Indep/Depvar list - All

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Causality.png Project: The Anthropology of Causality -- Cultural Consequences of Regionally Fluctuating Inequality -- Edu-Mod 2009-10: The Individual Studies

svg Causal graphs network Mar_15_2010

  1. pvar < .06. Showing Language and Distance effects for the dependent variables. The sizes of nodes show the extent of spatial clustering. Not shown are the negative versus positive or nonsignificant linguistic clustering. The colors show the layers of the directed asymetric graph (DAG).
  2. pvar < .06. Showing Language and Distance effects 2 for the dependent variables. Here the dependent variables begin at the two bottom rows, coded yellow for significant negative linguistic effects, green otherwise, and sizes of those nodes (which were dependent variables) showing relative significance of spatial clustering. This graph also minimizes the length of lines between levels. 10 dependent variables are at the lowest levels, 2 at the next level up, 1 at the third level, and only 3 independent variables at the top level. All features of the analysis are then shown in the causal graphs, except the regression coefficients as labels for the lines.

Each downward line in the graphs has a signed regression coefficient[1] from two-stage OLS using the software of Eff, E. Anthon, and Malcolm Dow. 2009. How to Deal with Missing Data and Galton's Problem in Cross-Cultural Survey Research : A Primer for R, in Structure and Dynamics: eJournal of Anthropological and Related Sciences 3#3 art 1. Spatial and linguistic clustering effects are estimated in stage one and used to make a new estimated Galton-effects variable that is included in stage two along with other independent variables for which the causal effects regression coefficients are estimated. The statistical diagnostics test whether the error terms are now free of language and distance autocorrelation, heteroscedasticity (bunching of error terms), variables with significant effects that are not specified in the final model but which reside in the set of initial variables, or whether there are variables among those specified that would be more predictive when logged.

  1. The unstandardized regression coefficients for each of the arrows are shown only for the triangular graph #6 to show an example of calculation of both direct and indirect effects according to the causal graphs methods of Judea Pearl. The magnitude of causal effects of the independent variables for each dependent variable can eventually be calculated when the fuller structure of these graphs, taking each independent variable as a dependent one, is estimated.

The independent variables with the most dependent variables effects are these:

Moral gods (4 effects), 3 strongly significant
Population density (3 effects), 2 strongly significant
Food trade (3 effects), 2 strongly significant

svg Causal graphs network Mar_9_2010 (updated)

http://bit.ly/98IC5J Causal graphs from cross-cultural research - subhead

  • Viewable only in Mozilla firefox, these four causal graphs have black solid lines for positive regression coefficients between independent vars and dependent vars (downward lines), and red dotted lines for negative coefficients.
  • The thickness of the lines reflects significance, starting at pvar <.06 (thin lines) up to pvar <.0003 (thick lines).
3. pvar < .06 - you can click the square boxes for URLs that provide background information. The four levels, which form a directed acyclic graph (DAG), represent, first, variables that appear only as predictors, then ones that are both dependent (from above) and independent (to the level below) in successive levels. Colors and sizes of nodes are those of graphs 1 and 2 above.
4. pvar < .06 bicomponent in 46: one transitive subgraph.
5. pvar < .06 bicomponent layers with one transitive subgraph - can you see it?
6. pvar < .06 showing how to compute causal effects that are both direct and indirect. This example shows how Causal relations are not functionally consistent, as in a graph where signs are balanced (positive products of signs in a closed circuit).

My perception is that everything in graphs 1-5 has a structure consistent with a DAG structure that is computable for causal graphs (for each dependent variable) according to Pearl. Is this correct? The regression coefficients are computed in the second stage of the 2SLS, with the endogenous variables (spatial and language clustering coefficients) estimated in the first stage for use as predictors in the second stage.

svg network 3_3_10 all obsolete

SCCS v17: Money - Spatially clustered, along trade routes, in dependencies and independent regions

Maps

Map requests

If your variable is not in the Query list then add to one of the three lists below ACCORDING TO YOUR EDUMOD number:

SCCS v591: Ownership of dwellings - No spatial clusters
 v17  EM-10dv money
 v26  EM-23 bodycontact (bodily contact-early infancy)
 v33  EM M-6 pain infliction
 v54  EM M-6 father (role of)
 v72  EM-26iv exogamy (intercommunity marriage)
v80 lrgfam - not yet mapped
v143 - mapped but not used (laungering div of labor)
 v153 EM-18iv techspec (technological specialization) (corpun was an incorrect label)
v453 EM-18iv corpun - you specified 153 by mistake. Thus, is not on the mapping site.

BIG CORRECTION Doug 13:53, 30 November 2009 (PST) to set the record straight: When you switched to depvarname<="wifebeating", Hiu Kwan. which is v453 you asked for v153 by mistake. To set the record straight since v153 was TECHNOLOGICAL SPECIALIZATION I made two changes: one to let polispec be v153 and then to let wifebeating be v453. Then I reran your results.

 v156 EM-27iv etc popdens
 v157 POLITICAL INTEGRATION (not used by anyone)
 v167 EM-24dv pre_mar_sex
 v169 EM-20dv
 v169 EM-28dv extramaritalsex
 v203 EM-27
 v203 EM-24iv etc gath
 v204 EM-18iv etc hunt
 v205 EM-24iv etc fish
 v208 EM-27iv brideprice (hi values: dowry)
 v227 EM-24iv exogamy
 v232 EM-24
 v232 EM-26iv etc cultints (intensive cultivation)
 v233 EM-18
 v233 EM-24
 v233=6 EM-26 etc cereals
 v234 EM-20iv etc settype (settlement type)
 v236 EM-26iv etc localjh
 v237 EM-20iv etc superjh
 v238 EM-21iv etc moralgods
 v239 EM-17dv strategy (games)
v242 agrlateboy - not yet mapped
 v243>1 EM-24iv etc plow
 v244 EM-26iv
 v244==2 EM-27iv etc pigs
 v244==4 EM-29iv etc bovines
 v245>1 EM-24iv milk
 v300 EM-6 
 v300 EM-26iv segadlboys
 v591 EM-29dv female control over dwelling
v661 EM-11iv 
v661 EM-13iv 
v661 EM-41iv fempolpar
v662 EM-13iv femsolgro - Sanday)
v662 EM-41iv,femalesol - Sanday=femsolgro)
 v663 EM-26 fempower
 v664 EM-40 maletough ideomaletough
 v666 EM-28
v666 violence  intervio
 v667 EM-11 rape
0v677 migr (no predictions)
 v678 EM-13dv foodstress (food stress or hunger)
 v678 EM-21dv stress (Food Stress or Hunger - Sanday)
v679 warfight (Warfare or Fighting - Sanday)
v693 War&Fighting (Frequency of Intercommunity Armed Conflict - Sanday)
 v740 EM-26iv fmargmar (marriage arrangements)
 v749 EM-18iv inheritance
v754 EM-10dv wifebeating
v754 EM-18iv (but changed depvar)
 v819 EM-10
 v819 EM-24
 v819 EM-26  foodtrade
 v838 EM-12iv
 v838 EM-29iv dateobs
 v857 EM-13
 v857 EM-24 etc ecorich
 v857 EM-29iv
 v857 EM-10
 v872 EM-26
 v872 EM-20
v872 EM-17 iv nuclearfam v210=3 EM-23
 v890 EM-29 femsubs
v1260 EM-27 pathstress (pathogens)
v1648 EM-6iv etc
v1648 EM-29 war
v1675 EM-12 homicide
v1684 EM-13 pctFemPolyg
v1685 EM-21 foodtrade
v1710 EM-23 freintovio (freq or occasional interpersonal violence)
v1721 EM-27dv wealth (wealthy)
v1764 EM-16dv react2viol

Not mapped but an iv

v56 prinrelcaretakeryoung
v270 stratif
v1684 weatherpest
v1734 mkt


SORTED BY EM

v1648 EM-6 
 v300 EM-6 
 v17  EM-10
 v857 EM-10
 v819 EM-10
 v667 EM-11
 v838 EM-12
v1675 EM-12
 v857 EM-13
 v678 EM-13
v1684 EM-13
 v239 EM-17
 v204 EM-18
 v153 EM-18
 v233 EM-18
 v749 EM-18
 v872 EM-20
 v237 EM-20
 v234 EM-20
 v169 EM-20
 v678 EM-21
v1685 EM-21
 v238 EM-21
 v26  EM-23
v1710 EM-23
 v167 EM-24
 v205 EM-24
 v232 EM-24
 v227 EM-24
 v245 EM-24
 v203 EM-24
 v857 EM-24
 v233 EM-24
 v243 EM-24
 v819 EM-24
 v244 EM-26
 v72  EM-26
 v236 EM-26
 v300 EM-26
 v233 EM-26
 v740 EM-26
 v819 EM-26
 v232 EM-26
 v872 EM-26
 v663 EM-26
v1721 EM-27
v1260 EM-27


 v156 EM-27
 v208 EM-27
 v203 EM-27
 v244 EM-27
 v890 EM-29
 v244 EM-29
v1648 EM-29
 v591 EM-29dv
 v838 EM-29iv
 v857 EM-29iv
 v54  EM M-6
 v33  EM M-6
 v664 EM-40
v157
v661
v662
v666
v679
v693
v754
v857
v872
1764