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Abstract

Decety et al.[1] examined the relationships between household religiosity and sociality in children sampled from six countries. We were keenly interested in Decety et al.[1]'s conclusions about a negative relationship between religiosity and generosity - measured with the Dictator Game - as our team has investigated related questions, often with potentially contrasting findings [2-5]. We argue here that, after addressing peculiarities in their analyses, Decety et al.[1]'s data are consistent with a different interpretation.
Current Biology
Magazine
Current Biology 26, R689–R700, August 8, 2016 © 2016 Elsevier Ltd. R699
Archaeological Trust Curatorial Department
for providing access to the samples from
Coppergate, England, and Camilla Speller for
sampling bones. This project was supported
by the Deutsche Forschungsgemeinschaft (LU
852/7-4).
REFERENCES
1. Andersson, L.S., Larhammar, M., Memic, F.,
Wootz, H., Schwochow, D., Rubin, C.-J.,
Patra, K., Arnason, T., Wellbring, L., Hjälm, G.,
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in mice. Nature 488, 642–646.
2. Promerová, M., Andersson, L.S., Juras, R.,
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Viking-Arab trade exchange. Saber and Scroll 4,
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8. Brink, S. and Price, N. (2008). The Viking World
(Routledge).
1Department of Evolutionary Genetics, Leibniz
Institute for Zoo and Wildlife Research, Alfred-
Kowalke-Str. 17, 10315 Berlin, Germany.
2Department of Medical Biochemistry and
Microbiology, Uppsala University, 75123
Uppsala, Sweden. 3Department of Animal
Breeding and Genetics, Swedish University of
Agricultural Sciences, 75007 Uppsala, Sweden.
4German Archaeological Institute, Department of
Natural Sciences, Berlin, 14195 Berlin, Germany.
5Centro de Ciencias de la Complejidad,
Universidad Nacional Autónoma de México,
Ciudad de México, Mexico. 6University of
Potsdam, Faculty of Mathematics and Natural
Sciences, Institute for Biochemistry and Biology,
Karl-Liebknecht-Str. 24-25, 14476 Potsdam,
Germany. 7The Agricultural University of Iceland,
Faculty of Land and Animal Resources, IS-112
Reykjavik, Iceland. 8Archaeological Research
Collection, Tallinn University, Rüütli 10, 10130
Tallinn, Estonia. 9National Historical Museums,
Contract Archaeology, 226 60 Lund, Sweden.
10Universidad Autonoma de Madrid, Laboratory
of Archaeozoology, Madrid, Spain. 11Centre
for GeoGenetics, Natural History Museum of
Denmark, University of Copenhagen, 1350K
Copenhagen, Denmark. 12Humboldt University
Berlin, Faculty of Life Sciences, Albrecht
Daniel Thaer-Institute, 10115 Berlin, Germany.
13Department of Archaeology, University of York,
York, YO10 5DD, United Kingdom. 14Slovak
Academy of Sciences, Institute of Archaeology,
949 21 Nitra, Slovak Republic.
*E-mail: michi@palaeo.eu (M.H.),
ludwig@izw-berlin.de (A.L.)
What is the
association
between religious
affi liation and
children’s altruism?
Azim F. Shariff1,6, Aiyana K. Willard2,
Michael Muthukrishna3,
Stephanie R. Kramer4,
and Joseph Henrich5
Decety et al. [1] examined the
relationships between household
religiosity and sociality in children
sampled from six countries. We were
keenly interested in Decety et al.
[1]’s conclusions about a negative
relationship between religiosity
and generosity — measured with
the Dictator Game — as our team
has investigated related questions,
often with potentially contrasting
ndings [2–5]. We argue here that,
after addressing peculiarities in
their analyses, Decety et al. [1]’s
data are consistent with a different
interpretation.
Given that previous studies (for
example [6–8]) have shown cross-
national variation in Dictator Game
behavior, Decety et al. [1]’s approach
of aiming to include country-level
xed effects in their analysis, to
account for mean differences among
countries, is sensible. But when they
included their categorically-coded
country (1 = US, 2 = Canada, and so
on) in their models, it was entered not
as fi xed effects, with dummy variables
for all of the countries except one, but
as a continuous measure. This treats
the variable as a measure of ‘country-
ness’ (for example, Canada is twice
as much a country as the US) instead
of providing the fi xed effects they
explicitly intended. We have repeated
Decety et al. [1]’s intended analysis by
using actual fi xed effects, along with
their model specifi cations, and then
explored other plausible specifi cations
and modelling approaches. Our
analyses reveal meaningfully different
results from those originally reported.
Decety et al. [1] report that children
from religious — especially Muslim —
households recommend more
punishment of a moral transgressor
than do children from non-religious
households. Using the same model
specifi cation as Decety et al. [1], but
including dummy-codes for country
(with USA as the referent), we fi nd
little support for this; no effect of
household religious affi liation emerged
(
β
= –0.03, t(774) = –0.31, p = 0.75).
Because Decety et al. [1]’s ordinary
least squares (OLS) regression
analysis is not ideal for the highly
negatively-skewed distribution of
punishment ratings, we also estimated
a model using the log of the reverse-
scored punishment values; this
similarly yielded no effect (
β
= 0.00,
t(774) = 0.14, p = 0.89).
Conducting Decety et al. [1]’s
intended analysis also fi nds no
support for their conclusion that
more religious parents report their
children having more empathy and
sensitivity to injustices. When country
is entered as fi xed, Decety et al.
[1]’s model specifi cation reveals no
relationship between religiosity and
either empathy (
β
= 0.04, t(764) = 1.15,
p = 0.25) or justice ratings (
β
= –0.03,
t(767) = –0.57, p = 0.57; Table S1 in
the Supplemental Information).
Decety et al. [1]’s primary claims
concern children’s altruistic behavior
in the Dictator Game. Here again, our
reanalysis using Decety et al. [1]’s
intended specifi cations calls their
conclusions into question. The fi xed
effects model shows no signifi cant
effect for religious affi liation on
generosity (OLS Model 2: p = 0.70;
Table 1), though we do observe effects
for age, country and (marginally)
socio-economic status. However,
Decety et al. [1]’s OLS model is poorly
suited for the many zero offers in the
data. To address this, we used a zero-
infl ated negative binomial regression,
but still, no relationship with religious
affi liation emerged. Indeed, within no
single country was household religious
affi liation a signifi cant predictor of
generosity (though sample sizes, and
thus statistical power, are reduced;
Table S2). Finally, given the overlap
between country and religious
affi liation, we also estimated a random
effects model, which yields similar
results (Table 1).
Though generosity appears
unrelated to household religious
Correspondence
Current Biology
Magazine
R700 Current Biology 26, R689–R700, August 8, 2016
9. Nakagawa, S., and Schielzeth, H. (2013). A
general and simple method for obtaining R2
from generalized linear mixed-effects models.
Meth. Eco. Evo. 4, 133–142.
10. Johnson, P.C.D. (2014). Extension of
Nakagawa & Schielzeth’s R2GLMM to random
slopes models. Meth. Eco. Evo. 5, 944–946.
1Department of Psychology and Social
Behavior, University of California, Irvine,
Irvine, CA 92697, USA. 2Department of
Psychology, University of Texas at Austin,
Austin, TX 78712, USA. 3Department
of Social Psychology, London School
of Economics, London WC2A 3LJ, UK.
4Department of Psychology, University
of Oregon, Eugene, OR 97401, USA.
5Department of Human Evolutionary Biology,
Harvard University, Cambridge, MA 02138,
USA.
E-mail: shariff@uoregon.edu
affi liation, Decety et al. [1]’s dataset
does reveal generosity to be
negatively related to both household
religious frequency (OLS:
β
= –0.26,
t(789) = –2.38, p = 0.02; zero-infl ated:
β
= –0.07, z = –2.13, p = 0.03), and
intrinsic religiosity (OLS:
β
= –0.19,
t(792) = –1.81, p = 0.07; zero-infl ated:
β
= –0.06, z = –2.05, p = 0.04; country-
by-country breakdown in Table S2).
However, the effect is quite small: an
increase in religiosity of 1 SD resulted
in 6–7% lower odds of sharing
stickers (roughly 0.2 fewer stickers);
see also Table S2.
In sum, Decety et al. [1] have
amassed a large and valuable
dataset, but our reanalyses provide
different interpretations of the
authors’ initial conclusions. Most
of the associations they observed
with religious affi liation appear to
be artifacts of between-country
differences, driven primarily by low
levels of generosity in Turkey and
South Africa. However, children from
highly religious households do appear
slightly less generous than those from
moderately religious ones.
SUPPLEMENTAL INFORMATION
Supplemental Information includes two ta-
bles and R code and can be found at http://
dx.doi.org/10.1016/j.cub.2016.06.031.
ACKNOWLEDGMENTS
We thank Jean Decety for graciously sharing
the original dataset.
REFERENCES
1. Decety, J., Cowell, J. M., Lee, K.,
Mahasneh, R., Malcolm-Smith, S., Selcuk, B.,
and Zhou, X. (2015). The negative association
between religiousness and children’s altruism
across the world. Curr. Biol. 25, 2951–2955.
2. Norenzayan, A., Shariff, A.F., Gervais, W.M.,
Willard, A.K., McNamara, R., Slingerland, E.
and Henrich, J. (2016). The cultural evolution of
prosocial religions. Behav. Brain Sci. 39, 1–65.
3. Purzycki, B.G., Apicella, C., Atkinson, Q.,
Cohen, E., McNamara, R.A., Willard, A. K.,
Xygalatas, D. Norenzayan, A., and Henrich, J.
(2016). Moralizing gods, supernatural
punishment, and the expansion of human
sociality. Nature 530, 327–330.
4. Shariff, A.F., and Norenzayan, A. (2011). Mean
gods make good people. Int. J. Psychol. Relig.
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5. Shariff, A.F., Willard, A.K., Andersen, T., and
Norenzayan, A. (2016). Religious priming a
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Table 1. Linear regression models.
OLS Model 1 OLS Model 2
Random effects
Model 3
Zero-infl ated
Negative Binomial
Model 4
Zero-infl ated
Negative Binomial
Model 5
(SE)
(SE)
(SE)
(SE)
(SE)
Religious (vs non) –0.50 (0.17)** –0.08 (0.21) –0.13 (0.21) –0.10 (0.04)* 0.00 (0.06)
Age 0.44 (0.03)*** 0.42 (0.03)*** 0.42 (0.03)*** 0.08 (0.01)*** 0.09 (0.01)***
Female 0.21 (0.15) –0.18 (0.14) –0.17 (0.14) –0.06 (0.04) –0.07 (0.04)
SES 0.21 (0.06)*** 0.11 (0.07) 0.12 (0.07) 0.04 (0.02)* 0.03 (0.02)
Country (vs USA)
Canada 0.29 (0.26) 0.05 (0.08)
South Africa –1.46 (0.26)*** –0.31 (0.08)***
Turkey –0.73 (0.24)** –0.24 (0.07)***
China –0.04 (0.34) 0.00 (0.08)
Jordan 0.07 (0.27) –0.08 (0.07)
R2 0.18** 0.25*** 0.23***
Models 1 and 4 show regression results without controlling for country of origin. Models 2 and 5 control for country. Model 3 includes random
intercepts for each country. The R2 reported for Model 3 includes variance explained by both fi xed and random factors [9,10]. p < 0.10, *p < 0.05,
**p < 0.01, ***p < 0.001.
The editors of Current Biology
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Correspondence containing data
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... The study was not retracted due to the measures used but because of inaccurate data analysis. Since Decety et al. (2015) reported the measures of religiousness and prosociality and Shariff et al. (2016) reported the reanalysis results, it was decided that this study will be referred to as Decety et al. (2015)/Shariff et al. (2016. This systematic review reports the measures used in Decety et al.'s study but the results of the association as reanalyzed by Shariff et al. ...
... The study was not retracted due to the measures used but because of inaccurate data analysis. Since Decety et al. (2015) reported the measures of religiousness and prosociality and Shariff et al. (2016) reported the reanalysis results, it was decided that this study will be referred to as Decety et al. (2015)/Shariff et al. (2016. This systematic review reports the measures used in Decety et al.'s study but the results of the association as reanalyzed by Shariff et al. ...
... Note: The shaded cells indicate the use of the dimension. Shariff et al., 2016;Kingston & Medlin, 2005). In one study, religious attendance consisted of a spiritual, educational program where attendance was reported by the spiritual trainer (Pandya, 2017). ...
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For centuries, domestic horses have represented an important means of transport and served as working and companion animals. Although their role in transportation is less important today, many horse breeds are still subject to intense selection based on their pattern of locomotion. A striking example of such a selected trait is the ability of a horse to perform additional gaits other than the common walk, trot and gallop. Those could be four-beat ambling gaits, which are particularly smooth and comfortable for the rider, or pace, used mainly in racing. Gaited horse breeds occur around the globe, suggesting that gaitedness is an old trait, selected for in many breeds. A recent study discovered that a nonsense mutation in DMRT3 has a major impact on gaitedness in horses and is present at a high frequency in gaited breeds and in horses bred for harness racing. Here, we report a study of the worldwide distribution of this mutation. We genotyped 4396 horses representing 141 horse breeds for the DMRT3 stop mutation. More than half (2749) of these horses also were genotyped for a SNP situated 32 kb upstream of the DMRT3 nonsense mutation because these two SNPs are in very strong linkage disequilibrium. We show that the DMRT3 mutation is present in 68 of the 141 genotyped horse breeds at a frequency ranging from 1% to 100%. We also show that the mutation is not limited to a geographical area, but is found worldwide. The breeds with a high frequency of the stop mutation (>50%) are either classified as gaited or bred for harness racing.
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The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models. The presentation of variance explained' (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest. One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation). Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems. This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.