Evolution and God

GSS SCITEST4 asks

In your opinion, how true is this? … D. Human beings developed from earlier species of animals.

Frequency Distribution

HUMANS EVOLVED FROM ANIMALS

ROW
TOTAL

GOD

COL TOTAL

Cells contain:
-Column percent
-N of cases
1
DEFINITELY TRUE
2
PROBABLY TRUE
3
PROBABLY NOT TRUE
4
DEFINITELY NOT TRUE
1: DONT BELIEVE 9.3
51
2.3
26
2.0
12
1.0
12
2.9
101
2: NO WAY TO FIND OUT 9.5
52
5.6
63
2.9
17
.4
5
3.9
137
3: SOME HIGHER POWER 18.6
102
13.7
155
5.2
31
1.8
22
8.8
310
4: BELIEVE SOMETIMES 5.5
30
5.8
66
3.0
18
.9
11
3.5
125
5: BELIEVE BUT DOUBTS 19.5
107
21.8
246
22.2
132
5.7
71
15.8
556
6: KNOW GOD EXISTS 37.7
207
50.8
574
64.7
385
90.4
1,133
65.2
2,299
100.0
549
100.0
1,130
100.0
595
100.0
1,254
100.0
3,528

Color coding: <-2.0 <-1.0 <0.0 >0.0 >1.0 >2.0 Z
N in each cell: Smaller than expected Larger than expected

As you would expect, this also holds for BIBLE:

Frequency Distribution

SCITEST4

ROW
TOTAL

BIBLE

COL TOTAL

Cells contain:
-Column percent
-N of cases
1
DEFINITELY TRUE
2
PROBABLY TRUE
3
PROBABLY NOT TRUE
4
DEFINITELY NOT TRUE
1: WORD OF GOD 14.0
47
21.0
160
31.6
134
54.5
466
34.0
807
2: INSPIRED WORD 44.9
151
56.7
432
54.7
232
41.5
355
49.2
1,170
3: BOOK OF FABLES 39.6
133
21.7
165
12.5
53
3.3
28
15.9
379
4: OTHER 1.5
5
.7
5
1.2
5
.7
6
.9
21
100.0
336
100.0
762
100.0
424
100.0
855
100.0
2,377

The trend holds when you compare across groups by educational attainment. Here is a table of the average response to SCITEST4 (lower = better).

Main Statistics

DEGREE

ROW
TOTAL

GOD

COL TOTAL

Cells contain:
-Means
-N of cases
0
LT HIGH SCHOOL
1
HIGH SCHOOL
2
JUNIOR COLLEGE
3
BACHELOR
4
GRADUATE
1: DONT BELIEVE 2.71
14
1.87
47
2.33
3
1.32
25
1.75
12
1.85
101
2: NO WAY TO FIND OUT 2.36
14
1.93
55
1.25
4
1.69
36
1.56
27
1.82
136
3: SOME HIGHER POWER 2.12
33
2.13
146
1.88
17
1.62
81
1.41
32
1.91
309
4: BELIEVE SOMETIMES 2.37
19
2.17
66
2.14
7
1.94
18
1.47
15
2.08
125
5: BELIEVE BUT DOUBTS 2.52
62
2.40
292
2.56
36
2.06
108
1.88
58
2.30
556
6: KNOW GOD EXISTS 3.06
414
3.20
1,251
3.07
146
2.82
344
2.38
132
3.06
2,287
2.89
556
2.88
1,857
2.81
213
2.37
612
2.00
276
2.72
3,514
Color coding: <-2.0 <-1.0 <0.0 >0.0 >1.0 >2.0 T
Mean in each cell: Smaller than average Larger than average

God and British Scientists

From The God Delusion:

1) 1,074 Fellows of the Royal Society were emailed

2) 23 percent responded

3) They were asked various propositions, such as, “I believe in a personal God, that is one who takes an interest in individuals, hears and answers prayers, is concerned with sin and transgressions, and passes judgement.”

4) They were invited to choose a number from 1 to 7 indicating strong disagreemant to strong agreemant.

5) 3.3% agreed strongly with the statement that a personal god exists (chose 7), while 78.8% strongly disagreed (chose 1).

In the United States the National Academy of Sciences members exhited theism to the tune of 10%.

Related: God & the scientists, God & the evolutionists.

What do Y & mtDNA tell us?

RPM has a post up about Y and mtDNA lineages, and what they can (or can’t) tell us about demographic history. I’m pretty skeptical myself about the broad and detailed deep time inferences some make with these markers (see The Real Eve for an extreme case), but Dienekes points me to a situation where there is some utility to this methodology:

The differential relative contribution of males and females from Africa and Europe to individual African American genomes is relevant to mapping genes utilizing admixture analysis…The European genetic contributions were highest (and African lowest) for the Y chromosome (28.46%), followed by the autosomes (19.99%), then the X chromosome (12.11%), and the mtDNA (8.51%). The relative order of admixture fractions in the genomic compartments validates previous studies that suggested sex-biased gene flow with elevated European male and African female contributions. There is a threefold higher European male contribution compared with European females (Y chromosome vs. mtDNA) to the genomes of African American individuals meaning that admixture-based gene discovery will have the most power for the autosomes and will be more limited for X chromosome analysis.

Similarly, Latin America shows strong signatures of asymmetrical gene flow in relation to the sexes. The key I think is that genetic data is a supplement to what we already know, and refines and confirms our hypotheses, the problem tends to be when genetic data is the sole leg upon which to stand, because as RPM notes assumptions of neutrality might not hold across the time spans and geographical distributions that we wish to survey.

Culture naturally

Back in August AlphaPsy had series of posts on ‘naturalism’ in the context of culture. Check them out! (links below) I strongly believe it is important to discuss human affairs with a multi-disciplinary lens, too often the public discourse is presupposed on naive psychology, while elite models tend to fixate on one dimension (e.g., the ‘rational actor,’ a pet historical paradigm, etc.).

Read More

Fisher and population size

One of the major dialogues in evolutionary genetics in the 20th century was that between R.A. Fisher and Sewall Wright. It is so seminal that the term Fisher-Wright controversy is often used. One of the major points of disagreemant between Fisher and Wright was the role of population substructure and the relevance of long term effective population size in shaping the trajectory of allele frequencies over time. At my other blog David B is starting a series which addresses this issue. The initial post deals with the period before the publication of The Genetical Theory of Natural Selection.

The rise of welfare?

Over the past few months I’ve read Winning the Race by John McWhorter and The Burden of Bad Ideas by Heather Mac Donald. One thing that both books assert is that in the late 1960s and early 1970s there was a proactive campaign by the National Welfare Rights Organization to get as many people on the rolls in places like New York City as possible. McWhorter also notes that there seems to have been a special emphasis on recruiting black Americans to heighten and exacerbate the racialized dimension of the problem. But getting poor people on welfare was only a means to an ends, and that ends was bankrupting the government and overturning the established social order. In other words, to overthrow the Great Society welfare state (and presumably replace with something more politically revolutionary). But in any case, the only reason I bring this up it that as I was reading this I was struck by an analogy to the Starve-the-beast philosophy promoted by Grover Norquist, except in the opposite direction. The key goal in both cases is to “break it” so that you can build it anew….

Skin color and IQ in the GSS

A question from Jason Malloy prompted a quick search of the GSS for data on the cause of the Black-White IQ gap. In 1982, the GSS characterized the skin color of Black participants on a 5-point scale (1:very dark brown to 5:very light brown). The very dark/light categories consist of only 50 and 14 individuals, respectively, and so in the following analysis I merged them with the dark/light brown categories, to give three COLOR levels: dark, medium, and light. In the web application, use COLOR(r:1-2;3;4-5) instead of COLOR. The WORDSUM variable is a 10 question vocabulary test, which I’m treating as a proxy for IQ. It is correlated with educational attainment (~.4), and also correlates (~.4-.5) with tests of reasoning and basic knowledge that were given in some years. These other tests are not available for 1982. In the all-subject all-year GSS data set, WORDSUM varies by SEX, and in 1982 COLOR also varies by SEX. Thus, SEX is controlled for in each analysis. WORDSUM is lower in the youngest and oldest age groups, so an AGE(25-65) filter was used.

Table 1. Mean WORDSUM score by COLOR and SEX with ANOVA

Main Statistics

SEX

ROW
TOTAL

COLOR

COL TOTAL

Cells contain:
-Means
-Std Devs
-N of cases
1
MALE
2
FEMALE
1: Dark 4.15
1.964
56
4.52
1.709
56
4.35
1.833
112
2: Medium 5.39
2.314
61
5.14
2.164
96
5.23
2.218
157
3: Light 6.04
1.860
17
5.58
2.254
41
5.70
2.153
58
4.97
2.224
134
5.06
2.088
193
5.02
2.139
327

color indicates T-statistic, and thus p-value

Color coding: <-2.0 <-1.0 <0.0 >0.0 >1.0 >2.0 T
Mean in each cell: Smaller than average Larger than average
Analysis of Variance
SSQ Eta_sq df MSQ F P
Main effects 89.443 .061 3 29.814 6.956 .0002
COLOR 89.099 .061 2 44.549 10.394 .0000
SEX .691 .000 1 .691 .161 .6884
Interaction 2.569 .002 2 1.285 .300 .7412
Residual 1,375.884 .937 321 4.286
Total 1,467.896 1.000 326

We can quantify the effect size of each skin color class using Cohen’s d statistic, which measures the mean difference in standard deviation units. In the 1982 dataset, the overall d for the Black-White gap on WORDSUM is -0.63 (among males d=-0.51, among d=-0.74). For comparison, the 1982 male-female gap among Whites is d=-.12, favoring females.

Table 2. Effect size (d) of COLOR on WORDSUM using “light” as a control group

Color Male Female Total
Dark -0.99 -0.53 -0.68
Medium -0.31 -0.20 -0.22
Light 0.00 0.00 0.00

We can also use Whites as the control group.

Table 3. Effect size (d) of COLOR on WORDSUM using Whites as a control group

Color Male Female Total
Dark -0.99 -1.10 -1.04
Medium -0.35 -0.69 -0.54
Light -0.07 -0.46 -0.33

Thus, there are substantial (moderate to large effect size) differences in WORDSUM scores between the darkest and lightest Blacks in 1982.

As reported by Rushton and Jensen (2005), Shuey (1966) reviewed 18 studies which used skin color as a measure of racial admixture to compare with IQ. Of those 18, 16 found a significant effect of the kind found here, but the overall correlation with IQ was low (r=.1). In this data, the COLOR WORDSUM correlation is r=.31 among males and r=.18 among females, with an overall correlation of r=.23. Off the top of my head, I’m not certain what the expected correlation would be between IQ and skin color among Blacks for a given measure of “between-group heritability” (BGH) as described by Jensen (1998). I’ll leave it as an exercise for our mathematically skilled commentators to derive a formula for this relationship and to evaluate the signficance of this finding in explaining the cause of the Black-White IQ gap.

Tall, to short, to tall (again)

height.jpgDienekes reports on a paper which chronicles the change height of “Europeans” over the last 20,000 years ago. Anthropologist Henry Harpending once told me that when the first modern humans arrived in European 40-30 thousand years ago they were as slim and towering as modern Nilotic peoples, in other words, they were evolutionary reflections of the African environment. But soon enough the nouveau Europeans shape shifted and developed a more robust physiognomy, with a reduction in median height. As you can see from the graph which I generated the Neolithic Revolution and the introduction of agriculture was the nadir of physical size, and undernourished reality of the farming cultures of Eurasia was a fact of life until the past century. But, note that even today Europeans are not as domineering in stature as they were 20,000 years ago. Humans have a tendency to view evolution as a progressive force, toward more complexity, size and intelligence. But we aren’t sure that this is correct, not only were modern humans larger during the Ice Age, but the largest cranial capacities of any human population can be found among the Neandertals.