Ren Feng – Sociology course material at Univ. Xiamen- expanded by Douglas R. White
Presenters Eric Baum, Stuart Martin, Argonne Labs, R code Anthon Eff, Paul Rodriguez
how to analyze new variables in CoSSciB
Galaxy (XSEDE): student guide
Complex Social Science and BioGeography
computing variables, making interaction items,
merging items within a categorical variable to make a
dummy (dichotomous) variable.
With these, students can detect more complicated
relationships among independent variables.
In development: Bayesian Causal Networks
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Dependent variable v2018: Private Property & Punishment for Theft
Layout of one of 50 sociology student projects at Xiamen University 2014
using CoSSci Gateway UC Irvine VM and UCSD Comet HPC, dataset SCCS
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Entering Dependent,
Independent and other
variables in CoSSci Galaxy
DpV V2018 Property: Importance of Private Ownership and Severity of Punishment for Theft
v17： Money (Media of Exchange) and Credit
v155：Scale 7- Money
v234：Settlement Patterns
v235：Mean Size of Local Communities
v710: Social Stratification in the Local Community
v727：Importance of Agriculture in Subsistence, including Gardening
v773: (No) Internal Warfare (between Communities of Same Society)
v95: Political Power- Third Most Important Source
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Entering Dependent,
Independent and other
variables in CoSSci Galaxy
Images here and below use Screen Shot then Insert then Photo then Picture from file
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Click diskette
lower left for
CSV results ->
Then: Find “Rmodel” in your *.csv file: prepare
to delete nonpredictive v234, v727, and check
“To Try” in .csv that informs you can test v95 to
add to your model if significant
Your new model has v155,v17,v235,v710 ,v773,
but not v234,v727,v95. Now change your model.
Then press
and
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Your new model, when executed, happens
to show that all these variables are
significant but two of them are opposite
measures of money (curvilinear). Your
instructor can run a crosstab with R:
table(dx$v17,dx$v155). Now you’ll learn
two tricks. If you make v155.d5 a dummy
variable you’ll get a dichotomy 1234/5.
For v17 however, you want to contrast
123/45 as a New Variable not a dummy.
View the codebook for v17 versus v155 at
eclectic.ss.uci.edu/~drwhite/courses/SCCCo
des.htm to see why this makes sense.
These are two important operations to
learn. But we are no closer to
understanding causes of private property in
the Standard Sample, probably because
only 79 cases are coded. Here is what I
mean: there may be a contradiction in the
results:
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To see the problem with this topic, D.R.White states the hypothesis that these data
represent colonialism rather than evolution of private property: where the currency is
completely foreign (i.e., code 4: colonial), private property is less likely, and we may be
looking at smaller colonized communities in the Standard Cross-Cultural Sample.
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In this model we’re testing whether v17.d4 (dichotomized at value 4
versus 1235) “Foreign Currency”: this tends to remove local private
property along with (negative v235) plus a tendency of this removal
to affect smaller communities (i.e., effects of colonialism). But bear
in mind that with 79 of 186 cases coded it’s a small subsample.
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how to define new variables in
Galaxy CoSSci
computing variables
The computed variables tried above were
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making interaction items
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merging items within a categorical
variable to make a dummy variable
v17.d4 dichotomy as in
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We may have figured out, then, the curvilineality as between v155.d5 and v17ge4
in the original model: one being the evolution of property, the other the colonial
suppression of indigenous property given foreign currency. This might make sense
given that the Hupka and Ryan coders of v2018 focused on The Cultural
Contribution To Jealousy (1990) Cross-Cultural Research 24(1-4): 51-71.
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Private Property & Punishment for Theft v2018
in the context of coding for The Cultural
Contribution To Jealousy (N=78 of 186 societies)
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Recap of how the final model was defined in CoSSci by the student (omitting
potential “To Try” variable in the *.csv output). Clicking the square, round (i) and
green buttons at the bottom of output 10 DEf01f general saved *.csv output,
diagnostics for correcting errors in the model, and return to the panel of
variables. Clicking the orange tab in the upper right light blue box saves the
steps in the output as online sharable/publishable model histories. The main
page of CoSSci Gateway has a 2-minute youtube explanation of the Gateway and
a 20 minute youtube on the Complex Social Science Gateway by Lukasz Lacinski.
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An upcoming CoSSci development
CoSSci Complex Social & Science users will soon be able to start with Bayesian
presuppositions or inferences about possible predictors of a dependent variable
from one of many cross-cultural datasets on hunter-gatherers or other worldwide or
regional datasets. Iterative improvements to initial models benefit from selecting
“to try” variables that are possibly predictive. Diagnostics with group significance
tests alert the user to opportunities in testing for Bayesian causality, which often
occur after a dozen or more improvements of the model. Of models tested, about
65% result in models that pass these tests. For each of these, using HPC iterations,
with imputation of missing data, subsets of variables will be tested for networks of
variables identified by library(bnlearn) to show Bayesian causalities. These build on
steps in developing the CoSSci Gateway for analysis of ethno-archaeological,
historical empires, and world bioecological data, reviewed in our talk – wirelessly
open to all for free – by Paul Rodriguez, Eric Blau, Lukasz Lacinski, Stu Martin,
Rachana Ananthakrishnan, Tom Uram, Tolga Oztan, Doug White – Sept 30-Oct 1
2015 for the Complex Systems Digital Campus (CS-DC) sponsored by UNESCO and its
Complex Systems Society (CSS) as an ECCS component (European Conferences on
Complex Systems: http://www.ccs2015.org) and as part of the Arizona State Tempe
Conference on Complex Systems, co-hosted by the Santa Fe Institute.
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