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Comparative Social Science: A Very Short Introduction
Cross-Cultural Research is the vehicle whereby Comparative
Social Science interrogates the texts of historians, economists
and ethnographers through comparable categories. These are
coded into variables that can be use for testing models of how
evolutionary processes transformed human societies. Such
codes may also be used in testing hypotheses about functional
relationships among variables and to trace both the diffusion
of peoples and the diffusion of variables across societies, i.e.,
the processes of human histories and evolution.
These problems are not so easy to decipher. Postmodernists do
not believe in “comparable categories” but scientists do and
define measures accordingly. Humans employ categories that
may be subjective and need to be decodes in context: contexts
of observable behavior, conversation, signs and symbols, … and
assign these to measures that may or may not be comparable.
Not all categories are equally viable: some may be highly
inferential, others reliable in the contexts of behavior or
culture as a places with sets of ideas of a high degree of
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commonality, agreement and interlocked functional activity,
like that of getting recurrent jobs done in contexts of
interaction. This is why ethnographers tend to stay within or
compare cultural contexts for long periods of time, benefits
from histories of these contexts, and why historians or
historical economists tend to gather a great deal of evidence
and observations before reaching conclusions.
Efforts to assemble samples of well described (and often
repeatedly studied) contexts for the study of cultures that
could be studied and coded for a large number of contexts and
variables did not take place easily in cross-cultural research.
From the 1880 to the 1960s comparativists tended to select
their own samples of societies or contexts to study on the
assumption that many such studies would cohere into shared
disciplinary knowledge. Having collected in my tenure as
graduate student all such studies that had been converted to
the punched-cards that era, I can testify empirically that
convergence did not tend to occur. Testable hypotheses did
coalesce and could be tested for specific disciplines and these
could be tested with correlations and significance tests but
anthropology had another problem: these statistics simply did
not work when historically similar local societies closely
abutted one another with constellations of similar or dissimilar
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spread abutted one another in much more complicated ways
than in random samples of persons in a large population.
Cultures were much more deeply entwined or separated,
depending on their larger histories, those of mass migration,
colonialism, fundamentally different types of economy or
transport.
The construction of samples that were relatively complete, like
extant hunter-gatherers studied by ethnographers (Binford
2001) and for the full compass of variables (some 506
variables in this case for 399 societies, plus a great many
ecological, animal species and climate variables coded) began
only in the 1960s. Jorgenson (1985) did the same for Western
American Indians (172 societies, 496 societies). Murdock
(1967) coded only 167 variables and 1257 societies but
Murdock and White (1969) began a different approach, where
186 maximally different earliest best-described societies in
their cultural milieu were precisely pinpointed in time and
space with applicable published ethnographic texts with the
idea that cross-culturalists could develop and contribute their
own codes on different topics and biogeographers could
contribute their data for the specific eco-contexts of each
society and its surrounding regions. Their database, the SCCS
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or Standard Cross-Cultural Sample, now hosts 2109 variables
contributed by over 100 cross-cultural and other researchers.
Some contributors to the open access code-appending SCCS
powerhouse for cross-cultural research contributed only a
25% random sample of coded cases, others 50% or 78%, etc.
New forms of analysis, however, compensated for missing data
by taking the set of societies coded for the dependent variable
as the focus of study, and for those societies imputing missing
data for all the other variables that the investigator wishes to
include among the possible hypothesized independent
variables that might be of interest. At this level of organization
it might seem that cross-cultural research could establish a
scientific foundation. Statistician Sir Francis Galton, however,
had noted a major flaw in the very first presentation of a crosscultural study in 1888-89 that had never been repaired. This
was the fact that societies were intertwined in ways that are
obscured by cultural borrowing, expansion of populations from
the same language groups, and shared environments. This
might also be true for ordinary survey questionnaires and
medical research on populations of patients.
The first full exposure to the problem was concerned with how
to extract from every set of dependent and independent
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variables common or independent evolutionary histories and
processes among societies as distinct from correlations among
variables that are often completely misleading. Correlation is
not causation. Eff and Dow (2009) were concerned with how
multiple evolutionary sources could be revealed to bring to
fruition Galton’s problem. They thought of the matrix of
weighted distances between societies as a measure of the
potential routes of diffusion, and the network matrix of
linguistic trees as a weighted measure of the potential routes of
cultural heritage. Indeed, these W matrices and their sums
with weighted squares W+W2 when normalized to row sums of
one might be put to good use in modeling evolutionary effects.
Part of their innovation was to consider multiplying every term
in a regression equation, both the dependent and independent
variables by the sum of these weights and then taking the
calculated value of the Wy term in this estimate as the total
measure of evolutionary effects. Then adding this Wy term to
the original regression equation can tested whether the
independent variables are truly independent (“exogenous”)
from the error term, in which case the separation has been
made between evolutionary effects and truly independent
effects on the dependent variable.
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First of all, however, Eff and Dow impute missing data for the
reason that the categories used in cross-cultural studies cannot
always be coded. Imputation proves to be important in
examining causal effects within networks of variables.
Eff and Dow’s example examined independent variables that
were predictive of each society’s evaluation of the value of
children, but this code was rather vague, and independent
variables accounted for very low predictions, with Rsq=0.10.
When focused on society’s evaluation of the value of girls, the
Rsq=0.16 as shown in Table 1 is produced by women’s work
and residence with wife’s kin in the first years of marriage.
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Table 1: Women’s work and kin as predictors of Value of Girls:
Subsistence Contribution, Fishing, Cultivation with Rain
The focus of Eff and Dow’s initial model was
Imputed datasets
The
XC
Bnlearn
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