size based on the estimated reliability of
the test. This correction is done by converting the d value to a point biserial
correlation, adjusting for imperfect reliability using the Spearman correction,
and converting back to a d value. Reliability was estimated using the item
response theory based method implemented in the function empirical_rxx().
5. Bootstrap the confidence intervals and standard errors for the gap sizes (1000
resamples).
Finally, we used the output of step (5) to perform a Hunter-Schmidt
psychometric meta-analysis of the various results. Table 2 shows the results.
Table 2. Black-White test score gaps by sample. d adjusted refers to values
adjusted for imperfect reliability. CI = confidence interval (bootstrap, centile
method). The test bias effect size was estimated with differential item functioning
partial fits and positive values indicate higher scores for the White group. Note
that one item was excluded from sample 1b because it had no variance in the
Black group, thus leaving 24/25 items.
Sample
n total
White/Black
d adjusted
(95% CI)
Test bias d
Test reliability
Test items
1b
1000
897/18
0.91
(0.38 - 1.51)
0
.65
24
1c
1006
417/355
1.00
(0.86 - 1.07)
0.05
.76
10
2 4
1351
1211/28
0.88
(0.55 - 1.25)
0
.83
20
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Almost all the information about the gap is coming from sample 1c, which
had a fairly balanced split between Black and White subjects. Yet the three
samples found much the same magnitudes when measurement error was
adjusted for. We carried out the random effects meta-analysis using metafor
(Viechtbauer, 2015), which resulted in an overall adjusted effect size of 0.99 d
(95CI: 0.89 1.09) corresponding to 14.8 IQ if one assumes the standard
deviations are identical to the general population. The unadjusted overall effect
size was 0.83 d (0.75 0.92). There was no detectable heterogeneity in either
analysis (p > .05, I² = 0%).
With regards to test bias, a small test-level bias of 0.05 was found in the large
sample. This was due to two biased items, one favoring each group (see Figure
1). However, the one favoring Whites was somewhat stronger in its effect
resulting in a 0.05 d bias at the test level, favoring Whites. This, however, is fairly
trivial compared to the 1.00 d gap between Blacks and Whites.
Figure 1. Item plots for differential item functioning analysis. Items PFA10 (top)
and PFA3 (bottom) show bias favoring Blacks and Whites, respectively.
Discussion
Our analysis of a moderately large online sample of UK adults from the
Prolific platform showed that the Black-White intelligence gap observed in
population-representative samples was also present here. This is not surprising
KIRKEGAARD, E.O.W. BLACK/WHITE INTELLIGENCE GAP
85
given that it is present inside many other occupations studied, a nearly-necessary
consequence of the population-level differences coupled with racially fair
selection (Murray, 2021; Roth et al., 2001). My meta-analysis of the three
samples found an overall gap size of 0.99 d (14.82 IQ points) when adjusted for
imperfect reliability. Adjusting for measurement error is important so as not to be
misled by differences between samples in measurement reliability, which would
otherwise show up as artificial heterogeneity between samples (Hunter &
Schmidt, 2015). If one does not adjust, the overall effect size is somewhat
reduced to 0.83 d (12.53 IQ points).
However, the moderately large size of the gap is remarkable considering that
according to Lynn and Fuerst (2021) most of the recent studies of adolescents in
the UK have shown smaller Black-White gaps of less than 10 points and in some
cases less than 5 points. Because these authors also report that the Black-White
gap diminished over time, it is likely that much of the discrepancy between their
adolescent data and the adult Prolific workers in the present study can be
explained as a cohort effect, with Flynn effects being stronger in the black than
the white population.
Because intelligence test scores are a strong predictor of job performance
(Schmidt et al., 2016), ethnic gaps in mean intelligence within occupations will
also result in job performance differences; these have also been meta-analyzed
(Roth et al., 2003, 2008). For survey workers, job performance would consist of
following instructions, not using multiple accounts, not filling in responses
dishonestly and so on (Arthur et al., 2021). Given the intelligence gap, we expect
that survey data quality from Black subjects should be somewhat lower quality
than data gathered from White subjects. This can be important because lower-
quality survey responses might result in differential reliability for psychological
scales or tests by race, which could create spurious interactions between race
and other variables as the strength of association would differ by race due to the
differential reliability. In terms of actual results, a number of studies have
compared self-report to objective measures of drug use. These have found that
Blacks and Hispanics more often lie or otherwise falsely report their drug usage
as compared with urine test results than do White subjects (Fendrich & Johnson,
2005; Hughes et al., 2010). A number of studies have found that scores on lying
scales of various tests are higher for Blacks and Hispanics than for Whites in the
United States (Pina et al., 2001; Reynolds & Paget, 1983; Reynolds & Richmond,
1978). Taken together, there is reason for caution when interpreting self-report
based findings that might instead be explained by differences in survey data
quality.
The limitations of the study include, first, the open sampling of American
adults on the platform, as opposed to a nationally representative sample,
MANKIND QUARTERLY 2022 63.1
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something which Prolific also offers for a higher price. Second, the tests used
were very short. It would be better to use a more diverse set of items, though this
has to be balanced against the cost of buying the data. As we did not collect the
data, we could not have chosen a different trade-off. Third, the use of a spatial
test might have inflated the Black-White gap because Blacks have been found to
underperform especially strongly on spatial tests (Frisby & Beaujean, 2015), and
thus, if the sample is representative of the general population, it might be
consistent with a substantial narrowing of the Black-White gap.
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There are few empirically derived theories explaining group differences in cognitive ability. Spearman's hypothesis is one such theory which holds that group differences are a function of a given test's relationship to general intelligence, g. Research into this hypothesis has generally been limited to the application of a single method lacking sensitivity, specificity, and the ability to assess test bias: Jensen’s method of correlated vectors. In order to overcome the resulting empirical gap, we applied three different psychometrically sound methods to examine the hypothesis among American blacks and whites in the Vietnam Experience Study (VES) and the National Longitudinal Survey of Youth 1979 (NLSY ‘79). We first used multi-group confirmatory factor analysis to assess bias and evaluate the hypothesis directly; we found that strict factorial invariance was tenable in both samples and either the strong or the weak form of the hypothesis was supported, with 87 and 78% of the group differences attributable to g in the VES and NLSY ’79 respectively. Using item response theory metrics to avoid pass rate confounding, a strong relationship between g loadings and group differences (r = 0.80 and 0.79) was observed. Finally, assessing differential item functioning with item level data revealed that a handful of items functioned differently, but their removal did not affect gap sizes much beyond what would be expected from shortening tests, and assessing the effect this had on scores using an anchoring method, the differential functioning was found to be negligible in size. In aggregate, results supported Spearman's hypothesis but not test bias as an explanation for the cognitive differences between the groups we studied.
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