Edu-Mod 2009-10: The Individual Studies
From InterSciWiki
- Dow and Eff Simple Functions
- THESE ARE EXAMPLES FOR VIEWING: THE MODELS USED TO RUN UNDER EFF-DOW software that is no longer maintained see EduR for new R code and examples
Indep/Depvar list - All -- Extra code for maps -- sccs codebook -- Chronology of class meetings fall 2010 -- Working *Rccs* models
- A perfect outline of the steps in analysis leading to a finished model Thanks to User:Rikki Lee
- Edu-Mod 2009-10: The Individual Studies
Edu-Mod 2010 THE INDIVIDUAL STUDIES - see below
Newsletter
Talk:Human Social Complexity - Causal Networks among World Cultures fall 2010
2010 SDC November 2010 Newsletter pdf: Research on Cultural Consequences of Regionally Fluctuating Inequality
Douglas R. White, Scott D. White, Tolga Oztan, and Ren Feng. Nov 2010 Structure and Dynamics Neweletter 1#1.
Eff and Dow 2009
EduR-0
EduR-1
EduR-2
EduR-3
EduR-4
EduR-5
EduR-6
Printable Moral Gods Model & Data
EduMac-1#B_moneystate Depvar Money, IndepVar moneystate Douglas R. White
coef Fstat ddf pvalue VIF
(Intercept) 0.439 0.342 862816.143 0.559 NA
language -0.330 0.548 159281.273 0.459 2.167
distance 0.852 25.439 11340.850 0.000 2.418
moneystate 0.041 0.027 299333.548 0.869 1.160
evileye 0.103 5.579 10200.660 0.018 1.942
ecorich -0.215 4.630 32404.279 0.031 1.076
eextwar -0.025 4.575 78.350 0.036 1.027
caststratLGd 0.640 3.441 6773.598 0.064 1.283
pastoralExch 0.516 2.711 1517867.199 0.100 1.251
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4589836 0.9750803 0.9855391
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.315 32624.416 0.575
Wald.on.restrs 3.152 31.855 0.085
NCV 1.198 3828.385 0.274
SW.normal 4.587 28965.121 0.032
lag..language 1.995 22162.069 0.158
lag..distance 0.976 1194.293 0.323
> aaa<-c(table(depvar), NROW(depvar),name)
> aaa
1 2 3 4
"68" "47" "13" "40"
"186" "god in human affairs"
EduMac-2#5B Final model Depvar ParNurtGuidance Douglas R. White, inspired by Joel Aronoff
coef Fstat ddf pvalue VIF
(Intercept) 23.345 7.979 31196.357 0.005 NA
language -1.084 2.818 21143.234 0.093 1.558
distance 0.352 1.352 60204.628 0.245 1.766
superjh -0.472 6.554 28408.436 0.010 1.151 v237
pigs -1.632 5.621 11009.154 0.018 1.418 v244==2
Rohner800 -0.011 2.750 23502.515 0.097 1.185 v800
agrlateboy -0.309 6.662 361.250 0.010 1.101 v300
valchild 0.108 6.436 1299.385 0.011 1.128 v473-476
R2:final model R2:IV_ language R2:IV_ distance
0.3409194 0.9982641 0.9991794 r=.575
Fstat df pvalue
RESET 9.349 2.174777e+04 0.002 some variable(s) nonlinear
Wald.on.restrs -0.012 4.284000e+00 1.000 no variable missing
NCV 13.476 1.125644e+04 0.000 heteroscadestic
SW.normal 5.983 1.563804e+04 0.014 errors not normally distribute
lag..language 0.005 1.398219e+10 0.945 no autocorrelation
lag..distance 0.000 2.159407e+06 0.997 no autocorrelation
EduMac-3 v892 extwar EduMod-49:_Imputation_and_Regression#B_v892_Aronoff_no_effect
coef Fstat ddf pvalue VIF Dependent variable: External war
(Intercept) 1.601 0.754 423.898 0.386 NA
fyll -0.127 0.018 9909.122 0.893 2.003
fydd 0.728 5.374 9715.912 0.020 1.973
ParNurtGuidance 0.019 0.470 18.591 0.501 1.055
agrlateboy -0.063 4.576 20291.137 0.032 1.154
roots -0.206 1.949 25286.373 0.163 1.106 v233==5
hunt -0.071 4.017 3856754.050 0.045 1.312 v204
superjh -0.217 17.737 21671.825 0.000 1.369
R2:final model R2:IV(distance) R2:IV(language)
0.1833700 0.9728393 0.9226144
Fstat df pvalue
RESET 1.321 14.912 0.268
Wald on restrs. 0.403 230.511 0.526
NCV 0.348 5692.039 0.555
SWnormal 5.776 682.582 0.017
lagll 1.302 186235.024 0.254
lagdd 0.866 74001.230 0.352
1 2 3
"31" "61" "65" "157" "External War"
EduMod-49#B_v892_adding_v893_not_attacked
mnb fst v pval VIF
(Intercept) -1.071 0.405 197.165 0.525 NA
fyll 0.804 0.864 508.224 0.353 2.065
fydd 0.393 1.899 12304.508 0.168 2.034
ParNurtGuidance 0.008 0.133 210.184 0.716 1.124
agrlateboy -0.020 0.513 43.930 0.478 1.193
notattacked 0.473 38.803 178.259 0.000 1.168
roots -0.197 2.158 296.689 0.143 1.098 v233==5
hunt -0.047 2.049 504.443 0.153 1.348 v204
superjh -0.143 8.750 413.141 0.003 1.452
R2:final model R2:IV(distance) R2:IV(language)
0.3464502 0.9805003 0.9362833
Fstat df pvalue
RESET 0.208 45066.272 0.648
Wald on restrs. 0.225 5943.242 0.635
NCV 2.503 331.612 0.115
SWnormal 0.878 38114.230 0.349
lagll 1.357 16659.943 0.244
lagdd 1.000 37648.912 0.317
EduMac-4 v1650 eextwar
EduMac-5 1665 homicide 34 User:Karen_Cachero#6B_Results_v1665_Individual_Aggression_-_Homicide
EduMac-6 v661 fempolpart
EduMac-9 v793 ditto
EduMac-10 v1570 eqChildcare
EduMac-11 v890 eqSubsist
EduMac-12 826 ditto
EduMod78#Yes.3D.3D10A_depvar_1188_Evil_Eye_adding_new_money_variables.2C_money045state_is_better
coef Fstat ddf pvalue VIF
(Intercept) -0.14281021 0.03228216 1027.4236 0.85744522 NA
fydd 0.82708434 32.46451721 285.2557 0.00000003 2.610
fyll -0.06211553 0.06142116 425.9788 0.80438309 2.155
pastoralExch 1.12100828 4.88357255 9823.0579 0.02713642 1.205
moneystate 0.37954294 3.88994886 2684987.9580 0.04857601 1.083 (SCCS$v237>=4)*1*((SCCS$v155==5)*2)+((SCCS$v155==4)*1)
moralgods 0.44306742 13.16877017 277.4541 0.00033879 1.431
R2:final model R2:IV(distance) R2:IV(language)
0.4859918 0.9950356 0.9956219
Fstat df pvalue
RESET 3.609 161182.428 0.057
Wald on restrs. 0.249 7077.252 0.617
NCV 1.267 35420.537 0.260
SWnormal 20.135 106180.984 0.000
lagll 0.586 36588.709 0.444
lagdd 1.244 58269.009 0.265
EduMod78#10B depvar 1188 Evil Eye adding new money variables MONEYSTATE2 subtract money 5, caststratLGD
coef Fstat ddf pvalue VIF
(Intercept) -0.1833308 0.1949801 177975.9924 0.65880419 NA
fydd 0.7914399 55.0155376 10660.9945 0.00000000 1.466
pastoralExch 1.0870849 4.5958485 1200.1910 0.03224993 1.189
moneystate 0.7375458 3.1502707 2415401.9661 0.07591450 1.041 (=5 money) (SCCS$v237>=4)*1*(SCCS$v155==5)*1
moralgods 0.4382242 12.9061250 273.6899 0.00038820 1.439
R2:final model R2:IV(distance) R2:IV(language)
0.4838521 0.9949572 0.9956965
Fstat df pvalue
RESET 4.350 270.038 0.038
Wald on restrs. 0.801 423.650 0.371
NCV 1.507 1550719.469 0.220
SWnormal 20.675 9727.239 0.000
lagll 0.317 621071.875 0.573
lagdd 1.243 7099.691 0.265
BUT NOTE THAT MONEY5 does almost as well
SO ASK WHETHER moneystate-->money045state is better
coef Fstat ddf pvalue VIF
(Intercept) -0.1205639 0.0841055 157875.7821 0.77180936 NA
fydd 0.7552921 48.0916036 24658.3895 0.00000000 1.546
pastoralExch 1.2454233 5.7988678 1415.5782 0.01616368 1.251
money5 0.3729648 4.5610647 84615.1226 0.03270960 1.141
moralgods 0.4367221 12.9882672 317.3184 0.00036380 1.438
R2:final model R2:IV(distance) R2:IV(language)
0.4886790 0.9949572 0.9956965
Fstat df pvalue
RESET 3.589 81.411 0.062
Wald on restrs. 0.801 423.650 0.371
NCV 1.788 407455.784 0.181
SWnormal 21.738 8945.522 0.000
lagll 0.437 279908.329 0.509
lagdd 1.301 8377.409 0.254
EduMod78#10B depvar 1188 Evil Eye adding new money variables
coef Fstat ddf pvalue VIF
(Intercept) -0.5363756 0.4179941 507485.69 0.51793994 NA
fydd 0.8217868 32.9548637 28177.59 0.00000001 2.719
fyll -0.1803265 0.5220060 144023.25 0.46998841 2.275
pastoralExch 1.1051720 4.6189729 54336.22 0.03162457 1.280 v((sccs$v208==1)*1)*(sccs$v858==6)*1
money5 0.2994621 2.7454903 325596.99 0.09753012 1.246 v155 0 1=4 2=5
caststratLGD 1.4298222 6.0501580 59159638.93 0.01390506 1.230 v272
moralgods 0.3565447 8.4401451 31258.63 0.00367284 1.530 v238
R2:final model R2:IV(distance) R2:IV(language)
0.5034358 0.9945598 0.9952893
Fstat df pvalue
RESET 4.642 4190.272 0.031
Wald on restrs. 0.249 16.832 0.624
NCV 2.498 59887.826 0.114
SWnormal 22.833 11688.777 0.000
lagll 0.736 6495.925 0.391
lagdd 1.488 100723.834 0.222
mnb fst v pval VIF
(Intercept) -0.536 0.418 507485.69 0.518 NA
fydd 0.822 32.955 28177.59 0.000 2.719
fyll -0.180 0.522 144023.25 0.470 2.275
pastoralExch 1.105 4.619 54336.22 0.032 1.280 v((sccs$v208==1)*1)*(sccs$v858==6)*1
money5 0.299 2.745 325596.99 0.098 1.246 v155
caststratLGD 1.430 6.050 59159638.93 0.014 1.230 v272
moralgods 0.357 8.440 31258.63 0.004 1.530 v238
1 2 3 4 5 6 7 8
"1" "45" "46" "27" "8" "13" "16" "30" "186" "EvilEye"
EduMod78#9B depvar 1188 Evil Eye with name change of ineq to pastoralExch
9B_with_name_change_of_ineq_to_pastoralExch]] Bridewealth*Pastoralism replaces Milking=
(pastoralExch replaced milking in the model)
coef Fstat ddf pvalue VIF
(Intercept) -0.5306308 0.4393791 59545.9182 0.50742485 NA
fydd 0.8828233 37.7932684 38350.8461 0.00000000 2.746
fyll -0.2790521 1.2348534 66186.5622 0.26646928 2.307
pastoralExch 0.8543720 3.0168861 12077.5183 0.08242704 1.170 v((sccs$v208==1)*1)*(sccs$v858==6)*1
money2 0.6172999 6.0066284 1500533.1269 0.01425235 1.152 v155
caststratLGD 1.5675553 7.6076989 5144005.9315 0.00581198 1.193 v272
moralgods 0.2728972 4.7587558 937.0427 0.02939753 1.580 v238
R2:final model R2:IV(distance) R2:IV(language)
0.5045979 0.9950910 0.9953553
Fstat df pvalue
RESET 5.140 2.931410e+02 0.024
Wald on restrs. 0.724 3.619550e+02 0.395
NCV 1.283 2.610698e+04 0.257
SWnormal 17.347 1.246865e+05 0.000
lagll 0.451 2.695941e+08 0.502
lagdd 1.270 9.152732e+05 0.260
mnb fst v pval VIF
(Intercept) -0.531 0.439 59545.918 0.507 NA
fydd 0.883 37.793 38350.846 0.000 2.746
fyll -0.279 1.235 66186.562 0.266 2.307
pastoralExch 0.854 3.017 12077.518 0.082 1.170
money2 0.617 6.007 1500533.127 0.014 1.152
caststratLGD 1.568 7.608 5144005.931 0.006 1.193
moralgods 0.273 4.759 937.043 0.029 1.580
> aaa
1 2 3 4 5 6 7 8
"1" "45" "46" "27" "8" "13" "16" "30"
"186" "EvilEye" dep_var
1 2 3 4 5 6 7 8
1 45 46 27 8 13 16 30
EduMod-79#9B Money depvar Money - depvar 155 pastoralExch replaces Milking as indep_var Doug White
coef Fstat ddf pvalue VIF
(Intercept) -0.5060522 8.264865 19912.99056 0.00404625 NA
fyll -0.9944897 9.453855 470.08785 0.00222972 4.347 fydd 1.0215292 20.373273 12957.64461 0.00000643 3.701
fratgrpstr 0.1270838 3.819071 21.29725 0.06392843 1.952 v570
milk -0.3518074 5.012013 279.74133 0.02595843 1.563 v245==1
popdens 0.2013530 18.983296 1640.71220 0.00001400 1.575 v156
superjh 0.3248632 32.958936 390441.16667 0.00000001 1.566 v237
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.4761986 0.9906723 0.9957669
Fstat df pvalue
RESET 3.008 650.288 0.083
Wald on restrs. 13.345 159.276 0.000
NCV 14.773 329.448 0.000
SWnormal 0.046 1501.758 0.830
lagll 1.342 1641251.910 0.247
lagdd 2.074 39250.186 0.150
mnb fst v pval VIF
(Intercept) -0.506 8.265 19912.991 0.004 NA
fyll -0.994 9.454 470.088 0.002 4.347
fydd 1.022 20.373 12957.645 0.000 3.701
fratgrpstr 0.127 3.819 21.297 0.064 1.952
milk -0.352 5.012 279.741 0.026 1.563
popdens 0.201 18.983 1640.712 0.000 1.575
superjh 0.325 32.959 390441.167 0.000 1.566
0 1 2 3
"77" "57" "27" "25" "186" "money PREVIOUS
final - no effect of pastoralExch
coef Fstat ddf pvalue VIF
(Intercept) -0.69330479 7.838704 55327.142 0.00511570 NA
fyll -0.73051433 6.029468 228251.930 0.01406971 3.816
fydd 0.84176630 14.576585 161885.679 0.00013463 3.546
caststratLgd 0.40406902 2.084580 3644.851 0.14887905 1.222
foodtrade 0.00822222 1.972942 479426.008 0.16013657 1.138 <--remove
milk -0.25940994 3.407930 193974.644 0.06488530 1.311
popdens 0.21664820 23.288911 1711460.936 0.00000139 1.519
superjh 0.31271892 28.963252 74819.652 0.00000007 1.637
R2:final model R2:IV(distance) R2:IV(language)
0.4813294 0.9914487 0.9958009
Fstat df pvalue
RESET 2.759 6395.248 0.097
Wald on restrs. 17.067 14.912 0.001
NCV 9.082 3347643.574 0.003
SWnormal 0.148 33508.469 0.701
lagll 1.406 1879583.073 0.236
lagdd 1.643 52070.817 0.200
mnb fst v pval VIF
(Intercept) -0.693 7.839 55327.142 0.005 NA
fyll -0.731 6.029 228251.930 0.014 3.816
fydd 0.842 14.577 161885.679 0.000 3.546
caststratLgd 0.404 2.085 3644.851 0.149 1.222
foodtrade 0.008 1.973 479426.008 0.160 1.138
milk -0.259 3.408 193974.644 0.065 1.311
popdens 0.217 23.289 1711460.936 0.000 1.519
superjh 0.313 28.963 74819.652 0.000 1.637
> aaa
0 1 2 3
"77" "57" "27" "25" "186" "money"
EduMod80#Final_model_3 depvar MORAL GODS with high superjh => moneystate
bridewealth p=.08 taken out because it overlaps in meaning with PastoralExch
moneystate dichotomized to remove the older tributary empires AND highest levels of superjh with NO MONEY
Excludes Aztec, Inca and two others as well)
coef Fstat ddf pvalue VIF
(Intercept) 0.7925 1.1150 8940.4772 0.2910 NA
language -0.4745 1.1441 6158.9424 0.2848 2.2120
distance 0.8663 27.5494 20175.6651 0.0000 2.4104
moneystate 0.0486 3.3703 1666.8047 0.0666 1.2193 moneystate=((sccs$v237>=4)*1)*(sccs$v155==5)*1,
evileye 0.1006 5.6799 246115.4130 0.0172 1.9249
ecorich -0.2150 4.8353 206724.7364 0.0279 1.0742
eextwar -0.0325 8.8536 280.3989 0.0032 1.0674
caststratLGd 0.5459 2.6781 14734.6445 0.1018 1.2652
pastoralExch 0.5527 3.2227 45967.6929 0.0726 1.2464
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4786708 0.9772413 0.9858183
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.217 10204.998 0.270
Wald.on.restrs 2.057 16.391 0.170
NCV 2.863 2011.170 0.091
SW.normal 3.147 870.801 0.076
lag..language 1.902 3483.142 0.168
lag..distance 1.293 6737.784 0.255
EduMod80#Final_model_2 with "later" high superjh => newstate
bridewealth p=.08 taken out because it overlaps in meaning with PastoralExch
newstate dichotomized to remove the older tributary empires with highest levels of superjh
Effectively, newstate(s) are monetized states (could excluded Aztec and Inca as well)
coef Fstat ddf pvalue VIF
(Intercept) 0.8218 1.1659 38625.0036 0.2803 NA
language -0.4886 1.1980 84985.6946 0.2737 2.2237
distance 0.8506 26.1706 25885.3513 0.0000 2.4124
newstate 0.0446 2.7472 46456.7368 0.0974 1.2509
evileye 0.1040 5.7775 930.3656 0.0164 1.9233
ecorich -0.2201 4.9736 44417.9828 0.0257 1.0745
eextwar -0.0290 6.9685 743.2881 0.0085 1.0742
caststratLGd 0.5396 2.5709 4237.1802 0.1089 1.2701
pastoralExch 0.5488 3.0908 5760.9615 0.0788 1.2499
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4704268 0.9757884 0.9866815
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.580 4530.351 0.446
Wald.on.restrs 4.129 77.831 0.046
NCV 2.459 272755.923 0.117
SW.normal 3.541 877.642 0.060
lag..language 2.123 140845.779 0.145
lag..distance 1.278 2708.469 0.258
EduMod-80#24 8B No milk No foodstressdepvar Moral gods v238==4 pastoralExch replaces Milking as indep_var Doug White
recovered at
Milk was confounded with pastoralExch
Try a Bridewealth*Milk interaction variables for past_exch, perhaps more societies
coef Fstat ddf pvalue VIF
(Intercept) 1.160 2.265 84020.721 0.132 NA
language -0.785 2.852 25694.706 0.091 2.436
distance 1.064 48.274 270475.086 0.000 2.078
superjh 0.159 6.260 1415.286 0.012 1.371 v237
ecorich -0.294 8.647 17356.003 0.003 1.119 v857
eextwar -0.033 8.262 88.313 0.005 1.078 v1650
bridewealth 0.296 3.260 215068.334 0.071 1.351 v208==1
caststratLGd 0.561 2.866 14870.122 0.090 1.258 v272
pastoralExch 0.562 3.160 12694.236 0.075 1.310 v((sccs$v208==1)*1)*(sccs$v858==6)*1
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4800011 0.9724753 0.9791463
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.067 809.206 0.302
Wald.on.restrs 1.308 887.127 0.253
NCV 3.075 675.371 0.080
SW.normal 3.907 488.292 0.049
lag..language 1.764 3605805.959 0.184
lag..distance 1.359 5048.970 0.244
1 2 3 4
"68" "47" "13" "40" "168" "MoralGods"
EduMod-80#28 7BBB Scott's code depvar Moral gods, indepvar pastoralExch
Foodstress was confounded with new Ecorich
7AAA reconstituted for this result.
coef Fstat ddf pvalue VIF
(Intercept) 1.0018 1.6620 2909.3706 0.1974 NA
language -0.6783 2.1251 8729.3393 0.1449 2.4224
distance 1.0188 44.8719 13787.6535 0.0000 2.0310
superjh 0.1581 6.4161 20840.4405 0.0113 1.3511
ecorich -0.3133 9.7206 9376.6147 0.0018 1.1203
eextwar -0.0321 7.8222 97.3484 0.0062 1.0857
bridewealth 0.2841 2.9705 850275.7190 0.0848 1.3591
caststratLGd 0.6068 3.3909 26392.8984 0.0656 1.2716
pastoralExch 0.5776 3.3139 28766.7898 0.0687 1.3167
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4766355 0.9745583 0.9834284
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.711 22010.415 0.399
Wald.on.restrs 4.698 141.737 0.032
NCV 2.414 5244.173 0.120
SW.normal 4.574 5913.610 0.032
lag..language 1.759 202542.799 0.185
lag..distance 0.975 786770.898 0.323
EduMod-80#BBB Moral gods - depvar v238==4 pastoralExch replaces Milking as indep_var Doug White
see User:Chesszhu#1B Results for v238 High Gods
coef Fstat ddf pvalue VIF
(Intercept) 1.89248661 3.613905 3603.4752 0.05737812 NA
fydd 0.90021643 29.043620 414121.5596 0.00000007 2.576
fyll -0.91368363 3.886237 243774.2137 0.04868451 2.533
pastoralExch 0.40723873 1.608959 1774.2132 0.20480459 1.377 v((sccs$v208==1)*1)*(sccs$v858==6)*1
PCAP -0.03579839 2.789466 6407.9520 0.09493466 1.181 v921 Agricultural potential
PCsize 0.16096145 5.873339 476.4816 0.01574332 1.545 v237 Superjh
milk 0.46745040 4.474213 137606.3445 0.03441185 2.314 v245==1
foodstress 0.20411571 4.644223 792.9312 0.03145866 1.135 v678
eextwar -0.03139114 8.096095 858.2568 0.00454169 1.167 v1650
bridewealth 0.27131665 2.769119 1074.0917 0.09639072 1.345 v208==1
caststratLGd 0.61956346 3.365172 1722.5442 0.06676103 1.331 v272
PCvioIntr -0.21418460 1.821413 196.9496 0.17869444 1.151 v666
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.5027403 0.9934338 0.9849926
> ccc
Fstat df pvalue
RESET 0.460 27754.259 0.498
Wald on restrs. 0.453 34.158 0.506
NCV 2.966 52593.177 0.085
SWnormal 6.656 364.615 0.010
lagll 1.391 6298.024 0.238
lagdd 1.071 610670.388 0.301
> bbb<-data.frame(round(cbind(mnb,fst,v,pval),3)) #.8 changed to back to .3 for significance test
> bbb$VIF[2:NROW(bbb)]<-round(apply(vif,2,mean),3)
> bbb
mnb fst v pval VIF
(Intercept) 1.892 3.614 3603.475 0.057 NA
fydd 0.900 29.044 414121.560 0.000 2.576
fyll -0.914 3.886 243774.214 0.049 2.533
pastoralExch 0.407 1.609 1774.213 0.205 1.377
PCAP -0.036 2.789 6407.952 0.095 1.181
PCsize 0.161 5.873 476.482 0.016 1.545
milk 0.467 4.474 137606.344 0.034 2.314
foodstress 0.204 4.644 792.931 0.031 1.135
eextwar -0.031 8.096 858.257 0.005 1.167
bridewealth 0.271 2.769 1074.092 0.096 1.345
caststratLGd 0.620 3.365 1722.544 0.067 1.331
PCvioIntr -0.214 1.821 196.950 0.179 1.15
1 2 3 4
"68" "47" "13" "40" "168" "MoralGods"
EduMod82#2B results (partial; then reran unrestricted, then restricted) depvar Caststrat Doug White
R2:final model R2:IV(distance) R2:IV(language)
0.2936848 1.0000000 1.0000000
Fstat df pvalue
RESET 16.599 4.258379e+57 0.000
Wald on restrs. 8.263 3.982800e+01 0.006
NCV 48.469 Inf 0.000
SWnormal 42.908 Inf 0.000
lagll 0.000 1.702433e+58 1.000
lagdd 0.000 1.395264e+61 1.000
mnb fst v pval VIF
(Intercept) -0.230 0.292 Inf 0.589 NA
fydd -0.570 2.828 Inf 0.093 4.111
fyll 1.179 6.570 Inf 0.010 3.186
plow 0.370 6.944 Inf 0.008 1.677
pigs -0.315 6.402 Inf 0.011 1.352 v244==2
milk 0.617 19.743 Inf 0.000001 2.460
bovines -0.415 9.050 Inf 0.003 2.661
PCAP 0.087 4.205 3.852951e+62 0.040 1.082
localjh 0.170 11.252 Inf 0.001 1.031
EduMod88#REDO_4B-3 depvar PastoralExch Doug White
coef Fstat ddf pvalue VIF
(Intercept) 0.63434841 45.801912 1400.18566 0.00000000 NA
eextwar 0.00369727 2.859618 143.72339 0.09299646 1.167
bridewealth 0.08046627 5.829870 56518.87106 0.01575944 1.528
money3 0.04665867 7.710904 6459.89820 0.00550465 1.164
fratgrpstr 0.02434053 2.988644 92.82553 0.08717503 2.026
cultints -0.03356325 7.862870 95140.43522 0.00504704 2.494 v232
gath -0.04144233 13.424685 5885.88092 0.00025052 1.642 v203
hunt -0.06219083 28.055482 15336.98557 0.00000012 2.387 v204
fish -0.02672401 8.860968 2048.79671 0.00294747 1.532 v205
ecorich -0.02466200 4.649883 6895.65526 0.03109013 1.207 v857
settype -0.06496535 70.761825 39979.21380 0.00000000 2.009 v234
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.5397413 0.9887614 0.9902409
Fstat df pvalue
RESET 128.310 278.075 0.000
Wald on restrs. 1.158 26.889 0.291
NCV 118.004 1846.946 0.000
SWnormal 28.200 1915.091 0.000
lagll 3.046 286449.936 0.081
lagdd 1.924 8274.933 0.165
mnb fst v pval VIF
(Intercept) 0.634 45.802 1400.186 0.000 NA
eextwar 0.004 2.860 143.723 0.093 1.167
bridewealth 0.080 5.830 56518.871 0.016 1.528
money3 0.047 7.711 6459.898 0.006 1.164
fratgrpstr 0.024 2.989 92.826 0.087 2.026
cultints -0.034 7.863 95140.435 0.005 2.494 v232
gath -0.041 13.425 5885.881 0.000 1.642 v203
hunt -0.062 28.055 15336.986 0.000 2.387 v204
fish -0.027 8.861 2048.797 0.003 1.532 v205
ecorich -0.025 4.650 6895.655 0.031 1.207 v857
settype -0.065 70.762 39979.214 0.000 2.009 v234
0 1
"173" "13" "186" "PastoralExch
EduMod88#Money_and_money2 non sig for milk: p= 0.167 vif=2.520 depvar PastoralExch Doug White
coef Fstat ddf pvalue VIF
(Intercept) 0.76376033 75.2594075 182.159537 0.00000000 NA
fydd -0.01701432 0.0044298 23.216651 0.94750445 2.441
fyll -0.58284814 1.4038677 322.989044 0.23694814 3.013
bridewealth 0.05854409 2.7448296 106.779030 0.10050510 1.860 v208==1
money2 0.18508417 11.8864393 10643.150806 0.00056763 4.742 v155
money -0.04882784 6.9990996 198.082648 0.00880976 4.791 v155
fratgrpstr 0.04838654 7.4360677 7.074902 0.02916823 2.573 v570
cultints -0.02657901 5.4044513 1326.841736 0.02023573 2.610 v232
gath -0.04569304 16.7741982 763.711636 0.00004660 1.833 v203
hunt -0.07496261 45.3070467 787.970328 0.00000000 2.458 v204
fish -0.02747429 10.0648860 118.255630 0.00192581 1.574 v205
ecorich -0.02272979 4.4340364 76776.781515 0.03523234 1.263 v857
popdens -0.04669315 12.4881764 14222.205757 0.00041084 2.857 v156
settype -0.06145296 62.3947586 1529.595201 0.00000000 2.345 v234
himilexp 0.06780612 6.0285979 234.792196 0.01480214 1.197 v899
R2:final model R2:IV(distance) R2:IV(language)
0.6148084 0.9883876 0.9897243
Fstat df pvalue
RESET 151.397 1368.396 0.000
Wald on restrs. 3.664 754.297 0.056
NCV 93.231 5081.924 0.000
SWnormal 19.254 156.465 0.000
lagll 1.123 33539.803 0.289
lagdd 1.431 21274.899 0.232
mnb fst v pval VIF
(Intercept) 0.764 75.259 182.160 0.000 NA
fydd -0.017 0.004 23.217 0.948 2.441
fyll -0.583 1.404 322.989 0.237 3.013
bridewealth 0.059 2.745 106.779 0.101 1.860
money2 0.185 11.886 10643.151 0.001 4.742 v155 1 23 4 5
money -0.049 6.999 198.083 0.009 4.791 v155 cat 4,5 empty (curvilinear effect in total)
fratgrpstr 0.048 7.436 7.075 0.029 2.573
cultints -0.027 5.404 1326.842 0.020 2.610 v232
gath -0.046 16.774 763.712 0.000 1.833 v203
hunt -0.075 45.307 787.970 0.000 2.458 v204
fish -0.027 10.065 118.256 0.002 1.574 v205
ecorich -0.023 4.434 76776.782 0.035 1.263
popdens -0.047 12.488 14222.206 0.000 2.857
settype -0.061 62.395 1529.595 0.000 2.345 v234
himilexp 0.068 6.029 234.792 0.015 1.197
0 1
"173" "13" "186" "PastoralExch"
PREVIOUS
EduMod88#6B_PastoralExch_is_dichotomized_depvar_-_restrictvars_minus_plow Doug White
R2:final model R2:IV(distance) R2:IV(language)
0.5778811 0.9877119 0.9896313
Fstat df pvalue
RESET 140.504 1108.691 0.000
Wald on restrs. 2.330 640.214 0.127
NCV 115.345 550654.160 0.000
SWnormal 26.787 6461.962 0.000
lagll 0.866 12724.667 0.352
lagdd 1.260 127683.842 0.262
mnb fst v pval VIF
(Intercept) 0.849 82.753 914.444 0.000 NA
fydd -0.092 0.133 269.279 0.715 2.488
fyll -0.145 0.095 13328.668 0.758 2.643
money2 0.195 10.338 167.222 0.002 5.273
money -0.040 4.119 207.275 0.044 5.583 v155
fratgrpstr 0.037 8.620 35.014 0.006 1.799
cultints -0.030 6.146 223.383 0.014 2.564 v232
gath -0.053 21.470 965.989 0.000 1.785 v203
hunt -0.082 49.797 455.066 0.000 2.435 v204
fish -0.036 17.676 390.359 0.000 1.481 v205
ecorich -0.025 4.539 5368.653 0.033 1.316
popdens -0.053 14.122 133.238 0.000 2.830
settype -0.064 62.238 11293.338 0.000 2.353 v234
himilexp 0.065 5.484 5211.099 0.019 1.170
0 1
"173" "13" "186" "PastoralExch"
EduMod89 depvar eextwar borrowed from extwar 90 EduMod-49:_Imputation_and_Regression Doug White
EduMod89 depvar eextwar borrowed from extwar EduMod-31: Imputation and Regression Doug White
#==1User:Amy_H._Truong#FINAL.3F? Amy Truong v1248. FEMALE BODY TYPE CONSIDERED MOST ATTRACTIVE
FINAL N=60 cases -
coef Fstat ddf pvalue VIF
(Intercept) 1.2073 0.8836 193.0588 0.3484 NA
language -0.0840 0.0160 582.4068 0.8993 2.2872
distance 0.4503 1.5073 3104.9319 0.2196 2.4584
hunt -0.3266 9.1542 416.9200 0.0026 1.3668 v204
Whyte620 0.5295 6.0972 134.8471 0.0148 1.2625 v620 Physical Punishment of the Spouse Condoned
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.2957465 0.9138426 0.8611659
> ols_stats$restrict_diagnostics
Fstat df pvalue Non-Signif. Pvalues give good models
RESET 0.185 515.230 0.667 No nonlinear relationships
Wald.on.restrs 0.384 21.416 0.542 No other indep. vars are significant
NCV 0.143 337.522 0.705 Error terms not bunched
SW.normal 0.874 5592.427 0.350 Error terms normally distributed
lag..language 0.756 1223.329 0.385 Error terms no language similarities
lag..distance 0.327 65.005 0.569 Error terms no distance similarities
#==1User:Amy_H._Truong#6BB final model? Amy Truong v1248. FEMALE BODY TYPE CONSIDERED MOST ATTRACTIVE
FINAL N=60 cases
combine the two models ... pushing limits of significance
sexagr_hunt=log(10-sccs$v204)*sccs$v175
coef Fstat ddf pvalue VIF
(Intercept) 2.1148 2.8470 364.6485 0.0924 NA
language -0.8289 1.5432 514.8986 0.2147 2.3322
distance 0.8636 6.2562 1109.3981 0.0125 2.2909
hunt -0.2289 5.0392 3084.6664 0.0249 1.2018 v204
sexagr_hunt 0.0709 4.1191 65.1508 0.0465 1.0499 log(10-v204)*v175
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.2598462 0.9716581 0.9642568
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 4.521 690.502 0.034
Wald.on.restrs 2.413 33.823 0.130
NCV 0.237 3323.269 0.627
SW.normal 0.322 215.617 0.571
lag..language 0.284 3014895.180 0.594
lag..distance 0.653 6327.667 0.419
> table(depvar)
depvar
1 2 3 4
20 6 20 12 N=58
hunt<---->nohunt and sex aggr
1248. FEMALE BODY TYPE CONSIDERED MOST ATTRACTIVE
128 . = Missing Data
20 1 = Plump or fat (original code 1)
6 2 = (original code 1.5)
20 3 = Moderate degree of fatness (original code 2)
12 4 = Slim or slender (original code 3)
This finding has an interaction term and small sample size
#==2User:ShawnRidgeway#2B_Working_Results_for_v892 External war (v892)
892. Frequency of external warfare - Attacking
29 . = Missing Data
31 1 = Continual
61 2 = Frequent
65 3 = Infrequent
clearly better than 1650 below - how do they differ?
coef Fstat ddf pvalue VIF
(Intercept) -1.382 0.677 5913.047 0.411 NA
language 0.410 0.238 3280.243 0.626 1.997
distance 0.250 0.696 3017.725 0.404 2.222
nuclearfam 0.392 12.603 237803.239 0.000 1.065
superjh -0.178 10.444 81416.470 0.001 1.974
dateobs 0.001 14.814 786037.080 0.000 1.104
rain 0.057 3.704 46468.687 0.054 1.416
intwar 0.166 4.800 1042.706 0.029 1.084 <-u need to think about whether this is cause or consequence, up to u
himilexp -0.359 10.041 1949.986 0.002 1.225
AP2 0.062 2.955 31525.911 0.086 1.448
money 0.082 3.805 87235.703 0.051 1.618 v155
anim11 -0.053 1.830 275825.588 0.176 1.505 <-can drop
hunt -0.065 3.087 44125.881 0.079 1.795 v204
R2:final model R2:IV_ language R2:IV_ distance
0.3740939 0.9353079 0.9739732
Fstat df pvalue
RESET 10.772 265392.45 0.001
Wald.on.restrs 0.001 22332018.49 0.974
NCV 0.911 13723.34 0.340
SW.normal 5.407 10232.20 0.020
lag..language 1.721 116015.98 0.190
#==3User:Melissamatlock#19b Melissa Matlock v167 premarsexfrq_fem
167. FREQUENCY OF PREMARITAL SEX- FEMALE
77 . = Missing data
51 1 = Universal
19 2 = Moderate
16 3 = Occasional
23 4 = Uncommon
coef Fstat ddf pvalue VIF
(Intercept) -2.854 3.044 10547.962 0.081 NA
language 2.208 7.023 3930.932 0.008 1.587
distance -0.452 0.974 15951.540 0.324 1.565
intwar 0.430 6.590 720.972 0.010 1.091 v891
plow 0.765 5.173 4670.859 0.023 1.447 v243
superjh 0.340 9.142 21690.785 0.003 1.611 v237
settype -0.120 5.633 112725.135 0.018 1.368 v234
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.2235379 0.8383865 0.7667631
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.984 6416.708 0.321
Wald.on.restrs 0.022 1412.291 0.883
NCV 0.206 1929.347 0.650
SW.normal 8.622 19974.902 0.003
lag..language 1.181 341057.632 0.277
lag..distance 2.066 420673.075 0.151
#==13User:Myhuang#7D variable taken out Results Michael Huang Political integration (v157)
157. SCALE 9- POLITICAL INTEGRATION
11 1 = None
72 2 = Autonomous local communities
46 3 = 1 level above community
28 4 = 2 levels above community
29 5 = 3 levels above community
coef Fstat ddf pvalue VIF
(Intercept) -0.331 0.504 1475.952 0.478 NA
language 0.326 1.716 737.928 0.191 3.832
distance -0.105 0.174 789.177 0.677 4.459
money 0.167 11.311 614.422 0.001 1.516 v155
settype 0.068 5.327 10535.304 0.021 1.486 v234
moralgods 0.158 6.826 82.888 0.011 1.268 v238
plow 0.800 17.949 6237.532 0.000 1.523(v243>1)*1
Whyte719 0.204 8.483 54.116 0.005 1.134 v719
ecorich 0.165 10.817 1618.296 0.001 1.146 v857
himilexp 0.525 15.582 183.903 0.000 1.145(v899==1)*1
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.5748788 0.9991338 0.9968453
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 7.303 256.773 0.007
Wald.on.restrs 7.351 45.260 0.009
NCV 0.773 298.223 0.380
SW.normal 0.679 522.736 0.410
lag..language 0.233 97799.749 0.629
lag..distance 0.291 30122.871 0.590
coef Fstat ddf pvalue VIF
(Intercept) -0.331 0.504 1475.952 0.478 NA
language 0.326 1.716 737.928 0.191 3.832
distance -0.105 0.174 789.177 0.677 4.459
money 0.167 11.311 614.422 0.001 1.516 v155 (MONEY)
settype 0.068 5.327 10535.304 0.021 1.486 v234 (SETTLEMENT PATTERNS/Nomadic or fully migratory v settled)
moralgods 0.158 6.826 82.888 0.011 1.268 v238 (HIGH GODS)
plow 0.800 17.949 6237.532 0.000 1.523(v243>1)*1 (Animal drawn plow)
Whyte719 0.204 8.483 54.116 0.005 1.134 v719 (Total Pages in Sources for the ethnography) Data Quality Control
ecorich 0.165 10.817 1618.296 0.001 1.146 v857 (Climate Type - Ordered in terms of Open Access to Rich Ecological Resources)
himilexp 0.525 15.582 183.903 0.000 1.145(v899==1)*1 (MILITARY EXPECTATIONS I-PRE-STATE)
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.5748788 0.9991338 0.9968453
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 7.303 256.773 0.007
Wald.on.restrs 7.351 45.260 0.009
NCV 0.773 298.223 0.380
SW.normal 0.679 522.736 0.410
lag..language 0.233 97799.749 0.629
lag..distance 0.291 30122.871 0.590
#==4User:Kcoste#8F v862 Standard Polygamy Code
coef Fstat ddf pvalue VIF
(Intercept) 1.591 1.775 74.707 0.187 NA
language -0.356 0.579 152.769 0.448 2.086
distance 0.694 14.189 1379.397 0.000 2.290
fratgrpstr 0.194 10.713 654.929 0.001 1.628 v570
marrcaptives 0.274 12.143 261.021 0.001 1.353 v870
milk -0.641 11.392 735.810 0.001 1.897 v245>1*1
Whyte615 0.113 5.132 60.053 0.027 1.167 v615
foodtrade -0.014 4.694 1912.754 0.030 1.096 v819
plow -0.360 2.661 291.756 0.104 1.711 v243>1*1
fish -0.114 7.887 379.461 0.005 1.289 v205
plunder -0.314 4.282 139.792 0.040 1.244 v912
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4621228 0.9947063 0.9980123
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.596 113.361 0.209
Wald.on.restrs 41.430 175.188 0.000
NCV 2.737 328.225 0.099
SW.normal 0.866 15.064 0.367
lag..language 0.139 10962.126 0.710
lag..distance 0.109 290080.026 0.741
#==14User:Tara Chamberlain#20BB results - no v212, fratgrpstr, pathstress pathstress 721Nationality of authorities Transfer of Residence at Marriage (v216)
See also: User:Tara Chamberlain#12B (keep variables that worked in 10B)
216. TRANSFER OF RESIDENCE AT MARRIAGE: AFTER FIRST YEARS
1 . = Missing Data
121126 1 = Wife to Husband's Group
33 28 2 = Couple to either Group or Neolocal
30 3 = Husband to Wife's Group
1 4 = No Common Residence
coef Fstat ddf pvalue VIF
(Intercept) 1.108 2.655 1093.621 0.104 NA
language 0.250 0.289 5618.673 0.591 2.974
distance 0.113 0.098 762.587 0.754 2.592
Whyte631 0.133 2.113 10.683 0.175 1.087 v631 Value of Labor (female)
anim -0.118 11.777 898.183 0.001 1.839 v206
settype -0.066 6.150 32024.511 0.013 1.458 v234
segadlboys 0.064 2.057 88.170 0.155 1.130 v242
bovines 0.280 3.585 15215.592 0.058 1.822 v244
famsize 0.033 4.208 92665.957 0.040 1.155 v80
exogamy -0.099 4.449 4716.123 0.035 1.113 v72
foodtrade -0.009 2.368 1173.375 0.124 1.165 v819
ncmallow -0.030 1.846 4669.203 0.174 1.099 v227
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.2114603 0.9948187 0.9894428
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 8.874 612.743 0.003
Wald.on.restrs 1.011 10.052 0.338
NCV 11.603 655.713 0.001
SW.normal 27.211 413.554 0.000
lag..language 0.303 5474.348 0.582
lag..distance 0.330 5536.903 0.566
NOT AS GOOD A MODEL
216. TRANSFER OF RESIDENCE AT MARRIAGE: AFTER FIRST YEARS
1 . = Missing Data
121126 1 = Wife to Husband's Group
33 28 2 = Couple to either Group or Neolocal
30 3 = Husband to Wife's Group
1 4 = No Common Residence
coef Fstat ddf pvalue VIF
(Intercept) 2.039 7.422 72.992 0.008 NA
language -0.069 0.022 3704.880 0.882 3.051
distance 0.053 0.020 123.969 0.888 2.656
fratgrpstr -0.164 4.581 10.669 0.056 1.972 v570 includes patrilocality
anim -0.068 3.682 692.243 0.055 1.972 v206
settype -0.051 3.571 2862.366 0.059 1.511 v234
bovines 0.225 2.384 6429.491 0.123 1.794 v244 might have VIF with animals
famsize 0.032 4.008 1835.967 0.045 1.160 v80
exogamy -0.106 5.363 10129.656 0.021 1.085 v72
foodtrade -0.008 2.040 1129.482 0.153 1.126 v819
pathstress 0.028 2.902 453.593 0.089 1.318 v1260 probably not causal (correlated with polygyny
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.2265011 0.9951105 0.9872714
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 13.282 88.479 0.000
Wald.on.restrs 0.245 8.364 0.634
NCV 11.393 5213.017 0.001
SW.normal 28.764 553.662 0.000
lag..language 0.257 11161.428 0.612
lag..distance 0.431 13923.423 0.512
#==24User:Natasha Strauss#4C output with final removal insig. var. - Sleeping proximity between husbands and wives (v750)
750. (Distant) Sleeping Proximity between Husbands and Wives (VAR LABEL REVERSED)
76 . = Missing data
43 1 = Same room and close proximity: e.g., same bed, same
blanket, touching, back to back, adjacent sleeping
places allocated to spouses
15 2 = Same room but no close proximity: e.g., different
beds, different hammocks, different sections of room
45 3 = Same room, proximity unknown
7 4 = Different rooms
coef Fstat ddf pvalue VIF
(Intercept) 14.910 44.317 2235919.8 0.000 NA
language -5.930 34.149 11509580.0 0.000 1.236
distance 0.859 10.219 558939.9 0.001 1.226
famsize 0.055 5.512 24357687.0 0.019 1.068 v68 - family size
rain -0.169 9.705 105898340.9 0.002 2.146 v854 - niche temperature
temp -0.178 11.280 22392936.6 0.001 1.468 v855 - niche rainfall
pathstress -0.103 11.989 2389405.1 0.001 1.856 v1260 - total pathogen stress
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3697587 0.9973861 0.9987345
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.481 590544.849 0.224
Wald.on.restrs -0.221 4.023 1.000
NCV 0.019 704014.561 0.890
SW.normal 4.483 19375947.384 0.034
lag..language 0.000 29557276.939 0.993
lag..distance 0.000 316665.367 0.986
#==5User:Elaine_Kao#8B_RESULTS Norms Of Premarital Sex Behavior of Females (v282)
coef Fstat ddf pvalue VIF
(Intercept) 7.629 9.903 5471.176 0.002 NA
language 0.154 0.043 3002.676 0.837 1.651
distance -0.663 3.229 278.336 0.073 1.651
famhousehold 0.199 5.757 761.898 0.017 1.185 v67
ndrymonth -0.070 2.842 88.972 0.095 1.393 v196
anim 0.174 6.699 1350.977 0.010 1.496 v206
cultints 0.190 3.973 400.890 0.047 1.913 v232
localjh -0.295 2.008 638.672 0.157 1.181 v236-1
superjh -0.333 5.759 515.623 0.017 2.179 v237
moralgods -0.224 3.224 3076.309 0.073 1.616 v238
agrlateboy -0.164 5.404 48.246 0.024 1.136 v300
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3788700 0.9865941 0.9888495
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 3.648 267.790 0.057
Wald.on.restrs -0.159 4.896 1.000
NCV 2.922 118.611 0.090
SW.normal 0.009 219.343 0.924
lag..language 0.091 70538.196 0.763
lag..distance 0.200 235099.080 0.654
v282 NORMS OF PREMARITAL SEX BEHAVIOR OF GIRLS
40 . = Missing data
13 1 = Early marriage of females (at or before puberty)
38 2 = Insistence on virginity
37 3 = Prohibited but weakly censured and not infrequent
16 4 = Allowed, censured only if pregnancy results
5 5 = Trial marriage, promiscuous relations prohibited
37 6 = Freely permitted, even if pregnancy results
Literary Articles:
http://www.jstor.org/stable/2088945 :Cultural Relativism and Premarital Sex Norms
http://www.jstor.org/stable/3773508 : Measurement of Adolescent Sexual Behavior in Standard Sample of Societies
http://www.jstor.org/stable/3772907?seq=27 : Standard Cross Cultural Sample
http://www.jstor.org/stable/2089719 : Value-Behavior Discrepancies Regarding Premarital Coitus in Three Western Cultures
#==9User:Heather_Hechter#5B_Results_from_5A Heather Hechter v678 Hunger
Hunger / Foodstress v678
678. Food Stress or Hunger
48 . = Missing data
47 1 = food constant
62 2 = occasional hunger or famine
26 3 = periodic or chronic hunger
3 4 = starvation or evidence of protein deficiency
(note: exact coding distinction between 3 and 4 unclear)
coef Fstat ddf pvalue VIF
(Intercept) 2.401 2.095 112.033 0.151 NA
language -0.792 0.859 219.745 0.355 1.848
distance -0.061 0.033 5568.915 0.856 1.553
climatetype -0.131 7.864 10073.907 0.005 1.155 v857
fish 0.136 4.373 401.347 0.037 1.436 v7 (v205)
recurfam 0.375 9.424 48.671 0.003 1.198 v1269
moralgod 0.194 8.699 4405.893 0.003 1.758 v238
brideprice -0.247 3.062 30748.450 0.080 1.421 v208
roots 0.285 3.180 4163.082 0.075 1.257 v233==5
milk 0.289 2.839 7287.937 0.092 1.796 v245
agrlateboy 0.056 2.814 531.892 0.094 1.222 v300
migr 0.254 3.823 268.146 0.052 1.184 v677
Whyte721 -0.147 2.249 38.509 0.142 1.658 v721
himilexp -0.331 5.933 64.897 0.018 1.132 v899
intwarB 0.020 4.058 493.214 0.045 1.233 v1649
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3454200 0.9922779 0.9942996
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.598 26.740 0.446
Wald.on.restrs 0.088 6.792 0.775
NCV 1.383 13811.562 0.240
SW.normal 4.756 31.844 0.037
lag..language 0.012 5416227.698 0.912
lag..distance 0.058 127459.951 0.810
>
#==7User:Chesszhu#1B Results for v238 High Gods
The earlier error, aliased coefficients in the model, came from adding language and distance to your restrict_vars. see: EduMod80#B
238. HIGH GODS
18 . = Missing data
68 1 = Absent or not reported
47 2 = Present but not active in human affairs
13 3 = Present and active in human affairs but not
supportive of human morality
40 4 = Present, active, and specifically supportive of human morality
coef Fstat ddf pvalue VIF
(Intercept) 1.670 4.028 280504.949 0.045 NA
language -0.959 4.593 14768.124 0.032 2.381
distance 0.917 31.757 11802.445 0.000 2.446
brideprice 0.381 6.033 4178.827 0.014 1.228
milk 0.506 5.406 16760.330 0.020 2.241
ecorich -0.284 8.282 21678.395 0.004 1.129
extwarB -0.030 7.744 3533.138 0.005 1.132
hunger 0.215 4.909 283.773 0.028 1.122
beyondlocal 0.175 7.945 6810.958 0.005 1.378
R2:final model R2:IV_ language R2:IV_ distance
0.4970537 0.9949873 0.9976981
Fstat df pvalue
RESET 0.692 41376.243 0.405
Wald.on.restrs 0.099 6.592 0.763
NCV 4.639 1281.250 0.031
SW.normal 4.995 268645.794 0.025
lag..language 0.204 26981.700 0.651
lag..distance 0.247 64235.989 0.620
#==10User:Victoria Valverde#15B: Results from 15A v1797 Gossip about scandal
EduMod87#12B_-_how_far_off_are_the_results_if_the_DQC_variables_are_eliminated? -- Comparison by DRW: Without DQC (Data quality controls): would have missed PATRILINEAL (which ties in with other variables) and the positive LANGUAGE family effect!
1797. Gossip on scandal
51 . = no data
73 0 = absent
18 1 = present for males only
41 2 = present for both sexes
3 3 = present for females only
coef Fstat ddf pvalue VIF
(Intercept) 12.252 4.962 23830.436 0.026 NA
language 1.079 3.553 486.946 0.060 1.894
distance 0.109 0.134 189.264 0.715 2.113
nuclearfam -0.484 8.080 3084.667 0.005 1.268 (v210<=3)*1
Mobility -0.176 2.990 228.714 0.085 1.416 v786
Rohner798 -0.007 5.654 38879.772 0.017 1.154 v798
Rohner800 -0.007 5.740 3234.312 0.017 1.524 v800
Rohner809 0.842 9.883 3594.534 0.002 1.573 v809
politics -0.251 7.615 25390.662 0.006 2.117 v835
patrilineal 0.097 4.066 13983.554 0.044 1.278 v836
climate 0.193 8.208 956.624 0.004 1.404 v857
foodtrade 0.023 9.403 893.713 0.002 1.474 v819
himilexp 0.399 6.289 178469.123 0.012 1.306 (v899==1)*1
rooming -0.281 2.594 1183.408 0.108 1.207 v864
appearance 0.041 1.360 23.274 0.255 1.323 v932
intwarB -0.032 7.284 427.118 0.007 1.241 v1649
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3820847 0.9975521 0.9977220
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 3.347 385.221 0.068
Wald.on.restrs 0.559 5.462 0.486
NCV 6.091 6827.591 0.014
SW.normal 4.893 131.819 0.029
lag..language 0.020 2672363.560 0.887
lag..distance 0.042 455963.588 0.837
11B model
coef Fstat ddf pvalue VIF
(Intercept) 12.1098 4.7860 1187469.9473 0.0287 NA
language 1.0021 3.2449 48129.1762 0.0717 1.8591
distance 0.2860 1.0614 777.6045 0.3032 1.9203
nuclearfam -0.4171 6.5871 9534.5476 0.0103 1.1484
Mobility -0.2348 4.4068 20.7628 0.0482 1.3391 v786
Rohner798 -0.0068 5.4838 209477.3585 0.0192 1.1571 DQC Date of Publication
Rohner800 -0.0068 5.9484 13894.0047 0.0147 1.5105 DQC Number of Pages Related to Child Training Practices
Rohner809 0.8268 9.8939 4273.9984 0.0017 1.4971 DQC Use of Tests (e.g., Rorschach, TAT, sentence completion, I.Q.)
politics -0.2617 7.0606 72.3628 0.0097 2.1398 v835 Political Integration
lingual 0.0971 4.0038 2744.3164 0.0455 1.2701 v836 Rule of Descent: Patrilineal
lingual1 0.1796 7.5776 2116.1755 0.0060 1.3229 v857 Climate type
foodtrade 0.0205 7.9680 38279.5569 0.0048 1.3838
himilexp 0.4279 6.1743 78.7755 0.0151 1.3170
intwarB -0.0346 8.6176 8785.4066 0.0033 1.2492
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3594879 0.9958365 0.9963791
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.755 81.676 0.189
Wald.on.restrs -0.138 4.978 1.000
NCV 5.942 493.892 0.015
SW.normal 4.732 46.882 0.035
lag..language 0.056 182055.743 0.813
lag..distance 0.086 53576.224 0.770
#==6User:Aimyn#21B depvar=sccs$v740>4 (arranged4fem) with v1248 Female body type significant Depvar: Marriage arrangements
coef Fstat ddf pvalue VIF
(Intercept) 3.331 1.139 1094.359 0.286 NA
language -0.031 0.001 2261.350 0.971 1.528
distance 0.047 0.010 161.026 0.921 1.465
fembodytype 0.223 2.407 14.785 0.142 1.101 v1428 (slim)
bovines 0.832 4.807 83.529 0.031 1.608 v244
cereals -0.490 2.480 8340.874 0.115 1.439 v233
agrlateboy 0.141 2.651 16.004 0.123 1.083 v300
Paige657 -0.632 2.922 160.980 0.089 1.106 v657 Divorce easy (neg:Hard)
pctFemPolyg 0.012 4.596 37.315 0.039 1.079 v873
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.1863415 0.9796791 0.9810386
> ols_stats$restrict_diagnostics
Fstat df pvalue Non-Signif. Pvalues give good models
RESET 0.165 51.784 0.687 No nonlinear relationships
Wald.on.restrs 1.172 11.133 0.302 No other indep. vars are significant
NCV 0.165 743.011 0.685 Error terms not bunched
SW.normal 9.562 6418.321 0.002 Error terms NOT normally distributed
lag..language 0.135 435608.833 0.713 Error terms no language similarities
lag..distance 0.120 24671.162 0.729 Error terms no ldistance similarities
> table(depvar)
depvar
1 2 3 4 5 6
12 40 4 27 35 33
#==21User:NCouts#3B_Results_from_var_903
coef Fstat ddf pvalue VIF
(Intercept) 5.573 13.035 2263.962 0.000 NA
language -3.512 13.450 1149.311 0.000 1.272
distance 0.613 2.984 472.585 0.085 1.422
plow -0.301 4.519 7093.706 0.034 1.333
plunder 0.342 8.332 165.693 0.004 1.302 v912
segadlboys -0.101 5.299 48.325 0.026 1.143
Whyte719 -0.102 3.155 60.518 0.081 1.148
Rohner810 0.677 7.318 1809.748 0.007 1.036
extwar 0.299 14.737 51.964 0.000 1.236
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3936560 0.9716874 0.9851775
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 2.365 605.753 0.125
Wald.on.restrs 2.608 7.357 0.148
NCV 1.569 2219.815 0.210
SW.normal 1.094 292.074 0.296
lag..language 0.091 6177491.180 0.763
lag..distance 0.074 128280.041 0.786
#==25User:Robert De Ramos#11B v227 Type of slavery
274. TYPE OF SLAVERY
6 . = Missing data
100 1 = Absence or near absence
27 2 = Incipient or nonhereditary
9 3 = Reported but type not identified
44 4 = Hereditary and socially significant
coef Fstat ddf pvalue VIF
(Intercept) 0.281 0.062 2896.137 0.803 NA
language 0.642 1.927 3021.781 0.165 3.014
distance 0.257 0.895 1174.016 0.344 3.077
plunder -0.464 6.481 337.141 0.011 1.134 v912
fish 0.100 3.332 5932.624 0.068 1.561 v205
anim 0.145 9.209 4506.707 0.002 1.509
ncmallow 0.067 3.935 1082.629 0.048 1.118 Few or no COUSIN MARRIAGES (Allowed)
settype 0.069 3.444 10324.984 0.063 1.235 v234
segadlboys -0.124 3.225 348.150 0.073 1.280
Paige661 0.263 2.108 242.380 0.148 1.136 Female Political Participation, at least informal
dateobs -0.001 4.429 78472.184 0.035 1.113 Earlier
pathstress 0.076 7.042 1130.501 0.008 1.731
extwarB 0.022 2.487 148.000 0.117 1.208
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3297544 0.9947275 0.9946049
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 2.815 737.270 0.094 May be missing variable in indepvar list
Wald.on.restrs 0.017 515.171 0.898
NCV 6.606 77.681 0.012
SW.normal 11.763 150.133 0.001
lag..language 0.267 660658.712 0.605
lag..distance 0.341 22384.798 0.559
#==12User:Karen Cachero#6B_Results_v1665_Individual_Aggression_-_Homicide
coef Fstat ddf pvalue VIF
(Intercept) -12.568 4.932 601.770 0.027 NA
language -1.143 3.008 829.542 0.083 2.192
distance -0.192 0.227 1245.306 0.634 1.833
famhousehold 0.432 8.951 2359.902 0.003 1.274 v67
money 0.352 5.034 944.286 0.025 1.145 v155
agrlateboy 0.374 9.728 220.733 0.002 1.107 v300
Rohner799 0.002 3.628 9807.391 0.057 1.039 v799
foodtrade -0.042 3.723 61861.616 0.054 1.144 v819
dateobs 0.008 6.961 1250.753 0.008 1.196 v838
intwarB 0.192 25.760 77.337 0.000 1.177 v1649
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4145503 0.9984555 0.9977719
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 2.242 752.271 0.135
Wald.on.restrs 0.082 5.062 0.786
NCV 0.984 297.628 0.322
SW.normal 0.414 303.490 0.521
lag..language 0.005 922159.615 0.946
lag..distance 0.003 4581070.200 0.953 begin_of_the_skype_highlighting              003 4581070.200 0.953      end_of_the_skype_highlighting
#==20User:Elizabeth Yeh#13B v678 Food Stress or Hunger
coef Fstat ddf pvalue VIF
(Intercept) 0.408 0.078 18942.723 0.780 NA
language 0.182 0.057 2938.284 0.811 1.746
distance 0.071 0.048 14961.330 0.827 1.603
fish 0.050 1.580 279.872 0.210 1.579 v205
moralgods 0.138 4.267 89.100 0.042 1.731 v238
migr 0.227 3.284 217.678 0.071 1.186 v677==2)*1
foodsur -0.167 3.909 275.213 0.049 1.232 v21
foodsup 0.112 4.017 4972.449 0.045 1.233 v22
himilexp -0.291 4.529 92.132 0.036 1.311 v899==1)*1
intwarB 0.028 8.794 350.169 0.003 1.180 v891
Whyte721 -0.228 6.567 46.486 0.014 1.492 v721
shortstar 0.437 10.590 251.707 0.001 1.202 v1262
recurfam 0.208 3.509 129.752 0.063 1.210 v1269
climatetype -0.115 5.711 239.207 0.018 1.250 v857
roots 0.304 3.808 454.517 0.052 1.269 v233==5
milk 0.337 3.276 42.189 0.077 1.838 v245>1)*1
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4141278 0.9858111 0.9898608
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 2.628 1986.189 0.105
Wald.on.restrs 0.304 14.193 0.590
NCV 5.261 1042.108 0.022
SW.normal 3.304 800.702 0.069
lag..language 0.043 51352.049 0.835
lag..distance 0.141 406908.365 0.708
. 1 2 3 4 "Food Stress Or Hunger"
"48" "47" "62" "26" "3"
#==16User:Rikki Lee#13B Results from 13A (Final Model) v165 Premarital sex attitudes-female, with variable numbers added
coef Fstat ddf pvalue VIF (Intercept) -29.668 3.816 1617948.056 0.051 NA language 0.057 0.007 1174831.929 0.934 1.561 distance 0.107 0.161 108936.772 0.688 1.691 moralgods 0.325 5.356 4900.005 0.021 1.388 v238 (high gods) himilexp 0.605 3.768 1705.698 0.052 1.075 v899 (military expectations) money 0.373 13.119 47689.956 0.000 1.153 v155 (currency) Rohner798 0.015 3.852 2513824.581 0.050 1.114 v798 (date of publication) ecorich -0.483 4.780 6195.532 0.029 1.290 v857 (climate type) intwar 0.391 3.300 359.858 0.070 1.088 v891 (frequency of internal war)
R2:final model R2:IV_ language R2:IV_ distance
0.3375411 1.0000000 1.0000000
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.169 4.759279e+04 0.681
Wald.on.restrs NaN NaN NaN
NCV 0.728 1.344465e+04 0.393
SW.normal 1.682 1.796476e+04 0.195
lag..language 0.000 4.240144e+58 1.000
lag..distance 0.000 1.482963e+62 1.000
#==23User:Kherr#4L Results v22 Food Supply (4H by Tolga) Keenan Herr
v22. Food Supply (Ecological or Distribution Network)
74 1 = Year-round food supply locally
10 2 = Daily variation in food supply
86 3 = Seasonal variation
14 4 = Annual variation
2 5 = Imported food supply
coef Fstat ddf pvalue VIF
(Intercept) 4.235 3.856 16643.470 0.050 NA
language -1.510 2.107 16035.241 0.147 1.487
distance 0.593 5.907 36300.445 0.015 1.617
fixityset 0.166 18.946 1759142.140 0.000 1.183
anim -0.155 10.638 412765.383 0.001 2.115
ncmallow -0.072 6.192 343.888 0.013 1.084
tree -0.723 6.170 258021.877 0.013 1.148
roots -0.811 15.538 432290.772 0.000 1.436 v233==5
milk 0.528 5.962 362366.271 0.015 2.077
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.2927561 0.9602346 0.9918506
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 2.233 712.226 0.136
Wald.on.restrs 2.756 13.250 0.120
NCV 0.856 535.643 0.355
SW.normal 0.258 20315.234 0.611
lag..language 0.163 640204.955 0.687
lag..distance 0.386 100949.373 0.534
4H coef Fstat ddf pvalue VIF
(Intercept) 4.443 4.022 894.817 0.045 NA
language -1.412 1.771 666.331 0.184 1.503
distance 0.615 6.253 4256.021 0.012 1.636
fixityset 0.184 20.065 214418.513 0.000 1.375
polygamy -0.155 2.180 13045525.965 0.140 1.165
anim -0.155 10.562 89849.112 0.001 2.132
ncmallow -0.079 7.363 716.622 0.007 1.103
tree -0.823 7.733 304282.747 0.005 1.199
roots -0.807 14.692 32060.332 0.000 1.510 v233==5
plow -0.363 2.472 300609.584 0.116 1.635
milk 0.619 7.923 34845240.424 0.005 2.170
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3067651 0.9656214 0.9925368
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.598 33996.619 0.206
Wald.on.restrs 2.259 11.668 0.159
NCV 0.905 24058.319 0.341
SW.normal 1.337 3890.684 0.248
lag..language 0.156 22184.644 0.693
lag..distance 0.291 244029.129 0.589
#==15User:Ychang2#11B v169 Extramarital Sex
169. EXTRAMARITAL SEX
77 . = Missing data
13 1 = Single standard- both allowed
48 2 = Double standard- husband only
24 3 = Double standard- both forbidden, women punished more
24 4 = Single standard- both condemned equally
setwd("C:/My Documents/sccs")
library(sccs)
data(sccs)
table(sccs$v169,sccs$v156,useNA="ifany")
1 2 3 4 5 Popdens
1 4 3 1 4 1
2 17 2 6 9 14
3 10 2 2 2 8
4 8 4 3 5 4
coef Fstat ddf pvalue VIF
(Intercept) -0.947 0.277 181901.27 0.599 NA
language 1.215 2.279 93513.40 0.131 1.563
distance -0.255 0.436 144229.51 0.509 1.470
popdens -0.310 13.687 19308.60 0.000 2.635 v156 (net of other variables)
ncmallow 0.107 7.970 30476.77 0.005 1.309 v227 NUMBER OF COUSIN MARRIAGES (Allowed)
tree -0.565 2.024 1052742.74 0.155 1.153 v233==4 MAJOR CROP TYPE- TREES
roots -0.434 3.209 386382.61 0.073 1.283 v233==5 MAJOR CROP TYPE- ROOTS
settype 0.270 23.570 541370.13 0.000 2.720 v234 SETTLEMENT PATTERNS
foodtrade 0.025 5.912 167095.65 0.015 1.276 v819 Percent Importance (of Trade) in Contribution to Subsistence and Trade
R2:final model R2:IV_ language R2:IV_ distance
0.2328807 0.9985488 0.9992735
Fstat df pvalue
RESET 1.371 3.556785e+04 0.242
Wald.on.restrs -0.099 4.073000e+00 1.000
NCV 3.143 1.416529e+04 0.076
SW.normal 2.294 8.580692e+04 0.130
lag..language 0.000 2.329977e+07 0.986
lag..distance 0.001 7.061082e+09 0.980
#==8User:Wilsonbm#4B_v238_High_gods
Breanna Brown and Eff: The State and the Supernatural: Support for Prosocial Behavior setwd("C:/My Documents/sccs") library(sccs) data(sccs) table(sccs$v626,sccs$v238,useNA="ifany") 1 2 3 4 NaN 1 7 8 3 8 1 2 29 18 5 7 7 (No) Belief that Women are Generally Inferior to Men coef Fstat ddf pvalue VIF (Intercept) 1.592 3.164 242.740 0.077 NA language -0.633 2.098 139588.662 0.147 2.099 distance 0.934 30.272 8781.146 0.000 2.433 Whyte626 -0.335 3.087 24.025 0.092 1.115 v626 (No) Belief that Women are Generally Inferior to Men (v626) extwarB -0.032 7.527 125.977 0.007 1.029 v1650 Frequency of External Warfare (v1650) foodscarc 0.112 3.983 839.288 0.046 1.010 v1685 Chronic Resource Problems (v1685) castrast 0.229 4.377 3659.845 0.036 1.176 v272 Caste Stratification (v272) evileye 0.474 6.488 2955.618 0.011 1.620 Evil Eye Belief (v1189) R2:final model R2:IV_ language R2:IV_ distance 0.4465931 1.0000000 1.0000000 Fstat df pvalue RESET 2.533 4.598200e+01 0.118 Wald.on.restrs NaN NaN NaN NCV 0.706 2.106470e+02 0.402 SW.normal 2.525 4.215870e+02 0.113 lag..language 0.000 4.435215e+59 1.000 lag..distance 0.000 1.104129e+60 1.000
#==18User:Cabaltem#11B ArrangedMarriageMTCv740.R RESULTS FINAL Marriage arrangements - v740
coef Fstat ddf pvalue VIF
(Intercept) 3.229 1.026 702.101 0.312 NA
language -0.930 1.458 2574.111 0.227 1.553
distance 0.407 0.873 640.354 0.351 1.631
writing 0.330 9.319 1285.007 0.002 1.614 v149
normpre -0.341 20.850 242.192 0.000 1.091 v282
castestrat 0.374 4.984 1404451.865 0.026 1.153 v273
bovines 0.748 5.213 833.909 0.023 1.657 v244
cereals -0.677 5.388 15199.078 0.020 1.549 v233
Paige662 -0.522 3.264 104.079 0.074 1.110 v662
pctFemPolyg 0.019 15.986 127.436 0.000 1.121 v872
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3401445 0.9893765 0.9901626
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.413 57.159 0.239
Wald.on.restrs 0.311 7.166 0.594
NCV 1.581 397.752 0.209
SW.normal 1.023 243.833 0.313
lag..language 0.052 66673.807 0.820
lag..distance 0.028 417929.487 0.866
#==17User:medicboi44#5B_Final_R2 v215 MARITAL RESIDENCE
coef Fstat ddf pvalue VIF
(Intercept) 7.664 26.203 65.431 0.000 NA
language 0.782 5.065 337.428 0.025 3.867
distance -0.830 6.675 394.282 0.010 4.076
distmoved -1.097 20.747 11.990 0.001 1.181
socstra 0.277 4.979 63.805 0.029 1.356
intwarB 0.051 3.653 33.230 0.065 1.101
domorg -0.178 8.358 105.427 0.005 1.056
fem_agri -0.014 5.099 2632.906 0.024 1.109
fratgrpstr 0.434 7.554 38.425 0.009 1.768
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.4488596 0.9992502 0.9980124
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 13.565 464.772 0.000
Wald.on.restrs -0.277 5.126 1.000
NCV 8.301 86.854 0.005
SW.normal 9.925 45.912 0.003
lag..language 0.056 147832.679 0.812
lag..distance 0.049 139543.644 0.825
> table(depvar)
depvar
0 1 2 3 4 5 6 7 8 9
1 16 14 1 12 8 1 48 69 15
#==11User:Alyssakeyes#3B depvar famine 1683
coef Fstat ddf pvalue VIF
(Intercept) -0.068 0.002 428260.6 0.965 NA
language 0.974 1.900 531894.5 0.168 1.681
distance -0.286 0.503 1807306.2 0.478 1.954
plow -1.332 8.539 97962615.4 0.003 1.704
bovines 0.742 3.843 65359800.0 0.050 1.868
temp 0.251 11.242 11075456.1 0.001 1.265
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.1602484 0.9964526 0.9965415
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.000 47525328.06 0.984
Wald.on.restrs -0.182 4.91 1.000
NCV 0.412 30978946.08 0.521
SW.normal 13.754 126654531.56 0.000
lag..language 0.008 1395882.02 0.929
lag..distance 0.003 1502191.41 0.958
1683. Threat of Famine (resolved rating) (codes not ordered)
17 . = Missing data No resolved rating (original code 0)
47 1 = Low threat of famine in the 25-year time period - food is
* reported to be ample or adequate, with no report of famine;
* or famine occurred only in the past; or occasional periods
* of food shortage are reported, but the scare foods are
* reported to be replaced by other available foods; or
* there may be chronic hunger in the absence of the
* conditions immediately below (original scores 2-4)
* (original code 1)
1 2 = original code 1.5
39 3 = Moderate threat of famine - there is no reported
* famine during the 25-year time period, but the
* ethnographer states that there is an ever present
* threat of famine (original code 2)
21 4 = Moderately high threat of famine - one famine occurred
* during the 25-year time period (original code 3)
2 5 = original code 3.5
9 6 = High - more than one famine occurred during the
* 25-year time period (original code 4)
50 . = Missing data
#==19User:Yiyun hung#7B v863 Distance between Co-Wives
- Madhavan, Sangeetha. “Best of Friends and Worst of Enemies: Competition and Collaboration in Polygyny.” Ethnology 41.1 (2002): 69-84.
Distance between Co-Wives
32 . = Missing data
1 0 = One wife, multiple husbands
25 1 = One wife (Atlas M)
59 2 = Co-residence for multiple wives (Atlas PR)
25 3 = One wife resides with husband, others in separate houses
12 4 = One wife resides with husband, others in separate communities
30 5 = Separate housing in compound for every wife (Atlas QS)
2 6 = Separate housing in village for every wife [as might occur, for example, with men's houses, e.g., Otoro]
coef Fstat ddf pvalue VIF
(Intercept) -1.688 13.870 253.099 0.000 NA
language 0.532 6.614 13490.274 0.010 2.558
distance 0.400 8.257 3983.723 0.004 3.120
plow -0.431 2.414 5163.878 0.120 2.158 v243>1*1
bovines 0.645 6.066 99297.719 0.014 3.168 v244==7*1
milk -0.352 2.427 822684.440 0.119 2.293 v245>1*1
pctFemPolyg 1.525 42.498 23.579 0.000 1.214 v872>1*1
household 0.132 6.076 15719.870 0.014 1.673 v67
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.6359925 0.9992364 0.9988732
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.648 199.966 0.201
Wald.on.restrs 1.121 23.584 0.300
NCV 1.846 6452.044 0.174
SW.normal 12.089 30.036 0.002
lag..language 0.038 293920.221 0.846
lag..distance 0.262 22703.770 0.609
. 0 1 2 3 4 5 6
"32" "1" "25" "59" "25" "12" "30" "2"
#==22User:Po Huang#8B Premarital Sex Attitudes- Female
v165
56 . = Missing data
30 1 = Expected
28 2 = Tolerated
22 3 = Mildly disapproved
11 4 = Moderately disapproved
4 5 = Disallowed
35 6 = Strongly disapproved
coef Fstat ddf pvalue VIF
(Intercept) -1.634 0.647 6873.452 0.421 NA
language 0.586 0.711 12004.467 0.399 1.499
distance 0.161 0.360 7356.223 0.548 1.665
moralgods 0.368 7.117 5492.506 0.008 1.325 v238
money 0.401 16.158 417640.519 0.000 1.070 v155
himilexp 0.589 3.569 1669.351 0.059 1.072 v899
ceremony2 -0.262 6.081 24880.876 0.014 1.043 v75
intwar 0.412 3.754 611.448 0.053 1.070 v891
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3224726 0.9931770 0.9963512
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.308 16075.250 0.579
Wald.on.restrs -0.198 5.809 1.000
NCV 0.006 77456.036 0.940
SW.normal 4.634 1087.927 0.032
lag..language 0.023 162631.391 0.879
lag..distance 0.062 20434.264 0.803
> table(depvar)
depvar
1 2 3 4 5 6
30 28 22 11 4
1 2 3 4 5 6
"30" "28" "22" "11" "4" "35" "165"
#==26User:Bpitt#3B DRW ran your model for you (it works)
75. CEREMONIAL ELEMENTS
54 1 = Feasting and/or drinking
10 2 = Exchanges other than food
51 3 = Entertainment
57 4 = Sacrifice other than human
13 5 = Human sacrifice
1 6 = Masochistic behavior
coef Fstat ddf pvalue VIF
(Intercept) -0.9774 0.6679 184.0533 0.4148 NA
language 0.9989 2.6776 5052.1357 0.1018 2.4195
distance 0.4175 1.0235 7419.6145 0.3117 3.0830
gath 0.1479 2.3038 15560.7626 0.1291 1.8698 v203
fish 0.2781 13.8148 47932.3420 0.0002 1.7254 v205
settype -0.1783 8.9097 2266.9606 0.0029 1.8519 v234
bovines 0.6642 5.1766 3594.2578 0.0230 1.7904
Whyte632 0.2702 4.3787 84.5588 0.0394 1.2524
Rohner801 -2.2257 4.2430 11917.6850 0.0394 1.3842
Rohner809 1.0124 6.0838 11723.1537 0.0137 1.4340
Rohner810 -1.1496 3.6890 9381.0426 0.0548 1.2048
rain -0.1983 6.1418 19049.9672 0.0132 2.1401
intwar 0.2857 2.8382 4107.5036 0.0921 1.1737
extwar -0.3576 4.8455 83.2732 0.0305 1.2193
CVrain -0.0019 3.1047 9297.1600 0.0781 1.1225
cultints 0.2425 7.1629 7024.3498 0.0075 2.2186 v232
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.2556559 0.9879144 0.9831996
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 6.602 690.753 0.010
Wald.on.restrs -0.015 516.375 1.000
NCV 17.708 191.454 0.000
SW.normal 23.061 829.773 0.000
lag..language 0.461 18739.854 0.497
lag..distance 0.648 6963.235 0.421
#==27User:Sbakshi#5B v36 Magical protectiveness Sharon
coef Fstat ddf pvalue VIF
(Intercept) 4.493 4.991 7.756200e+03 0.026 NA
language -0.727 1.100 3.533508e+03 0.294 1.877
distance 0.389 1.830 4.291145e+03 0.176 1.335
exogamy -0.115 4.461 5.112835e+05 0.035 1.129
popdens 0.126 4.177 1.292520e+05 0.041 2.473
hunt -0.075 1.766 4.511707e+08 0.184 2.600 v204 <-- not signif
anim -0.111 8.258 2.091650e+07 0.004 1.877
brideprice 0.427 8.326 3.314412e+05 0.004 1.398
settype -0.105 6.164 3.532874e+07 0.013 2.747 v234
superjh 0.156 6.439 3.603546e+05 0.011 1.611
Rohner799 0.000 3.490 3.681897e+07 0.062 1.017
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.1815100 0.9835975 0.9872413
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 1.007 2548476.689 0.316 Ok
Wald.on.restrs -0.009 22.041 1.000 Ok
NCV 0.363 307015.220 0.547 Ok
SW.normal 11.410 4763440.110 0.001 Not a normal distribution
lag..language 0.248 46445.588 0.619 Ok
lag..distance 0.450 13294.213 0.503 Ok
#==28 v709 Social Stratification replalcing v1669 suicide User:jloyola#Results b4 v709 Social Stratification
709. Social Stratification in the Larger Society
94 . = Missing data
24 1 = Lack of significant stratification among free men
31 2 = Differences in wealth and control, but not crystallized
into distinct and hereditary social classes
11 3 = Dual stratification into hereditary elites and commoners
26 4 = Complex stratification into three or more classes/castes
coef Fstat ddf pvalue VIF
(Intercept) -0.549 0.230 472.734 0.632 NA
language -0.342 0.449 712.084 0.503 1.989
distance 0.545 3.660 862.272 0.056 2.242
extwar -0.289 3.851 190.790 0.051 1.288 v892 <-- primary causal variable (Hi/Lo)
war 0.025 2.753 1838.651 0.097 1.211 v1648 <-- looser definition of war (Lo/Hi)
intwar 0.193 1.410 980.629 0.235 1.144 v891 (Hi/Lo, opposite v891)
ecorich 0.288 15.667 3685.372 0.000 1.042
money 0.200 8.158 99281.880 0.004 1.293 v155
attitudetowardsdivorce 0.089 1.630 2830.175 0.202 1.202 <-- marginal but shows marriage/div may be linked to social caste
moralgods 0.165 2.643 1879.531 0.104 1.428
marriagearrangements 0.076 1.435 107.776 0.234 1.229 <-- marginal but shows marriage/div may be linked to social caste
R2:final model R2:IV_ language R2:IV_ distance
0.4893717 0.9200043 0.8712029
Fstat df pvalue
RESET 0.841 367.694 0.360
Wald.on.restrs 1.956 30.161 0.172
NCV 0.020 167.525 0.888
SW.normal 0.060 3107.344 0.806
lag..language 1.789 8121.680 0.181
lag..distance 1.400 1154.080 0.237
#==29User:Judy Zhu#23B Final model v1781 gossip on Adultery
- Needed to revamp latest model and double-check for synonyms. Good work! > ols_stats$restrict_stats
coef Fstat ddf pvalue VIF
(Intercept) 1.703 7.749 1931.717 0.005 NA
language -0.184 0.038 270.368 0.845 2.014
distance -0.042 0.008 212.400 0.929 1.944
exogamy -0.086 2.636 2934.380 0.105 1.109 v72 INTERCOMMUNITY MARRIAGE
AP2 -0.168 13.211 88.427 0.000 1.217 v928 Agricultural Potential 2
annrain 0.090 10.503 243070.724 0.001 1.181 v929 Average Annual Rainfall
lazygoss -0.153 3.003 9784.685 0.083 1.238 v1792 Gossip on laziness
scandalgoss 0.156 4.695 1322.591 0.030 1.191 v1797 Gossip on scandal
sexgoss 0.157 4.669 1348.341 0.031 1.275 v1798 Gossip on sex/sexual joking
wifebeatgoss 0.238 5.737 213058.919 0.017 1.105 v1801 Gossip on wife beating
sexratio -0.213 4.007 24.631 0.056 1.122 sexratio=1+(sccs$v1689>85)+(sccs$v1689>115)
warfight -0.329 5.342 190.242 0.022 1.116 v679 Warfare or Fighting
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.3384030 0.9873908 0.9848081
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 4.035 87.038 0.048
Wald.on.restrs -0.117 6.241 1.000
NCV 17.001 501.862 0.000
SW.normal 15.769 424.557 0.000
lag..language 0.060 115854.652 0.806
lag..distance 0.056 380761.635 0.813
>
>
Earler model superceded R2:final model R2:IV_ language R2:IV_ distance
0.2515565 0.9926848 0.9919984 (Good improvement)
coef Fstat ddf pvalue VIF
(Intercept) 0.726 1.754 240.143 0.187 NA
language -1.137 1.388 3868.279 0.239 2.042
distance 0.267 0.301 5911.655 0.583 1.994
exogamy -0.098 2.964 3796.428 0.085 1.137
AP2 -0.152 11.504 10122.197 0.001 1.190 v928 agricultural potential
annrain 0.093 10.128 2960622.418 0.001 1.163
maleapp 0.042 2.042 28.089 0.164 1.115 v932 Male physical appearance modification
opposedkin 0.218 0.660 20.240 0.426 1.100 <--will take out
boastinggoss 0.324 8.480 15062.759 0.004 1.058 v1782 boastful gossip #not a synonym...you can use this
familygoss 0.098 1.872 35134.640 0.171 1.169 v1787 family gossip
farmgoss -0.224 3.447 985.752 0.064 1.153 v1788 farm gossip
#==30User:Shejazi#4B_v169_Extramarital_Sex
169. EXTRAMARITAL SEX
77 . = Missing data
13 1 = Single standard- both allowed
48 2 = Double standard- husband only
24 3 = Double standard- both forbidden, women punished more
24 4 = Single standard- both condemned
coef Fstat ddf pvalue VIF
(Intercept) 0.743 0.170 7203.561 0.680 NA
language 0.532 0.444 5265.115 0.505 1.542
distance -0.134 0.124 18573.534 0.725 1.453
popdens -0.307 14.264 13859.114 0.000 2.507
ncmallow 0.111 8.648 5762.690 0.003 1.321
settype 0.243 21.631 7791.051 0.000 2.394 v234
segadlboys -0.141 4.628 136.746 0.033 1.067
foodtrade 0.026 6.182 8099.193 0.013 1.286
> ols_stats$r2
R2:final model R2:IV_ language R2:IV_ distance
0.2369880 0.9994173 0.9992919
> ols_stats$restrict_diagnostics
Fstat df pvalue
RESET 0.445 1.883105e+03 0.505
Wald.on.restrs -0.051 4.090000e+00 1.000
NCV 1.155 7.599941e+03 0.282
SW.normal 3.494 1.923974e+04 0.062
lag..language 0.000 7.457297e+08 0.989
lag..distance 0.001 1.201504e+07 0.982
#==00 Incomplete? User talk:Marforid
Duane Marfori not coming to class at all - was emailed
#==00 Incomplete Jie Sun
#==00 Incomplete v1009 W_Sys_labor User:Ahwalker
Amani Labor – Thought I had dropped the class; didn't know I couldn't after week 2; really need to catch up, hoping it's possible
Talk:Human Social Complexity - World Cultures 2010
use: ANTH174_10 in search window to find class notes. - talk/Discussion
- Click article to return to class page
- If you log in (real name) you can open this page write comments or questions: start with the edit tab above. -
- Ariel S not signed in
Find n dependent variable for your term project (can change)
- Sources for codes on the SCCS
- SCCS index of variables
- codebook
- SCCS 2SLS Variable Names
- ================================ Depvar (choosing your depvar the key to your project)
EduMod-78 Evil eye - depvar 1189 Doug White
EduMod-79 Money - depvar 155 Doug White
EduMod-80 Moral gods - depvar 238 Doug White
EduMod-31: Imputation and Regression - depvar 892 extwar Alex
2010 v892 Alex
892. FREQUENCY OF EXTERNAL WAR - ATTACKING (VAR LABEL REVERSED)
[Follows Otterbein 1970: 84, 143-144]
29 . = Missing Data
31 1 = Continual
61 2 = Frequent
65 3 = Infrequent
903. PRESTIGE ASSOCIATED WITH BEING A SOLDIER OR WARRIOR (VAR LABEL REVERSED)
35 . = Missing Data
61 1 = A great deal; important for every male
64 2 = some, not necessary to be a warrior to have influence in the community
26 3 = No special consideration, respect, or distinctions for a man who fights
912. PLUNDER (VAR LABEL REVERSED) (INCLUDING CAPTIVES FOR SLAVES, HOSTAGES, ADOPTION)
18 . = Missing Data
104 1 = Present
64 2 = Absent or not mentioned
237. JURISDICTIONAL HIERARCHY BEYOND LOCAL COMMUNITY
2 . = Missing data
82 1 = No levels (no political authority beyond community)
48 2 = One level (e.g., petty chiefdoms)
23 3 = Two levels (e.g., larger chiefdoms)
19 4 = Three levels (e.g., states)
12 5 = Four levels (e.g., large states)
- This model was not properly constructed. The object is not to take variables that obviously are a part of the same phenomenon (here, a warfare complex) to predict warfare. Regressions coefficients alone are merely predictions, correlations. Regression coefficients cannot be causal if there are omitted variables. The critique here is not with Alex, but with the bad advice he was getting on causal modeling to choose variables that are obviously good PREDICTORS and ignore all others. Ren Feng was new to the problem of causality and did not give good advice. This does not lead to a causal model.
NOT A GOOD CAUSAL MODEL!!!
coef Fstat ddf pvalue VIF
(Intercept) -1.73147478 1.2818655 29.07113 0.26680505 NA
fyll 0.72520771 0.8082993 83.08151 0.37122189 1.997
fydd 0.39374182 2.1956964 1209.98619 0.13865702 2.040
plunder 0.35051229 11.1305381 5297.71900 0.00085503 1.210 v912 more plunder
prestigewarrior 0.21830094 8.5840789 106.80714 0.00414711 1.302 v903 more prestige
FrqAttacked 0.36381942 23.9843690 40.26092 0.00001619 1.220 v893 being attacked (biggest effect)
superjh -0.09865217 6.2782466 1442.75704 0.01233231 1.149 v237 more *state* external warfare
R2:final model R2:IV(distance) R2:IV(language)
0.4331529 0.9820238 0.9411298
Fstat df pvalue
RESET 0.613 398.637 0.434
Wald on restrs. 0.553 230.279 0.458
NCV 6.689 1920.383 0.010
SWnormal 0.001 184417.863 0.978
lagll 1.006 22781.254 0.316
lagdd 0.997 33308.388 0.318
Instead the object is to run a completely general unrestricted model and see ALL OF WHAT PREDICTS the depvar.
The concept of implausibility (Bayesian constraint) and errors in causal logic
Critiquing the model above it is easy to spot the following implausibilities:
Warfare for plunder does not cause warfare. The prestige of warriors does not cause warfare. It is when wars are won that warriors gain prestige.
The following are not implausible (not Bayesian constraints):
More superordinate hierarchy may cause more warfare. Being attacted may be a cause of external war.
So here are some GOOD CAUSAL MODELS for the same project:
coef Fstat ddf pvalue VIF
(Intercept) -1.57119812 1.097643 6958.6180 0.29482042 NA
fyll 1.01580236 1.508930 23487.4806 0.21931468 2.034
fydd 0.31590128 1.248229 25106.4197 0.26390173 2.106
foodscarc 0.05371256 1.902185 296.8695 0.16887209 1.052 <-- remove in final model
FrqAttacked 0.53699757 58.254083 846.1294 0.00000000 1.094
localjh -0.14211018 2.790793 86491.0865 0.09481089 1.037
superjh -0.09934922 6.112151 387.1047 0.01385456 1.048
R2:final model R2:IV(distance) R2:IV(language)
0.3679240 0.9813494 0.9358644
Fstat df pvalue
RESET 0.153 200.595 0.696
Wald on restrs. 0.319 82.154 0.574
NCV 2.722 157.658 0.101
SWnormal 0.434 1157.110 0.510
lagll 1.174 23398.815 0.279
lagdd 0.915 241029.387 0.339
WITHOUT foodscarc
coef Fstat ddf pvalue VIF
(Intercept) -1.56678527 1.082033 5716.0767 0.29828729 NA
fyll 1.09889350 1.753672 6456.6650 0.18546271 2.024
fydd 0.28079555 0.976354 2397.3471 0.32320034 2.089
localjh -0.12610289 2.206010 6659.4806 0.13752113 1.018
FrqAttacked 0.52783026 55.467163 274.6798 0.00000000 1.079
superjh -0.09899757 6.141427 1295.8759 0.01333167 1.046
R2:final model R2:IV(distance) R2:IV(language)
0.3587232 0.9813494 0.9358644
Fstat df pvalue
RESET 0.400 268.426 0.528
Wald on restrs. 0.319 82.154 0.574
NCV 3.277 162.443 0.072
SWnormal 0.755 256.052 0.386
lagll 1.147 20448.377 0.284
EduMod-32: Imputation and Regression 2009 v892 Alex
This model was not properly constructed. The object is not to take variables that obviously are a part of a warfare complex to predict warfare. Regressions coefficients alone are merely predictions, correlations. They cannot be causal if there are omitted variables. The critique here is not with Alex, but with the advice he was getting on causal modeling that one chooses variables that are good PREDICTORS and ignore others. This would not be a causal model. Instead the object is to run a completely general unrestricted model and see ALL OF WHAT PREDICTS the depvar.
coef Fstat ddf pvalue VIF
(Intercept) -0.6890498 0.1985860 28849.9244 0.65586794 NA
fyll 0.7444431 0.7449938 19930.4545 0.38807627 1.949
fydd 0.4430477 2.2509797 991.0665 0.13384833 1.998
plunder 0.5386941 23.7296775 24652.6337 0.00000112 1.074 v912 more plunder
superjh -0.1553373 10.5831194 2048.8790 0.00115980 1.344 more *state* external warfare
himilstate 0.2762169 3.0634474 376.1699 0.08088689 2.228 more *state* military expections
himilexp -0.4533772 8.3637926 293.4747 0.00411395 2.121 *less* prestate military expectns
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.2895756 0.9777535 0.9268318
Fstat df pvalue
RESET 2.225 7841.552 0.136
Wald on restrs. 2.214 121.206 0.139
NCV 0.814 138371.772 0.367
SWnormal 4.940 697.497 0.027
lagll 1.074 5224567.501 0.300
lagdd 0.715 4630.280 0.398
EduMod-55: Imputation and Regression - depvar 661 "FemPartic" Chelsea
- f(665,667)
coef Fstat ddf pvalue VIF
(Intercept) -1.1294534 1.015124 97.28792 0.31617773 NA
fyll 1.0138916 2.164094 104.63368 0.14426893 1.026
fratgrpstr -0.0554429 4.201923 28.74573 0.04959894 1.043 v570
fempower4 0.4600046 31.501713 103.93233 0.00000017 1.115 v660 femCtrlOwnProd
fempower6 0.3410036 17.750886 248.02469 0.00003527 1.131 v662 femSolidaryGrp
R2:final model R2:IV(distance) R2:IV(language)
0.3577129 0.9231332 0.9319455
Fstat df pvalue
RESET 0.513 7.961 0.494 ok
Wald on restrs. 0.771 307.163 0.381 ok
NCV 0.418 73.167 0.520 ok
SWnormal 4.259 22.895 0.051 not a problem
lagll 0.763 7067699.730 0.382 ok
lagdd 0.385 1082.288 0.535 ok
1 2
"62" "83" "145" "FemPartic"
EduMod-56: Imputation and Regression - depvar 577 "mythical founders" Gloria
No support for Decision-Making Bodies: How Most Decisions are Made (v764) (Ross, 1983, 1986). Olga Artemova and Andrey V. Korotayev. 2003. Monopolization of Information and Female Status: A Cross-Cultural Test. Cross-Cultural Research 37; 81.
577. Mythical Founders of the Culture
121 . = Missing Data
21 1 = All male
18 2 = Both sexes, but the role of men more important
19 3 = Both sexes, and the role of both sexes fairly equal
7 4 = Both sexes, but female role more important, or solely female
coef Fstat ddf pvalue VIF
himilexp=(SCCS$v899==1)*1, table(SCCS$v899) 1 2 = 99 68
899. MILITARY EXPECTATIONS I-PRE-STATE (VAR LABEL REVERSED)
[Follows Naroll l966, with modifications by Otterbein 1970]
19 . = Missing Data
99 1 = High, with any one of the following present:
Subjugation of territory or people (909)
Collection of tribute (910)
Land - fields, hunting/fishing territory, pastures (911)
Trophies and honors (including captives for sacrifice) (913)
68 2 = Low, absence of any of the above
(Intercept) 5.627 9.591 13727986.062 0.002 NA
fyll -2.042 5.320 8163802.593 0.021 1.265
fydd 0.818 5.127 155287.270 0.024 1.315
gath -0.302 9.083 199339.557 0.003 1.330 v203
himilexp -0.640 6.835 1150.606 0.009 1.086 v899 (low military)
R2:final model R2:IV(distance) R2:IV(language)
0.2272362 0.9522017 0.9457600
Fstat df pvalue
RESET 0.064 30624.247 0.801
Wald on restrs. 1.296 26.173 0.265
NCV 1.353 9254.226 0.245
SWnormal 5.319 4089.138 0.021
lagll 0.264 13608.506 0.608
lagdd 0.313 30515.845 0.576
1 2 3 4
"21" "18" "19" "7" "65" "mythic founders" V577
himilexp=SCCS$v899
gath=SCCS$v203
v2<-9-gath
v1<-himilexp
table(v1,v2)
v2 (positively correlated)
v1 0 1 2 3 4 5 6 8
1 50 30 12 6 0 0 1 0
2 29 14 9 2 8 4 1 1
The idea being that as gathering does down but himilexp isnt too high (i.e., curvilinear) female founders are most common. The curvilinear idea is that hunting societies are more male oriented, gathering and extensive agriculture more female oriented, then male orientation takes over again with pastoralism, plowing and prestate high military expectation. Here's how your two variables, together, interact with v577:
v3=v2+v1 v4=SCCS$v577 v3 v4 3 4 5 6 7 8 9 10 11 --> less gathering AND less military 1 0 0 0 0 4 2 5 8 1 2 0 0 1 0 1 2 3 8 1 3 0 0 0 0 1 2 5 6 3 4 0 0 0 0 0 0 0 4 3
User:Jasiellt#final_results - EduMod-57: Imputation and Regression - depvar 821 "PctFemContAg" User:Jasiellt
(no negative effect of plow for of wagelabor
R2:final model R2:IV(distance) R2:IV(language)
0.2772143 0.9721914 0.9852138
Fstat df pvalue
RESET 0.638 2940.816 0.424
Wald on restrs. 0.079 37762.980 0.778
NCV 2.049 22512.039 0.152
SWnormal 0.017 11279.771 0.897
lagll 0.472 3926143.155 0.492
lagdd 0.811 2732090.096 0.368
mnb fst v pval VIF
(Intercept) -22.611 2.043 394236.44 0.153 NA
fyll 1.344 10.545 17677.56 0.001 1.652 <- pattern due to language family
fydd 0.360 1.520 21899.71 0.218 2.087
anim -2.679 4.695 82400.00 0.030 1.453 v206
war 0.447 3.124 556.11 0.078 1.010 v1648
popdens -2.260 3.620 497650.81 0.057 1.051 v156
next to last model:
R2:final model R2:IV(distance) R2:IV(language)
0.2810274 0.9715283 0.9843970
Fstat df pvalue "PctFemContAg"
RESET 0.586 12218.688 0.444 <-no vars need log
Wald on restrs. 0.149 1601.024 0.699 <-no var missing
NCV 2.417 13632.135 0.120 <-no bunching
SWnormal 0.006 1981789.848 0.936 <-normal distrib errors
lagll 0.411 377470.146 0.522 <-no lang autocor
lagdd 0.832 1907553.379 0.362 <-no dist autocor
mnb fst v pval VIF
(Intercept) -30.645 2.942 417585.374 0.086 NA
fyll 1.399 10.982 75919.214 0.001 1.717
fydd 0.399 1.854 354805.414 0.173 2.099
anim -2.750 4.911 965958.604 0.027 1.464 v206
war 0.446 2.968 83.698 0.089 1.010 v1648
wagelabor 2.332 1.151 1114.314 0.284 1.200 <-- drop for a final xR model
popdens -2.356 3.912 48757.127 0.048 1.052 v156
Bryan EduMod-58: Imputation and Regression - 654 "spiritAggression" Bryan
Click image for map
dropped 649 "TheoriesofFate" (too few principal cases)
coef Fstat ddf pval VIF
(Intercept) 0.062 0.004 89969.4 0.952 NA
fyll 0.341 0.723 64930.3 0.395 2.085
fydd 0.686 9.805 668174.8 0.002 1.946
cmplx151 -0.697 6.605 15401863.2 0.010 3.614 v151 agriculture
cmplx157 0.447 3.450 3587041722.5 0.063 1.480 v157 political integration
witchcraft -0.137 3.733 5606994.0 0.053 1.212 v656
cultints 0.145 4.612 4960093.7 0.032 3.852 v232
R2:final model R2:IV(distance) R2:IV(language)
0.2389135 0.9841678 0.9854289
Fstat df pvalue
RESET 3.846 129411.685 0.050
Wald on restrs. 1.193 133.415 0.277
NCV 15.865 842933.736 0.000
SWnormal 6.284 1482647.509 0.012
lagll 1.128 16799.868 0.288
lagdd 1.049 421810.832 0.306
1 2 3 4
"2" "18" "37" "74" "131" "spiritAggression"
Prior model with nonsignificant variables
R2:final model R2:IV(distance) R2:IV(language)
0.2660482 0.9847517 0.9857011
Fstat df pvalue
RESET 2.430 15178.180 0.119
Wald on restrs. 0.305 88.894 0.582
NCV 12.355 84.525 0.001
SWnormal 3.782 34.868 0.060
lagll 1.202 139157.092 0.273
lagdd 1.332 33411.814 0.249
mnb fst v pval VIF
(Intercept) 0.027 0.001 4511343.244 0.979 NA
fyll 0.268 0.424 20235.269 0.515 2.241
fydd 0.719 10.687 39667.103 0.001 1.995
cmplx151 -0.688 6.476 19459.250 0.011 3.647 v151 agriculture
cmplx157 0.527 4.725 24167.416 0.030 1.525 v157 political integration
witchcraft -0.136 3.722 69816.001 0.054 1.219 v656
cultints 0.134 3.976 15002769.224 0.046 3.903 v232
exogamy -0.078 2.325 2097.789 0.127 1.057 v72
sexratio 0.167 2.109 43.887 0.154 1.135 v1689 1+(SCCS$v1689>85)+(SCCS$v1689>115)
1 2 3 4
"2" "18" "37" "74" "131" "spiritAggression"
EduMod-59: Imputation and Regression - 155<=17 155 Money TJ
coef Fstat ddf pvalue VIF
(Intercept) 0.7126272 2.676457 1.447403e+03 0.10205950 NA
fyll -1.2333233 13.725209 1.504065e+02 0.00029666 4.078
fydd 1.1050277 25.514472 6.965334e+03 0.00000045 3.412
fratgrpstr 0.2502255 6.773247 9.276634e+00 0.02794422 1.954 v570
cereals -0.2937851 2.384662 2.749067e+04 0.12254291 1.406 v233==6)*1
milk -0.4173179 3.437748 2.332840e+02 0.06498469 1.598 v245>1
popdens 0.3148705 22.526657 1.362230e+05 0.00000207 1.652 v156
superjh 0.4152110 25.214029 2.335964e+03 0.00000055 1.642 v237
R2:final model R2:IV(distance) R2:IV(language)
0.4756762 0.9900347 0.9950832
Fstat df pvalue
RESET 1.078 177.927 0.300
Wald on restrs. 8.587 20.142 0.008
NCV 3.754 57.135 0.058
SWnormal 0.761 38.455 0.389
lagll 1.508 148535.942 0.219
lagdd 2.036 76007.113 0.154
1 2 3 4 5
"77" "14" "43" "27" "25" "186" "money" v155
Ariana3 - EduMod-60: Imputation and Regression - depvar 167 Lo-"Pre_MaritalSex" Ariana
R2:final model R2:IV(distance) R2:IV(language)
0.1707546 1.0000000 1.0000000
Fstat df pvalue
RESET 0.117 2.192447e+03 0.733
Wald on restrs. 0.062 2.491630e+03 0.803
NCV 1.122 3.998436e+03 0.290
SWnormal 14.776 4.464219e+05 0.000
lagll 0.000 8.073869e+66 1.000
lagdd 0.000 1.475778e+61 1.000
mnb fst v pval VIF
(Intercept) -1.238 0.727 14543.124 0.394 NA
fyll 1.710 4.775 37471.498 0.029 1.608
fydd -0.255 0.408 2501597.373 0.523 1.486
nomarrpaymt -0.585 5.569 4921094.865 0.018 1.050 v208==6)*1
plow 0.673 3.895 491306.444 0.048 1.435 v243>1)*1
superjh 0.174 2.352 1081.152 0.125 1.555 v237
1 2 3 4
"51" "19" "16" "23" "109" "Lo_premarital_sex"
EduMod-64: Imputation and Regression - depvar 678- "famine" Leon
R2:final model R2:IV(distance) R2:IV(language)
0.1370943 0.9462151 0.9131939
Fstat df pvalue
RESET 0.455 172.028 0.501
Wald on restrs. 2.423 28.531 0.131
NCV 2.371 226.434 0.125
SWnormal 2.435 110.038 0.122
lagll 0.539 17178.088 0.463
lagdd 1.437 4483.717 0.231
mnb fst v pval VIF
(Intercept) -0.204 0.022 3180.183 0.881 NA
fyll 0.731 0.784 389.353 0.377 1.361
fydd 0.564 2.266 180.419 0.134 1.419
plunder -0.373 5.982 18.605 0.025 1.065 v912
tree 0.424 2.680 1199770.534 0.102 1.028 v233==4)*1
moralgods 0.165 8.419 3058.470 0.004 1.050 v238
himilexp -0.348 6.702 7815.343 0.010 1.083 v899==1)+1
1 2 3 4
"47" "62" "26" "3" "138"
678. Food Stress or Hunger
48 . = Missing data
47 1 = food constant
62 2 = occasional hunger or famine
26 3 = periodic or chronic hunger
3 4 = starvation or evidence of protein deficiency
EduMod-65: Imputation and Regression - depvar 90 "police" Nick
coef Fstat ddf pvalue VIF
(Intercept) 2.11549231 3.847829 9.798975e+02 0.05009324 NA
fyll -0.90891797 3.283457 6.787713e+03 0.07002553 2.244 <- negative on language
fydd 0.49596925 4.520688 1.917518e+07 0.03348737 2.034
roots -0.47381415 4.236001 1.149779e+08 0.03957528 1.215 v233==5
plow 0.95749750 9.920426 4.269428e+04 0.00163567 1.760 v243>1)*1
hunt -0.18504478 9.023295 9.428143e+03 0.00267260 1.571 v204
fish -0.12614962 5.536005 3.104774e+04 0.01863526 1.318 v203
anim -0.28010379 19.939583 1.873217e+02 0.00001379 2.028 v206 *** miscoded in *.xls
foodtrade 0.01873274 4.248579 5.189531e+03 0.03933290 1.199 v819 *** miscoded in *.xls
exogamy -0.19025095 6.649897 1.259677e+04 0.00992739 1.067 v72
superjh 0.56960390 36.268465 1.414129e+02 0.00000001 1.773 v237
moralgods 0.21863101 5.333031 5.491904e+01 0.02471200 1.520 v238
fempower 0.28426556 1.594840 2.331993e+02 0.20789726 1.085 v663
R2:final model R2:IV(distance) R2:IV(language)
0.5865162 0.9811534 0.9772505 <-- 59% very high r2)
Fstat df pvalue
RESET 13.974 189.742 0.000 <- which variable to log?
Wald on restrs. 0.084 5328.628 0.773
NCV 8.438 53.792 0.005
SWnormal 10.732 1317.135 0.001
lagll 1.489 1279956.371 0.222
lagdd 2.543 32429.985 0.111
EduMod-66: Imputation and Regression - depvar 1721 "Wealthy" User:Dante Anton
1721. Number of rich people (wealthy)
88 . = missing data
27 1 = absence of rich (original code 10)
41 2 = presence of rich, no information on numbers
(original code 20)
27 3 = few rich (original code 21)
3 4 = many rich (original code 22)
Remember 1721. Number of rich is positively correlated with 1723. Number of poor
98 . = missing data
32 1 = absence of poor (original code 10)
41 2 = presence of poor, no information on percentage
(original code 20)
8 3 = few poor (original code 21)
7 4 = many poor (original code 22)
B| DRW added gath+pathstress+ to your xR
coef Fstat ddf pvalue VIF (Intercept) 3.70027678 8.212032 55547.4555 0.00416291 NA fyll -0.92980374 1.613728 30744.7629 0.20397819 1.773 fydd 0.36747778 1.444957 323049.3831 0.22933973 1.845 gath -0.13701038 7.049799 213258.7531 0.00792800 1.409 v203 pathstress -0.06041091 7.514747 1161.8502 0.00621347 1.600 marrcaptives 0.19863236 6.136059 244.8343 0.01392135 1.214 v870
pigs -0.63360969 9.000588 12076.8837 0.00270443 1.107 v244==2
popdens 0.21413882 14.208924 4031.1441 0.00016593 1.672
famsize -0.13279842 5.340838 2365029.6096 0.02083154 1.161
R2:final model R2:IV(distance) R2:IV(language)
0.3877335 0.9563904 0.9529405
Fstat df pvalue
RESET 0.034 2182972.020 0.854
Wald on restrs. 2.238 24.395 0.147
NCV 1.174 78760.666 0.279
SWnormal 0.394 13827.987 0.530
lagll 0.524 606647.512 0.469
lagdd 1.143 29325832.033 0.285
1 2 3 4
"27" "41" "27" "3" "98" "Wealthy"
original results (I led you not to pick the somewhat less significant variables in xRU
R2:final model R2:IV(distance) R2:IV(language)
0.2848861 0.9585911 0.9491467
coef Fstat ddf pvalue VIF
(Intercept) 2.9657712 4.801780 14287.1793 0.02844644 NA
fyll -1.4732094 4.035367 6639.1025 0.04459640 1.543
fydd 0.7827075 7.231401 5444.6234 0.00718575 1.451
marrcaptives 0.1827836 4.897914 231.5023 0.02786530 1.110 v870
pigs -0.4624476 4.300054 67746.5024 0.03811493 1.082 v244=2
popdens 0.2371860 22.338894 72624.3200 0.00000229 1.150 v156
famsize -0.1244347 4.178108 705274.7293 0.04094976 1.138 v68 1-12 --> v80 1-5
R2:final model R2:IV(distance) R2:IV(language)
0.2836994 0.9573003 0.9491625
Fstat df pvalue
RESET 0.378 31403.692 0.539
Wald on restrs. 2.580 88.744 0.112
NCV 0.644 1211.747 0.422
SWnormal 2.498 8304.591 0.114
lagll 0.357 38563.840 0.550
lagdd 0.763 16492.965 0.382
1 2 3 4
"27" "41" "27" "3" "98" "Wealthy"
EduMod-67: Imputation and Regression - depvar 1189 "EvilEye" Evan
These results are the sum of v1189 and v1188, better than 1189 alone John M. Roberts: Belief in the evil eye in world perspective.
More digits
coef Fstat ddf pvalue VIF
(Intercept) -0.05521461 0.00876799 57010.7150 0.92539741 NA
fydd 0.69733746 27.64702795 213625.8208 0.00000015 2.201
money 0.25460839 5.01792309 394.3831 0.02564261 1.474 v155
plow 0.25585125 0.29399114 375311.6049 0.58767451 1.614
milk 1.01300879 4.66363729 1559339.3148 0.03080801 2.324 v245>1
superjh -0.17080860 1.32437400 10937.6676 0.24983372 1.693
moralgods 0.37406339 6.47982766 4541.7413 0.01094333 1.490 v238
R2:final model R2:IV(distance) R2:IV(language)
0.4846324 0.9936442 0.9934272
Fstat df pvalue
RESET 5.276 1.279510e+02 0.023
Wald on restrs. 5.990 7.657300e+01 0.017
NCV 0.801 1.429979e+03 0.371
SWnormal 19.370 6.853164e+03 0.000
lagll 0.739 1.626158e+09 0.390
lagdd 1.402 1.997999e+04 0.236
1 2 3 4 6 7 8 9
"1" "45" "46" "27" "8" "13" "16" "30" "186" "EvilEye"
- Fewer digits
These results are the sum of v1189 and v1188, better than 1189 alone John M. Roberts: Belief in the evil eye in world perspective. R2:final model R2:IV(distance) R2:IV(language) 0.4844030 0.9933850 0.9932345 Fstat df pvalue RESET 4.206 138.222 0.042 Wald on restrs. 5.065 31.389 0.032 NCV 0.516 691.696 0.473 SWnormal 18.496 1962.256 0.000 lagll 0.717 445841.066 0.397 lagdd 1.543 1245266.681 0.214 mnb fst v pval VIF (Intercept) -0.083 0.020 45208.41 0.888 NA fydd 0.706 28.172 30088.64 0.000 2.206 money 0.252 5.026 1287.20 0.025 1.470 v155 plow 0.254 0.288 24818.18 0.592 1.622 v243>1)+1 milk 0.962 4.088 22091.55 0.043 2.377 v245>1 superjh -0.174 1.384 43087.53 0.239 1.691 v237 moralgods 0.379 6.500 71280.88 0.011 1.543 v238 1 2 3 4 6 7 8 9 "1" "45" "46" "27" "8" "13" "16" "30" "186" "EvilEye" [1] "number of imputations nimp=" "3"
DRW test to show that Social Stratif not significant
R2:final model R2:IV(distance) R2:IV(language)
0.3904385 0.9915090 0.9868068
Fstat df pvalue
RESET 7.290 219.971 0.007
Wald on restrs. 4.915 582.501 0.027
NCV 0.445 4804.749 0.505
SWnormal 29.162 60553.691 0.000
lagll 1.087 5962.102 0.297
lagdd 1.429 20336.127 0.232
mnb fst v pval VIF
(Intercept) -0.146 1.410 144.223 0.237 NA
fyll -0.337 0.866 4197.695 0.352 2.226
fydd 1.014 33.777 77909.272 0.000 2.493
wealthy 0.006 0.023 18.693 0.882 1.148 v1721
socstrat 0.023 0.520 149.514 0.472 2.531 v158
money 0.047 4.381 117.345 0.039 1.450 v155 <== v17
superjh -0.046 1.535 176.803 0.217 2.565 v237
moralgods 0.073 6.493 1016.286 0.011 1.432 v238
0 1
"119" "67" "186" "EvilEye"
Revised drw model shows effect of witchcraft causing illness
No significant social stratification variables, see John M. Roberts R2:final model R2:IV(distance) R2:IV(language) 0.4905394 0.9941474 0.9940997 Fstat df pvalue RESET 5.078 6529.299 0.024 Wald on restrs. 6.443 452.700 0.011 NCV 0.274 697.598 0.601 SWnormal 8.736 25.873 0.007 lagll 0.475 934.261 0.491 lagdd 1.766 1785.132 0.184 mnb fst v pval VIF (Intercept) -0.279 0.132 21963.262 0.716 NA fyll -0.143 0.330 183695.982 0.565 2.198 fydd 0.777 25.208 173.056 0.000 2.948 witchcraft 0.370 4.082 6.332 0.087 1.402 v882 money 0.190 5.116 600.141 0.024 1.206 v155 <== v17 moralgods 0.343 8.132 5910.997 0.004 1.466 v238 1 2 3 4 5 6 7 8 "1" "45" "46" "27" "8" "13" "16" "30" "186" "EvilEye" 1188
EduMod-70: Imputation and Regression - depvar 1472 "FormalEd" Jeff
R2:final model R2:IV(distance) R2:IV(language)
0.3265406 0.9904255 0.9964702
Fstat df pvalue
RESET 0.699 1157.379 0.403
Wald on restrs. 12.701 8.586 0.007
NCV 4.099 10394.925 0.043
SWnormal 7.884 14451.870 0.005
lagll 0.017 2064801.732 0.895
lagdd 0.065 1115384.118 0.799
mnb fst v pval VIF
(Intercept) -0.714 1.815 13349.528 0.178 NA
fyll 0.196 0.305 2012939.145 0.581 1.918
fydd 0.749 9.785 266180.109 0.002 1.880
wagelabor 0.434 8.478 660.954 0.004 1.144 v1732
1 2 3 4 5
"50" "6" "21" "5" "8" "90" "FormalEd"
EduMod Mac-10: Imputation and Regression depvar 666 "InterPerViol" Ari
coef Fstat ddf pvalue VIF
(Intercept) 5.44607393 7.512493 488.80480 0.00635163 NA
fyll -3.52846963 7.861037 668.27106 0.00519774 1.245
fydd 1.11070801 9.985387 74.36364 0.00228229 1.246
superjh 0.08662230 5.740095 206.46742 0.01747440 1.104
moralgods -0.08895484 6.532027 88.91742 0.01229306 1.155
agrlateboy 0.06130542 10.029779 214.06730 0.00176504 1.048
R2:final model R2:IV(distance) R2:IV(language)
0.2328954 0.9565326 0.8411570
Fstat df pvalue
RESET 0.213 36723.103 0.645
Wald on restrs. 0.760 5.443 0.420
NCV 6.791 6542.643 0.009
SWnormal 22.081 1683.694 0.000
lagll 0.684 22583.730 0.408
lagdd 0.702 592.968 0.402
1 2
"43" "88" "131" "InterPerViol"
R2:final model R2:IV(distance) R2:IV(language)
0.2361495 0.9424094 0.8330983
Fstat df pvalue
RESET 0.135 4672.544 0.713
Wald on restrs. 0.361 19648.337 0.548 no missing variables
NCV 5.497 108.692 0.021 clustered error terms
SWnormal 20.461 396.459 0.000
lagll 0.753 185780.389 0.386
lagdd 1.245 16847.837 0.264
mnb fst v pval VIF
(Intercept) 5.406 7.596 248654.319 0.006 NA
fyll -3.636 8.524 1168199.643 0.004 1.250
fydd 1.225 12.538 274.144 0.000 1.253
superjh 0.086 5.742 237.852 0.017 1.088 v237
moralgods -0.083 6.393 8577.316 0.011 1.141 v238
agrlateboy 0.066 11.803 13765.204 0.001 1.038 v300
1 2
"43" "88" "131" "InterPerViol"
Edu-Mod 2009 THE INDIVIDUAL STUDIES
Based on variables in the SCCS codebook, these are results from Human Social Complexity and World Cultures 2009-2010
- Doug 13:48, 29 November 2009 (PST): In selecting final statistical results, I made suggestions where R2 < 15 for new variables or new literature, and in a number of cases took non-significant variable out of the model to produce the Restricted Model. I also found that if one fy (ll or dd) variable case close to significant e.g. <.22 or so, taking the other out (or trying both singly) sometimes produced a significant result for the remaining effect. Distance fydd were never significantly negative, so all spatial clustering was positive. fyll was sometimes negative, indicating a feature that is not done with other societies in the same language families. EM-15, 18 and 22 are the best examples of this: violence, wifebeating and money, with fydd significant in the latter two. There is a non significant tendency for fyll to be negative with warfight (EM-6) as well. Willingness for authorship is indicated by a leading + (does not affect the grade for this scientific writing course).
EduMod-6: (your dep var here) Imputation and Regression depvar 679 warfight Kat
(Intercept) 1.072 54.266 3038.376 0.000 NA
pigs 0.157 2.028 21877.912 0.154 1.338 v244==2
milk 0.099 1.060 6924.022 0.303 1.325 v245>1
popdens 0.010 0.148 30242.760 0.700 1.293 v156
agrlateboy 0.054 6.140 613.970 0.013 1.160 v300
war 0.021 11.647 140.761 0.001 1.122 v1648 war
R2:final model IV(distance) IV(language)
0.2086215 0.9295857 0.9397868
EduMod-10: (your dep var here) Imputation and Regression depvar 17==>155 money Deborah
(Intercept) 0.086 0.019 627699.607 0.891 NA
fyll -0.183 0.289 302292.007 0.591 2.875
fydd 1.003 16.589 317495.111 0.000 3.057
foodtrade 0.031 8.712 8447742.248 0.003 1.105 v819
ecorich 0.095 1.238 6588985.817 0.266 1.121 v857
war -0.020 1.591 372.138 0.208 1.019 v1648 war
> r2
R2:final model IV(distance) IV(language)
0.2350829 0.9905900 0.9949863
EduMod-11: (your dep var here) Imputation and Regression depvar 667 rape Amanda
2SLS model for rape = SCCS$v667
coef Fstat ddf pvalue VIF
(Intercept) 1.204 3.717 3743.789 0.054 NA
fydd -0.514 1.383 1078.445 0.240 1.158
freintovio 0.325 7.306 212.412 0.007 1.188 v1710
ideomaltough 0.317 5.144 241.921 0.024 1.337 v664
> r2
R2:final model IV(distance) IV(language)
0.2258225 0.9461911 0.9609407
+EduMod-12: (your dep var here) Imputation and Regression depvar 1675 "soc_aggr"=Socially Organized Homicide Villy
with money
coef Fstat ddf pvalue VIF
(Intercept) -6.191 0.553 19191.517 0.457 NA
fyll -1.573 2.972 8780.817 0.085 1.052
money 0.288 1.750 134319.883 0.186 1.037 v155 <== v17
dateobs 0.007 3.496 249175.045 0.062 1.129 v838
war 0.288 32.758 14518.659 0.0000001 1.163 v1648
R2:final model R2:IV(distance) R2:IV(language)
0.4279652 0.9724051 0.9459899
without money
1675. Socially Organized Homicide
(Intercept) -7.055 0.728 507758.22 0.394 NA
fyll -1.445 2.575 66950.15 0.109 1.033
dateobs 0.008 3.950 299561.61 0.047 1.118 v838
war 0.299 35.898 333261.58 0.0000001 1.138 v1648?
R2:final model IV(distance) IV(language)
0.4117645 0.9767617 0.9496814
Fstat df pvalue
RESET 0.041 257858.640 0.839 <-- good
Wald on restrs. 2.476 4507.719 0.116 <-- good
NCV 0.207 166727.820 0.649 <-- good
SWnormal 0.280 428886.808 0.597 <-- good
lagll 0.083 799243.944 0.773 <-- good
lagdd 0.123 91981.181 0.726 <-- good
EduMod-13: (your dep var here) Imputation and Regression depvar 678 foodstress Lawrence
(Intercept) 0.221 0.026 3978.758 0.872 NA
fyll 0.210 0.071 3033.455 0.790 1.305
fydd 0.546 2.377 18249.668 0.123 1.369
ecorich -0.112 5.160 20834.907 0.023 1.068 v857
moralgods 0.127 4.754 1289.122 0.029 1.067 v238
himilexp -0.320 5.698 3288.341 0.017 1.068 v899
weatherpest 0.100 2.868 267.651 0.092 1.056 v1684
agrlateboy 0.061 3.126 1758.514 0.077 1.054 v300
R2:final model IV(distance) IV(language)
0.1634943 0.9616244 0.9408496
Fstat df pvalue
RESET 0.184 5783.607 0.668
Wald on restrs. 13.422 79.897 0.000
NCV 1.763 533.089 0.185
SWnormal 3.556 540.919 0.060
lagll 0.276 418888.582 0.599
lagdd 1.271 352082.077 0.260
EduMod-15: (your dep var here) Imputation and Regression depvar 1772 violence Ambrose
(Intercept) 17.369 7.882 178363.115 0.005 NA fyll -2.892 9.220 13164.456 0.002 1.412 fydd -0.047 0.014 5477.757 0.907 1.420 dateobs 0.003 1.069 11824092.032 0.301 1.114 v838 exogamy -0.657 10.090 27556751.609 0.001 1.103 v72 localjh -1.072 5.451 2213610.897 0.020 1.400 v236 nuclearfam -1.433 5.113 28887323.570 0.024 1.336 v872 mrktxchngout 2.959 13.642 341333.927 0.000 1.117 v1734
without 838
R2:final model IV(distance) IV(language)
0.4058828 0.9713896 0.9849845
coef Fstat ddf pvalue VIF
(Intercept) 22.3500 33.3834 267826.04 0.0000
fyll -2.9668 13.2874 83500.31 0.0003
exogamy -0.6177 9.1918 80697.71 0.0024 v72
localjh -1.0264 5.2649 4280277.45 0.0218 v236
nuclearfam -1.5394 6.1680 777166.86 0.0130 v872
mrktxchngout 3.0716 15.4811 5543553.80 0.0001 v1734
R2:final model IV(distance) IV(language)
0.3967628 0.9700511 0.9853158
Fstat df pvalue
RESET 0.006 7.625879e+08 0.939
Wald on restrs. 0.177 1.947470e+02 0.675
NCV 0.031 7.804293e+05 0.860
SWnormal 0.056 4.739346e+06 0.812
lagll 0.002 2.460259e+07 0.965
lagdd 0.097 6.294796e+05 0.756
EduMod-16: (your dep var here) Imputation and Regression depvar 1746 react2viol --> v528 control as depvar Stephen
last try: FINAL RESULT for v528 control as depvar
coef Fstat ddf pvalue VIF
(Intercept) 15.187 5.802 42540.20 0.016 NA
fyll -3.512 6.536 60113.32 0.011 1.132
fydd 0.945 24.796 337276.41 0.000 1.135
importmothbandg 0.247 4.519 575.72 0.034 1.009 v988 Importance of Mothers boy and girl
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.2345002 0.9775479 0.8206093
Exclusive care by mother --> leads to the Highest level of control of Boys&Girls (no wonder matriliny is relatively rare).
first try: No prediction. Second try changed to: depvar 492 warmth of caretakers
(Intercept) 3.469 0.410 11593.819 0.522 NA
fyll 0.599 0.462 6408.863 0.497 1.422
fydd 0.185 0.263 42361.553 0.608 1.475
money -0.205 4.616 34891.693 0.032 1.091 v155 <== v17
prinrelcaretakeryoung -0.097 2.379 207.965 0.125 1.051 v56
agrlateboy -0.174 5.391 330.049 0.021 1.025 v300
> r2
R2:final model IV(distance) IV(language)
0.1344706 0.9532665 0.9528267
Not very signif results: Like Ryan, below, you might consult additional sources to get hypotheses, such as http://sppsp.highwire.org/cgi/content/abstract/32/11/1559, Group Morality and Intergroup Relations: Cross-Cultural and Experimental Evidence. Otherwise I suggest substituting other dependent variables having to do with related to authority, obedience, submission, parents.
Third try: took v516 indifference as depvar.
coef Fstat ddf pvalue VIF
(Intercept) 6.819 6.806 10673.695 0.009 NA
fyll -2.737 7.015 8558.934 0.008 1.306
fydd 0.916 6.483 2653.134 0.011 1.266
money 0.123 1.864 54410.880 0.172 1.085 v155=17
prinrelcaretakeryoung 0.041 0.493 928.464 0.483 1.050 v56 NS
segadlboys 0.217 3.433 113.281 0.067 1.147 242
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.1351416 0.9295218 0.8899221
Then dropped prinrelcaretakeryoung - Now something to write about ALTHO Money is not significant. v516 indifference as depvar
coef Fstat ddf pvalue VIF
(Intercept) 7.298 8.405 10641.742 0.004 NA
fyll -2.818 7.623 11366.074 0.006 1.290
fydd 0.898 6.287 2089.039 0.012 1.256
money 0.123 1.885 57921.871 0.170 1.083 v155=17
segadlboys 0.211 3.337 118.250 0.070 1.139 v242
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.1329234 0.9295218 0.8899221
EduMod-17: (your dep var here) Imputation and Regression depvar 122 games of strategy Nathan
coef Fstat ddf pvalue VIF
(Intercept) -0.1963683 10.697370 3149943.9 0.01107289 NA
fyll -0.3573787 1.925958 291090.7 0.1652024 2.748786
fydd 0.9878472 32.366629 281700.2 0.00000001277 2.669258
stratif 0.1206975 36.494753 2090781.4 0.00000000153 1.111105 v270
> r2
R2:final model IV(distance) IV(language)
0.4863288 0.9871563 0.9945682
EduMod-18: (your dep var here) Imputation and Regression depvar 754 wifebeating Hiu Kwan
corrected with corpun but you have a tendency not to copy your programs so as to get your final results which are even better. You probably work very quickly and thus make errors of incomplete checking and incomplete documentation. Check your edumod site where I made corrections 1, 2, 3. Great results nonetheless.
http://bit.ly/b69wcn possibly good r2=0.4711501
coef Fstat ddf pvalue VIF
(Intercept) 8.199 29.855 1249.578 0.000 NA
fyll -4.178 24.346 2390.085 0.000 1.325
fydd 0.831 13.458 5371.344 0.000 1.183
roots -0.311 7.471 7831.241 0.006 1.477 v233==5
cereals -0.320 10.647 4346.565 0.001 1.691 v233==6
livingnew -0.082 4.928 77.032 0.029 1.162 v749
corpun 0.039 3.518 237.181 0.062 1.069 v453
hunt -0.101 12.853 1478.633 0.000 1.288 v204
R2:final model R2:IV(distance) R2:IV(language)
0.4711501 0.9825244 0.9503747
Fstat df pvalue
RESET 6.585 562.199 0.011
Wald on restrs. 5.108 103.131 0.026
NCV 11.251 8459.005 0.001
SWnormal 6.086 1424.976 0.014
lagll 0.184 622176.454 0.668
lagdd 0.197 179619.090 0.657
http://bit.ly/by4jCA yet another model R2 = 0.5360205 but lots of nonsignificant variables
needs LIVING NEW! to match your powerpoint
see http://bit.ly/9Ky7cy - WRONG v1166 for name techspe!!!
coef Fstat ddf pvalue VIF
(Intercept) 7.539 27.524 146123.065 0.000 NA
fyll -3.941 22.047 110249.565 0.000 1.280
fydd 0.833 12.719 10380.770 0.000 1.192
roots -0.322 7.651 436920.034 0.006 1.479 v233==5
cereals -0.349 12.225 65285.211 0.000 1.695 v233==6
corpun 0.043 4.488 841.592 0.034 1.067 v453
techspe 0.267 2.796 22359.257 0.095 1.141 v1166 divorce!!!
hunt -0.095 11.363 52107.437 0.001 1.275 v204
R2:final model R2:IV(distance) R2:IV(language)
0.4353688 0.9831351 0.9538725
> incorrect in not adding corpun correctly specified as v453 not v153
- Changed from 752 polygyny GREAT RESULT
see http://bit.ly/cKUR1U - RIGHT!!
coef Fstat ddf pvalue VIF
(Intercept) 8.375 30.828 3281.511 0.000 NA
fyll -4.218 24.327 5069.418 0.000 1.331
fydd 0.766 11.177 54241.457 0.001 1.196
roots -0.323 7.525 3299.034 0.006 1.494 v233==5
cereals -0.385 13.531 7519.183 0.000 1.877 v233==6
livingnew -0.070 3.937 207.998 0.049 1.158 v <--wasnt in final edu-model. undefined. 1166?
techspecial 0.068 4.645 14160.229 0.031 1.379 v153
hunt -0.095 11.334 7697.674 0.001 1.307 v204
R2:final model IV(distance) IV(language)
0.4506234 0.9847481 0.9552661
coef Fstat ddf pvalue VIF
(Intercept) 7.19267 23.44421 45407.72 0.00000
fyll -3.62064 17.63661 46486.63 0.00003
fydd 0.82320 11.81269 155887.13 0.00059
roots -0.31280 6.76358 818899.57 0.00930 v233==5
cereals -0.33612 10.67803 1164233.14 0.00108 v233==6
techspe 0.28669 3.08090 1495695.42 0.07922 v1166
hunt -0.09536 10.98199 461251.41 0.00092 v204
(missing “livingnew”)
> r2
0.5259626 0.9819218 0.9482473
After Fixing it... Final Results
All P values are <.05
> bbb
coef Fstat ddf pvalue VIF
(Intercept) 8.466 29.774 1014.913 0.000 NA
fyll -4.251 23.546 1383.676 0.000 1.365
fydd 0.762 11.180 162554.409 0.001 1.201
roots -0.312 7.332 24569.422 0.007 1.492 v233==5
cereals -0.367 12.335 8144.970 0.000 1.893
livingnew -0.077 4.872 350.874 0.028 1.204
techspe 0.061 3.833 35114.992 0.050 1.390
hunt -0.097 11.895 11793.340 0.001 1.311 v204
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.4566717 0.9843880 0.9545625
My Independent Variables
fyll
fydd
roots = 233. MAJOR CROP TYPE (Roots or tubers)
cereals = 233. MAJOR CROP TYPE (Cereal grains)
livingnew = 749. Living Arrangements for Newlyweds
techspe = 153. SCALE 5- TECHNOLOGICAL SPECIALIZATION
hunt = 204. DEPENDENCE ON HUNTING
EduMod-20: (your dep var here) Imputation and Regression depvar 169 extramarital sex Bui
Best - Adding fyll gives the best R2.
Fstat df pvalue
RESET 0.328 16443.01 0.567
Wald on restrs. 1.768 125.57 0.186
NCV 0.009 63156.48 0.925
SWnormal 3.909 12544.60 0.048
lagll 0.217 382228.95 0.641
lagdd 0.873 1725110.81 0.350
R2:final model IV(distance) IV(language)
0.1553265 0.9400495 0.9071833 <- R2 is 16%
coef Fstat ddf pvalue VIF
(Intercept) -2.069 1.001 102335.978 0.317 NA
fyll 1.711 4.941 79634.792 0.026 1.218
popdens -0.212 7.264 254524.622 0.007 2.182 v156 <- negative corr coeff
pctFemPolyg -0.006 3.458 3786.085 0.063 1.062 v1684
settype 0.141 8.037 58060.619 0.005 2.012 v234
superjh 0.161 2.920 6144.932 0.088 1.550 v237
R2:final model IV(distance) IV(language)
0.1587851 0.9446204 0.9089112
(Intercept) 0.466 0.211 29568.675 0.646 NA
fydd 0.745 4.241 47025.594 0.039 1.108
popdens -0.251 10.002 88960.899 0.002 2.153 v156 <- negative corr coeff
pctFemPolyg -0.006 2.684 9541.567 0.101 1.077 v1684
settype 0.168 11.328 50551.706 0.001 1.968 v234
superjh 0.129 1.943 8816.497 0.163 1.465 v237
R2:final model IV(distance) IV(language)
0.1358130 0.9437086 0.9075315
coef Fstat ddf pvalue VIF
(Intercept) 2.478 103.902 67437.152 0.000 NA
popdens -0.235 9.023 26642.713 0.003 2.125 v156 <- negative corr coeff
pctFemPolyg -0.007 4.274 6242.394 0.039 1.032 v1684
settype 0.159 10.631 425869.721 0.001 1.947 v234
superjh 0.090 1.022 4845.974 0.312 1.384 v237
R2:final model IV(distance) IV(language)
0.1451483 0.9423272 0.9082094
coef Fstat ddf pvalue VIF
(Intercept) -2.116 1.009 19479.57 0.315 NA
fyll 1.229 1.968 11958.28 0.161 1.532
fydd 0.485 1.427 36211.65 0.232 1.405
popdens -0.230 8.163 352496.49 0.004 2.249 v156 <- negative corr coeff
pctFemPolyg -0.006 2.660 881.63 0.103 1.091 v1684
settype 0.150 8.743 73963.00 0.003 2.075 v234 <- significant
superjh 0.181 3.677 15177.64 0.055 1.552 v237 <- significant
EduMod-21: (your dep var here) Imputation and Regression depvar 678 famine Ralph
coef Fstat ddf pvalue VIF
(Intercept) 0.5112 0.6862 7223.643 0.4075 NA
fydd 0.6527 4.8449 6045.295 0.0278 1.057
moralgods 0.1035 3.6398 2120.823 0.0566 1.083 v238
ecorich -0.1247 6.7136 18392.013 0.0096 1.134 v857
laketwohunf 0.3181 3.2197 1620.818 0.0729 1.104 v1899
foodscarc 0.2102 16.0595 268.324 0.0001 1.070 v1685
himilexp -0.2144 2.7892 1521.711 0.0951 1.073 v899
> r2
R2:final model IV(distance) IV(language)
0.2498157 0.9551714 0.9356978
Ralph, I pulled your results from very low r-squared to 25% by adding back some of the significant variables in your earlier runs. Looks Good! You could revise your powerpoint accordingly, see your Edumod site, last results. You certainly learned well how to add variables! And your hypothesis still stood up
EduMod-22: (your dep var here) Imputation and Regression depvar 155 <--17 155 money Peyman
coef Fstat ddf pvalue VIF
(Intercept) 1.26474539 5.329240 8605096 0.0209705456 NA
fyll -0.72486323 5.462145 41757822 0.0194327783 3.303
fydd 0.72577974 10.390128 8557207 0.0012669128 3.428
foodtrade 0.01749311 3.858317 226238576 0.0494999490 1.142 v819
ecorich -0.17634335 5.039715 113335768 0.0247725737 1.320 v857 <-- interestingly non ideal climate
popdens 0.34950422 24.204448 698453736 0.0000008663 1.643 v156
superjh 0.39980869 21.848604 1102810 0.0000029507 1.545 v237
R2:final model R2:IV(distance) R2:IV(language)
0.4534504 0.9513012 0.9878678
Fstat df pvalue
RESET 0.276 9.204047e+06 0.599
Wald on restrs. 40.011 8.017607e+05 0.000
NCV 0.461 3.428915e+08 0.497
SWnormal 0.666 7.754900e+08 0.414
lagll 2.558 1.096519e+10 0.110
lagdd 1.639 1.440335e+06 0.200
EduMod-23: (your dep var here) Imputation and Regression depvar 26 bodilycontact Jackie
Not 1710 sleeping arrangements==
(Did more xUR<- but not trim to xR<-)
coef Fstat ddf pvalue VIF
(Intercept) 1.466 2.310 312780.05 0.129 NA
fyll -0.401 1.423 282093.10 0.233 1.865
fydd 1.143 52.858 235440.67 0.000 1.831
nuclearfam -0.582 9.347 17736.58 0.002 1.560 v210<=3 v872? Independent Polyandrous Families
lrgfam -0.071 7.897 47956.48 0.005 1.584 v68 1-12 --> v80 1-5 *not on map list
> r2
R2:final model IV(distance) IV(language)
0.4199224 0.9878506 0.9854879
Looks like the answer to your question is the nuclear and the Independent Polyandrous Family. You might check out those 3 cases coded: 3= Independent Polyandrous Families for variable 210.-24: (your dep var here) Imputation and Regression]] depvar
EduMod-24: (your dep var here) Imputation and Regression depvar 167 pre_mar_sex Michael
167. FREQUENCY OF PREMARITAL SEX- FEMALE
77 . = Missing data
51 1 = Universal
19 2 = Moderate
16 3 = Occasional
23 4 = Uncommon
coef Fstat ddf pvalue VIF
(Intercept) -1.631 1.174 70391.459 0.278 NA
fyll 2.132 6.646 11262.288 0.010 1.626
fydd -0.435 0.840 5338.330 0.360 1.863
ecorich -0.138 2.118 783907.278 0.146 1.155 v857 (more PreMarSex in richer environment)
roots -0.444 2.246 151489.409 0.134 1.197 v233==5 (dto: roots)
plow 1.084 12.058 1135404.100 0.001 1.192 v243>1 (less PreMarSex in plow societies)
foodtrade 0.021 3.644 1322183.303 0.056 1.093 v819 (less PreMarSex in with food from trade)
ncmallow 0.080 3.006 9624.904 0.083 1.084 v227 numCosAlld (less PreMarSex the fewer cousins allowed in marriage)
> r2
R2:final model IV(distance) IV(language)
0.1819867 0.9249267 0.9295315
coef Fstat ddf pvalue VIF
(Intercept) -1.929 1.662 91997.06 0.197 NA
fyll 1.765 6.905 111415.39 0.009 1.068
ecorich -0.124 1.806 917573.51 0.179 1.078 v857
plow 1.080 12.597 7070830.03 0.000 1.111 v243>1
foodtrade 0.021 3.724 3296448.44 0.054 1.088 v819
ncmallow 0.084 3.339 13758.85 0.068 1.063 v227
> r2
R2:final model IV(distance) IV(language)
0.1486653 0.9242870 0.9286989
Not much r2 in the last model, will be less when ecorich is removed. Maybe adding latitude<-SCCS$v179 would add predictiveness
EduMod-26: (your dep var here) Imputation and Regression depvar 740 marr_arranged Abiha
coef Fstat ddf pvalue VIF
(Intercept) -0.107 0.001 36901.816 0.975 NA
fyll 1.113 1.984 10745.185 0.159 1.280
dateobs -0.001 2.448 40445.967 0.118 1.101 v838
cultints 0.186 2.813 17850.507 0.093 2.255 v232
cereals -0.876 6.767 13234.615 0.009 1.749 v233==6
bovines 0.840 5.103 14865.559 0.024 1.877 v244==7
foodtrade 0.027 3.967 77497.447 0.046 1.246 v819
popdens -0.204 3.236 7830.208 0.072 1.973 v156 <- negative corr coeff
exogamy 0.242 4.954 105534.447 0.026 1.118 v72
localjh 0.490 4.396 24655.067 0.036 1.184 v236
fempower -0.169 5.149 749.082 0.024 1.057 v663
pctFemPolyg 0.014 7.107 205.293 0.008 1.083 v872
r2 0.2451251
R2:final model IV(distance) IV(language)
0.2451251 0.9108772 0.9008883
Fstat df pvalue
RESET 0.371 1252.574 0.542 <--good diagnostic, no significant nonlinears needed
Wald on restrs. 2.233 1016.628 0.135 <--good diagnostic, no significant excluded variables
NCV 1.630 763.710 0.202 <--good diagnostic, no bunching of autocorrelation errors
SWnormal 5.108 47610.956 0.024 <--diagnostic ok, don’t expect normality
lagll 1.756 556755.724 0.185 <--good diagnostic, no language residuals
lagdd 0.651 146180.887 0.420 <--good diagnostic, no distance residuals
EduMod-27: (your dep var here) Imputation and Regression depvar 1721 wealthy Lyndon
DRW made a mistake with this output - programs 1-2 were followed by Program 2 without seperators... THESE ARE THE CORRECTED RESULTS Doug 20:27, 11 December 2009 (PST)
- Much more interesting because Patrick's hunches about extwar (tho his var was v1648) and socstrat were CORRECT!!!
B| Restricted model (Shrunk xR further) for "wealth" as depvar RESULTS
New Model http://bit.ly/c1T3ca
coef Fstat ddf pvalue VIF
(Intercept) 3.056 6.418 112954.938 0.011 NA
fyll -0.675 1.319 411201.743 0.251 1.180
gath -0.132 5.937 503084.973 0.015 1.605 v203
pigs -0.598 8.692 634911.791 0.003 1.066 v244==2
popdens 0.130 5.589 64044.717 0.018 1.631 v156
pathstress -0.059 9.999 61716.313 0.002 1.225 v1260
brideprice 0.346 5.226 244811.573 0.022 1.236 v208
extwar 0.199 4.423 3532.412 0.036 1.170 v892 extwar<-- Patrick's new indep var
socstrat 0.178 4.514 1884.706 0.034 1.450 v1751 <-- Patrick's new indep var
R2:final model R2:IV(distance) R2:IV(language)
0.4113524 0.9629149 0.9438192
Old model
coef Fstat ddf pvalue VIF
(Intercept) 4.285 4.984 271450.75 0.026 NA
fyll -0.857 1.310 23602.17 0.252 1.720
fydd 0.263 0.731 90039.63 0.392 1.768
dateobs 0.000 0.175 53615620.63 0.676 1.122 v
gath -0.137 6.508 50706063.79 0.011 1.438 v203
pigs -0.557 6.571 3952502.93 0.010 1.115 v244=2
popdens 0.160 8.389 238997436.21 0.004 1.524 v156
pathstress -0.054 5.984 1616190.78 0.014 1.528 v1260
brideprice 0.331 4.299 7028256.62 0.038 1.255 v208
R2:final model R2:IV(distance) R2:IV(language)
0.3536885 0.9628847 0.9439482
Fstat df pvalue
RESET 0.018 11666418.6 0.893
Wald on restrs. 0.010 136366.0 0.921
NCV 0.578 6039012.8 0.447
SWnormal 3.066 3168299.5 0.080
lagll 0.269 158797.3 0.604
lagdd 0.652 291338.3 0.419
previously (MISTAKEN - no longer in results file)
coef Fstat ddf pvalue VIF
(Intercept) 2.536 77.259 Inf 0.000 NA
gath -0.132 6.227 Inf 0.013 1.427 v203
pigs -0.625 9.149 Inf 0.002 1.022 v244==2
popdens 0.164 9.404 Inf 0.002 1.441 v156
pathstress -0.065 11.653 Inf 0.001 1.177 v1260
brideprice 0.288 3.521 Inf 0.061 1.175 v208
> r2
R2:final model IV(distance) IV(language)
0.3390135 0.9628847 0.9439482
Try restoring:
fyll -0.857 1.310 23602.17 0.252 1.720
coef Fstat ddf pvalue VIF
(Intercept) 3.599 8.523 2038377 0.004 NA
fyll -0.538 0.786 1788182 0.375 1.169
gath -0.136 6.622 858994036 0.010 1.437 v203
pigs -0.618 9.008 4044936515 0.003 1.024 v244==2
popdens 0.171 10.069 2426464775 0.002 1.467 v156
pathstress -0.064 11.118 2690589910 0.001 1.187 v1260
brideprice 0.316 4.110 1760015405 0.043 1.226 v208
> r2
R2:final model IV(distance) IV(language)
0.3531059 0.9637096 0.9420445
EduMod-28: (your dep var here) Imputation and Regression depvar 666. Moderate or Frequent Interpersonal Violence Patrick Kim
depvar 666. Moderate or Frequent Interpersonal Violence - interperviol: interviol / violence / freintovio
55 . = Missing data
43 1 = Absent
88 2 = Present
HARD TO DEBUG TO ELIMINATE DATEOBS AND ECORICH BECAUSE FX<- ELEMENTS WERE DELETED
coef Fstat ddf pvalue VIF
coef Fstat ddf pvalue VIF
(Intercept) 4.257 3.014 2049.423 0.083 NA
fyll -2.543 2.896 1264.383 0.089 1.386
fydd 1.285 11.270 772.984 0.001 1.352
dateobs 0.000 0.465 4869.854 0.495 1.050 v838
ecorich -0.029 0.793 13274.670 0.373 1.211 v857
superjh 0.103 6.856 5100.674 0.009 1.267 v237
moralgods -0.081 5.068 1316.496 0.025 1.184 v238
agrlateboy 0.065 9.732 782.277 0.002 1.061 v300 added to Eff and Dow indepvars
R2:final model IV(distance) IV(language)
0.2131279 0.9131640 0.7228776
Fstat df pvalue
RESET 0.705 1064.215 0.401
Wald on restrs. 2.873 358.016 0.091
NCV 3.752 1893.522 0.053
SWnormal 17.751 1459.958 0.000
lagll 1.475 311571.730 0.225
lagdd 2.501 24337.150 0.114
EduMod-29: (your dep var here) Imputation and Regression depvar 591 Ownership or Control of the Use of Dwellings Kimberley
coef Fstat ddf pvalue VIF
591: Ownership or Control of the Use of Dwellings
1 2 3 4
"22" "12" "25" "13" "72" "female control over dwellings"
coef Fstat ddf pvalue VIF
(Intercept) 4.776 17.106 52643.934 0.000 NA
dateobs -0.001 3.348 216022.072 0.067 1.212 v838
pigs -0.900 7.447 50125.299 0.006 1.060 v244==2
ecorich -0.274 9.488 237819.554 0.002 1.030 v857
femsubs 0.291 11.422 10868.924 0.001 1.132 v826
war -0.041 4.530 1237.883 0.033 1.146 v1648
R2:final model IV(distance) IV(language)
0.2852806 0.9663462 0.9707996
Fstat df pvalue
RESET 2.555 89543.076 0.110
Wald on restrs. 1.167 104.057 0.283
NCV 1.032 5958.284 0.310
SWnormal 0.037 1172082.865 0.848
lagll 0.082 96609.778 0.775
lagdd 0.294 402078.856 0.588
EduMod-30: (your dep var here) Imputation and Regression depvar 740 Marriage Arrangements=6 Parental control Jessica
DRW did your cull down to xR<- Restricted model
coef Fstat ddf pvalue VIF
(Intercept) -0.300 1.320 3420.563 0.251 NA
fyll 1.443 1.843 6618.819 0.175 1.037
foodtrade 0.010 10.013 162778.505 0.002 1.100 v819
femsubs -0.043 3.457 3637.048 0.063 1.108 v663
himilexp -0.123 3.348 24121.373 0.067 1.073 v899
pctFemPolyg 0.005 14.979 918.824 0.000 1.123 v1684
agrlateboy 0.045 5.556 125.812 0.020 1.029 v300
> r2 R2:final model IV(distance) IV(language)
0.1733631 0.9231913 0.8182336
Fstat df pvalue
RESET 3.660 68.237 0.060
Wald on restrs. 5.331 914.415 0.021
NCV 11.391 115.157 0.001
SWnormal 28.342 491.715 0.000
lagll 1.307 1131277.093 0.253
lagdd 1.155 187678.807 0.282
EduMod-31: (your dep var here) Imputation and Regression depvar 1650 frq_ext_war Scott Feuchter
v1650 frq_ext_war
coef Fstat ddf pvalue VIF
(Intercept) 26.006 4.953 1158.161 0.026 NA
fyll -2.193 1.843 1978.222 0.175 1.698
fydd 0.977 10.256 11354.007 0.001 1.715
PrestigeWarrior -1.701 5.130 442.014 0.024 1.109 v903
Freqattacked -2.399 9.951 685.006 0.002 1.119 v893
R2:final model R2:IV(distance) R2:IV(language)
0.2125149 0.8259968 0.6381231
Fstat df pvalue
RESET 0.202 923.436 0.653
Wald on restrs. 0.051 25.108 0.822
NCV 2.012 1572.295 0.156
SWnormal 7.243 570.064 0.007
lagll 0.946 287934.419 0.331
lagdd 0.604 428.167 0.437
EduMod-31: Imputation and Regression depvar 892 Alex 2010 replaces 2009
WITHOUT foodscarc
coef Fstat ddf pvalue VIF
(Intercept) -1.56678527 1.082033 5716.0767 0.29828729 NA
fyll 1.09889350 1.753672 6456.6650 0.18546271 2.024
fydd 0.28079555 0.976354 2397.3471 0.32320034 2.089
localjh -0.12610289 2.206010 6659.4806 0.13752113 1.018
FrqAttacked 0.52783026 55.467163 274.6798 0.00000000 1.079
superjh -0.09899757 6.141427 1295.8759 0.01333167 1.046
R2:final model R2:IV(distance) R2:IV(language)
0.3587232 0.9813494 0.9358644
Fstat df pvalue
RESET 0.400 268.426 0.528
Wald on restrs. 0.319 82.154 0.574
NCV 3.277 162.443 0.072
SWnormal 0.755 256.052 0.386
lagll 1.147 20448.377 0.284
coef Fstat ddf pvalue VIF
(Intercept) -1.73147478 1.2818655 29.07113 0.26680505 NA
fyll 0.72520771 0.8082993 83.08151 0.37122189 1.997
fydd 0.39374182 2.1956964 1209.98619 0.13865702 2.040
plunder 0.35051229 11.1305381 5297.71900 0.00085503 1.210 v912 more plunder
prestigewarrior 0.21830094 8.5840789 106.80714 0.00414711 1.302 v903 more prestige
freqattacked 0.36381942 23.9843690 40.26092 0.00001619 1.220 v893 being attacked (big effect)
superjh -0.09865217 6.2782466 1442.75704 0.01233231 1.149 v237 more *state* external warfare
R2:final model R2:IV(distance) R2:IV(language)
0.4331529 0.9820238 0.9411298
Fstat df pvalue
RESET 0.613 398.637 0.434
Wald on restrs. 0.553 230.279 0.458
NCV 6.689 1920.383 0.010
SWnormal 0.001 184417.863 0.978
lagll 1.006 22781.254 0.316
lagdd 0.997 33308.388 0.318
EduMod-32: (your dep var here) Imputation and Regression depvars v892=extwar Alex
Same depvarname as Scott but different variable number
2009 v892=extwar Alex
coef Fstat ddf pvalue VIF
(Intercept) -3.482 4.361 658.288 0.037 NA
fyll 1.378 2.436 605.874 0.119 1.773
fydd 0.406 2.128 2651.988 0.145 1.754
prestigewarrior 0.256 10.300 180.523 0.002 1.224 v903
freqattacked 0.500 43.436 850.188 0.000 1.218 v893
militarysuccess 0.075 2.997 1132.099 0.084 1.094 v908
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.3927474 0.8454564 0.7587783
> ccc
Fstat df pvalue
RESET 0.324 381.443 0.570
Wald on restrs. 3.185 726.086 0.075
NCV 1.867 69.615 0.176
SWnormal 0.156 13464.176 0.693
lagll 1.264 19935.309 0.261
lagdd 0.609 6539.668 0.435
EduMod-33: (your dep var here) Imputation and Regression depvar incomplete Natasha
(NR due to Illness)
EduMod-40: (your dep var here) Imputation and Regression depvar 664 Ideology of Male Toughness Bryan 2009
664. Ideology of Male Toughness
1 2
"21" "87" "108" "maletough"
coef Fstat ddf pvalue VIF
(Intercept) -0.353716277 1.875316 1217.4918 0.1711200360 NA
fydd 0.938145946 39.163140 1258.0293 0.0000000005 1.187
pctFemPolyg -0.003127366 7.119312 1143.9931 0.0077335890 1.160 v1684
intervio 0.336313718 23.056449 542.7897 0.0000020374 1.146 v666 (violence) interperviol: interviol / violence / freintovio
R2:final model R2:IV(distance) R2:IV(language)
0.4424344 0.9804846 0.9681605
Fstat df pvalue
RESET 3.128 2944.882 0.077
Wald on restrs. 20.925 40.696 0.000
NCV 12.020 1381.130 0.001
SWnormal 23.864 21039.491 0.000
lagll 1.175 207177.862 0.278
lagdd 0.728 13459.500 0.394
DRW: variance actually drops with fyll
coef Fstat ddf pvalue VIF
(Intercept) 0.650493757 0.5256909 4871.0778 0.4684589696 NA
fyll -0.654464947 1.3321947 5935.5898 0.2484616171 1.546
fydd 1.055017234 33.9906529 1370.0843 0.0000000069 1.710
pctFemPolyg -0.002608273 4.6255947 253.6818 0.0324433192 1.162
intervio 0.315879828 20.7593131 908.0216 0.0000059188 1.144
R2:final model R2:IV(distance) R2:IV(language)
0.4388517 0.9815415 0.9656521
Fstat df pvalue
RESET 2.592 1668.343 0.108
Wald on restrs. 18.480 54.363 0.000
NCV 10.143 713.128 0.002
SWnormal 25.492 43440.024 0.000
lagll 0.310 786915.103 0.578
lagdd 0.853 3567.043 0.356
EduMod-41: (your dep var here) Imputation and Regression & EM-8 depvar 667 rape - Christina
(Intercept) 1.8576 95.3407 13009.98 0.0000 NA
cultints 0.0537 3.7116 882892.16 0.0540 1.001 v232
fempolpar -0.3122 9.6715 6040.82 0.0019 1.001 v661
R2:final model IV(distance) IV(language)
0.1331899 0.9430003 0.9602931
Not much R2 - what about adding significant variables from Amanda EM-11
EduMod-42: (your dep var here) Imputation and Regression depvar 714 sexratio714 - Doug White
Fstat df pvalue
RESET 0.510 5.769700e+01 0.478
Wald on restrs. 9.626 7.795000e+00 0.015
NCV 0.498 4.081920e+02 0.481
SWnormal 10.489 1.420267e+03 0.001
lagll 0.361 4.374437e+13 0.548
lagdd 0.040 1.873223e+05 0.842
mnb fst v pval VIF
(Intercept) 0.119 0.033 330.456 0.857 NA
fydd 0.652 4.966 36682324.341 0.026 1.142
polygyny 0.166 1.220 283642.344 0.269 1.334
war679 0.043 0.101 435.977 0.751 1.073
loCasualties 0.134 1.025 50.148 0.316 1.114
fratgrpstr -0.040 0.548 395.491 0.459 1.338
plunder 0.099 0.572 4454.191 0.449 1.119 v912
1 2 3
"20" "60" "10" "90" "sexratio714"
EduMod Mac-6: (your dep var here) Imputation and Regression depvar 33 pain infliction Ryan (R2 near zero!)
Initial model was this.
coef Fstat ddf pvalue VIF
(Intercept) -0.007 0.000 7.822997e+04 0.998 NA
fyll 1.041 1.122 1.618677e+04 0.290 1.717
fydd 0.191 0.236 8.993515e+03 0.627 1.717
dateobs -0.001 1.800 1.673191e+08 0.180 1.014 v838
localjh 0.216 2.544 2.271746e+07 0.111 1.026 v236
> r2
R2:final model IV(distance) IV(language)
0.0240394 0.9534388 0.9311677
Basically zero R2. Have you tried googling: "Standard Cross-Cultural Sample"+"pain infliction"? The book by Levinson, available at the library, is full of information on your topic.
- Used Spss to see what was related to childhood pain, and discovered v35, Ceremonialism surrounding pain, just after v33, Childhood pain. DRW entered into the model, it worked. That suggested connection with polgyny, as in the article that should be read before the ppt and writeup of final paper:
- John W. M. Whiting and Beatrice B. Whiting. 1975. Aloofness and Intimacy of Husbands and Wives: A Cross-Cultural Study. Ethos, Vol. 3, No. 2 (Summer, 1975), pp. 183-207.
coef Fstat ddf pvalue VIF
(Intercept) -2.860 1.720 15874.136 0.190 NA
fyll 1.666 4.674 11167.081 0.031 1.129
anim 0.105 6.311 221261.889 0.012 1.270 v206
fish 0.099 4.416 1085147.313 0.036 1.309 v205
pctFemPolyg 0.008 4.692 325.586 0.031 1.129 v1684
ceremonial 0.189 5.477 7237.927 0.019 1.073 v35
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.0940897 0.9558412 0.9344346
EduMod-47: Imputation and Regression depvar 901 loCasualties Doug
901. CASUALTY RATE (VAR LABEL REVERSED)
[Follows Otterbein 1970: 81, 146]
107 . = Missing Data
35 1 = High (1/3rd or more of combatants)
44 2 = Low
"This has turned out to be a difficult variable to code;
information does not exist in most cases." Wheeler l974:270
added superjh
> bbb
mnb fst v pval VIF
(Intercept) 2.255 14.530 324.079 0.000 NA
fydd -0.455 1.783 587.381 0.182 1.630
fratgrpstr 0.044 1.219 178.258 0.271 1.245
superjh -0.113 5.088 60217.176 0.024 1.797
plunder 0.195 3.388 96407.231 0.066 1.061 v912
plow -0.521 8.044 3751.375 0.005 2.323 v243 plow -> casualties
R2:final model R2:IV(distance) R2:IV(language)
0.3130611 0.9742442 0.9759367
Fstat df pvalue
RESET 1.849 546.058 0.175
Wald on restrs. 1.019 4.515 0.364
NCV 1.490 602.035 0.223
SWnormal 17.349 826566.901 0.000
lagll 0.464 3239276.127 0.496
lagdd 0.272 9324.339 0.602
early (see EduMod54)
R2:final model R2:IV(distance) R2:IV(language)
0.2736470 0.9720598 0.9678211
Fstat df pvalue
RESET 2.203 2.561876e+03 0.138
Wald on restrs. 1.013 3.273230e+02 0.315
NCV 3.411 1.214156e+05 0.065
SWnormal 23.588 1.847591e+07 0.000
lagll 0.403 4.303043e+08 0.526
lagdd 0.231 6.396923e+04 0.631
mnb fst v pval VIF
(Intercept) 2.620 22.893 20709.86 0.000 NA
fydd -0.477 2.076 20294.78 0.150 1.525
plow -0.461 6.588 793025.94 0.010 2.182 v243>1)*1 lolo=high casualties w/plow
superjh -0.103 4.438 37129052.78 0.035 1.656 v237 high lolo=high casualties w/superjh 1 2
"35" "44" "79" "noCasualties of war"
mnb fst v pval VIF
(Intercept) 2.499 20.808 122520.4 0.000 NA
fydd -0.549 2.827 146915.3 0.093 1.545
plow -0.529 8.581 3124471.5 0.003 2.265
plunder 0.180 2.961 7672896.6 0.085 1.046 v912
superjh -0.099 4.213 105577824.4 0.040 1.661
R2:final model R2:IV(distance) R2:IV(language)
0.3037894 0.9792319 0.9769128
Fstat df pvalue
RESET 3.284 6.092270e+03 0.070
Wald on restrs. 5.525 1.653600e+01 0.031
NCV 2.345 1.385467e+06 0.126
SWnormal 15.482 1.802453e+06 0.000
lagll 0.456 3.024974e+09 0.500
lagdd 0.186 7.237257e+05 0.666
1 2
"35" "44" "79" noCasualties
EduMod-48: Imputation and Regression depvar 679 warfight Doug
R2:final model R2:IV(distance) R2:IV(language)
0.2320951 0.8971305 0.9250728
Fstat df pvalue
RESET 1.013 460.612 0.315
Wald on restrs. 0.033 38.358 0.856
NCV 4.642 2551.851 0.031
SWnormal 21.057 1147.379 0.000
lagll 0.830 79368939.357 0.362
lagdd 1.422 261048.303 0.233
mnb fst v pval VIF
(Intercept) 4.407 10.481 102.914 0.002 NA
fyll -3.095 10.452 85.760 0.002 1.748
fydd 1.149 4.960 2822.048 0.026 1.706
fratgrpstr 0.100 8.469 27.816 0.007 1.476 v570
marrcaptives 0.122 8.193 115687.577 0.004 1.291 v870
pigs 0.274 7.176 335.679 0.008 1.139 v244==2
polygyny 0.022 0.465 518.488 0.495 1.433 v860 <-- delete
EduMod-49:_Imputation_and_Regression#A.7C_Restricted_DepVar_892_Freq_of_Warfare_Burton-White_Remove_plunder_fixedresources_p.3D0.708.2C_Ecorich_p.3D.236.2C_and_Remove_himiexp.21_ecorich_alone_p.3D.289_fixedresources_along_0.238_.28not_Pigs_milk.2Bbovines.2B.29_2 depvar 892 extwar Doug
EduMod-49: Imputation and Regression depvar 892 extwar Doug
see EduMod-32: Imputation and Regression v892 Alex User:Alexander George-Johnson - freqexternalwar
EduMod-50: Imputation and Regression depvar 570 fratintgrp Doug
"fratintgrp" v570
v570 "Fraternal Interest Groups"==
coef Fstat ddf pvalue VIF
(Intercept) 1.4779856 5.852997 53.68208 0.01896963 NA
fyll 0.5212404 9.890801 32.38010 0.00354796 1.522
pre_mar_sex 0.1343625 2.269284 1143.30050 0.13223706 1.093 v167 NS
gath -0.1402414 2.807774 296.90609 0.09486122 1.613 v203
fish -0.2666837 17.217714 1887.13743 0.00003481 1.214 v205
localOfficeNY 0.7034525 7.202605 230.51163 0.00780779 1.529 v1742>=1)*1
popdens 0.1524839 3.257872 106.50937 0.07390675 1.557 v156
fempower -0.1229961 3.942763 59.18168 0.05171249 1.133 v663
> r2
R2:final model R2:IV(distance) R2:IV(language)
0.6376127 0.9961557 0.9989275
Fstat df pvalue
RESET 7.852 1736.398 0.005
Wald on restrs. 5.143 19.455 0.035
NCV 5.332 35.231 0.027
SWnormal 0.324 9.656 0.582
lagll 0.110 1529634.434 0.740
lagdd 0.530 4440619.416 0.466
1 2 3 4 5
"35" "12" "16" "8" "11" "82" "Fraternal InterestGroups"
add "FemPartic" depvar is still fratgrpstr
661. Female Political Participation, at least informal influence
41 . = Missing data
62 1 = Absent
83 2 = Present
R2:final model R2:IV(distance) R2:IV(language)
0.6373354 0.9959525 0.9989719
Fstat df pvalue
RESET 3.507 8648.356 0.061
Wald on restrs. 0.618 3.310 0.484
NCV 5.559 32212.950 0.018
SWnormal 0.254 6557.123 0.614
lagll 0.093 104122.766 0.760
lagdd 0.334 5627.259 0.563
mnb fst v pval VIF
(Intercept) 1.643 7.283 3465.749 0.007 NA
fyll 0.162 0.321 341.191 0.572 5.021
fydd 0.385 2.294 87.692 0.134 5.523
pre_mar_sex 0.067 0.376 12.177 0.551 1.255 v167
gath -0.119 1.997 1201.969 0.158 1.639 v203
fish -0.236 11.290 1091.135 0.001 1.428 v205
localOfficeNY 0.498 3.177 91.487 0.078 1.670 v
popdens 0.172 4.441 29126.854 0.035 1.546 v
Paige661 -0.496 5.003 1917.846 0.025 1.105 v661 "FemPartic" ?????
EduMod-51: Imputation and Regression depvar 1769 Pryor's agrisys Doug
Inoperative because an extra variable is required.
EduMod-53: Imputation and Regression depvar 862 sororal polygyny Doug (R2 .34)
R2:final model R2:IV(distance) R2:IV(language)
0.3418397 0.9851930 0.9945666
Fstat df pvalue
RESET 0.018 8029.494 0.894 No remaining nonlinear variables
Wald on restrs. 0.474 145.614 0.492 Nno significant excluded variables
NCV 2.555 4668.842 0.110 Error terms not bunched
SWnormal 7.576 19919.490 0.006 Error terms not normally dist.
lagll 0.030 30256.144 0.863 No remaining network effects
lagdd 2.018 33297.765 0.155 No remaining network effects
> bbb<-data.frame(round(cbind(mnb,fst,v,pval),3)) #.8 changed to back to .3 for significance test
> bbb$VIF[2:NROW(bbb)]<-round(apply(vif,2,mean),3)
> bbb
mnb fst v pval VIF
(Intercept) 0.368 1.277 455611.42 0.258 NA
fydd 0.593 13.483 565078.01 0.00024 1.318
wives4wealthy 0.145 16.368 33803.37 0.00005 1.073 v866
wives4hunters 0.537 12.335 25846.72 0.00044 1.103 v867
gath 0.091 6.856 35066.91 0.00883 1.245 v203
fish 0.049 2.735 132467.12 0.09816 1.179 v205
tree 0.314 2.655 147756.25 0.10325 1.068 v233==4
> aaa 1 2 3 4
"25" "94" "36" "9" sum= "164" "sororal polygyny"
EduMod-54: Imputation and Regression depvar 860 general polygyny Doug (R2 .42)
coef Fstat ddf pvalue VIF
(Intercept) 1.40727731 4.213711 70.522389 0.04381042 NA
fydd 0.60591064 15.966734 99.663004 0.00012362 1.525
fratgrpstr 0.14629869 2.106269 4.316298 0.21527213 1.617 v570 <-- added (not significant)
marrcaptives 0.30969289 10.326649 106.688930 0.00173450 1.297 v870
plunder -0.47610865 5.957795 25.634272 0.02187990 1.234 v912
plow -0.50251248 3.254884 108.066785 0.07399658 1.748 v243>1)*1
ecorich 0.27533645 5.645073 33755.535059 0.01751004 1.135 v857
fixedresources 0.18111364 0.664945 49.741011 0.41870723 1.809 v859>=8)*1 (not significant)
fem_agri 0.00669009 3.119944 88.369908 0.08079269 1.224 v821
femproduceND -0.51963611 5.618524 1808.970982 0.01787593 1.074 v658
R2:final model R2:IV(distance) R2:IV(language)
0.4449750 0.9880311 0.9855434
Fstat df pvalue
RESET 1.374 536.575 0.242
Wald on restrs. 2.551 105.124 0.113
NCV 2.006 469.776 0.157
SWnormal 0.232 47.199 0.633
lagll 2.143 23438.187 0.143
lagdd 2.505 758.610 0.114
Without Ecorich (dichotomized 1-4/5-6)
ADD: fem_agri 1-10
femproduceND 1-2
mnb fst v pval VIF
(Intercept) 1.019 2.916 816.089 0.088 NA
fydd 0.600 16.188 1714.459 0.000 1.454
fratgrpstr 0.199 6.138 58.710 0.016 1.277 v570 <-- added
marrcaptives 0.307 8.259 170.596 0.005 1.334 v870
plunder -0.514 6.591 203.057 0.011 1.243 v912
plow -0.515 3.986 815.291 0.046 1.437 v243>1)*1
fempower -0.085 3.290 1524.320 0.070 1.092 v663 (scale of 657-662)
femsubs 0.129 5.232 2347.825 0.022 1.112 v890
R2:final model R2:IV(distance) R2:IV(language)
0.4228538 0.9836772 0.9801087
Fstat df pvalue
RESET 0.351 257.064 0.554 No remaining nonlinear variables
Wald on restrs. 1.967 1997.328 0.161 No significant excluded variables
NCV 0.889 3173.292 0.346 Error terms not bunched
SWnormal 0.003 6444.251 0.955 Error terms not normally distributed
lagll 0.835 11231.752 0.361 No remaining network effects
lagdd 4.082 120957.814 0.043 Spatial clusters in remaining network effects 1 2 3 4 5
"27" "32" "45" "34" "46" "184" "polygyny"
With Ecorich (dichotomized 1-4/5-6)
bbb
mnb fst v pval VIF
(Intercept) 1.077 3.116 137.539 0.080 NA
fydd 0.567 13.607 265.405 0.000 1.583
ecorich 0.075 0.144 1403.743 0.704 1.233 v857>=4 (Not significant)
fratgrpstr 0.206 7.154 11.643 0.021 1.348 v570
marrcaptives 0.327 11.699 1444.501 0.001 1.319 v870
plunder -0.550 8.937 249.970 0.003 1.219 v912
plow -0.533 3.933 42.841 0.054 1.514 v243>==1)*1
fempower -0.075 2.058 12.972 0.175 1.083 v663 (scale of 657-662)
femsubs 0.118 4.243 62.569 0.044 1.112 v890
No effect of loCasualties
R2:final model R2:IV(distance) R2:IV(language)
0.4667883 0.9879008 0.9846574
> ccc
Fstat df pvalue
RESET 1.044 920.187 0.307
Wald on restrs. 3.663 53.253 0.061
NCV 3.282 1433.270 0.070
SWnormal 0.271 38.966 0.606
lagll 1.656 16218.119 0.198
lagdd 2.491 4464.359 0.115 > bbb
mnb fst v pval VIF
(Intercept) 1.300 3.039 23.912 0.094 NA
fydd 0.568 16.404 4021.954 0.0001 1.454
loCasualties 0.138 0.306 3.903 0.610 1.165 <-- no effect
fratgrpstr 0.256 15.093 4949.483 0.0001 1.274
marrcaptives 0.272 7.633 104.146 0.007 1.387 v870
plunder -0.526 8.222 98.073 0.005 1.258 v912
plow -0.490 3.536 109.359 0.063 1.592
ecorich 0.275 5.743 3089.952 0.017 1.146
fem_agri 0.005 2.318 3658.316 0.128 1.191
femproduceND -0.486 4.186 16.095 0.057 1.061
> aaa
1 2 3 4 5
"27" "32" "45" "34" "46" "184" "polygyny"
- EduMod Mac-10: Imputation and Regression - v6 Ari: Aszmuilo InterPerViol
- EduMod-32: Imputation and Regression - v892=extwar Alex User:Alexander George-Johnson - freqexternalwar
- EduMod-55: Imputation and Regression - v661 Chelsea User:CCrager - name: FemPartic in politics GIVES YOU A PPT and PAPER TOPIC. The idea we discussed in class was to add some of the other Sanday variables v657-v660, then v662 to predict your depvar v661.
- EduMod-56: Imputation and Regression - v577 User talk:GloriaM - Mythical Founders of the Culture (name: mythicfounders} have UR results, can do your Restricted Model.
- EduMod-57: Imputation and Regression -- v821 User:Jasiellt - Percent Female Contribution to Agriculture
- EduMod-58: Imputation and Regression - v649 Bryan User:Bwilliams - beliefs about illness: Theories of Fate --- revise to add indepvars (Murdock:) societal complexity v149-158, only v153,v155,v156 are indepvars so far no predictions
- EduMod-59: Imputation and Regression - v155 TJ (Tim) User:Tsalunga? - v155 Money
- EduMod-60: Imputation and Regression - v282 User:Ariana Keil Pre_maritalSex
- EduMod-67: Imputation and Regression - v953-984 4 Evan User:EStanfield - project Evil Eye
- EduMod-62: Imputation and Regression - v953-984 4 Evan User:EStanfield - alt Contraception & abortion
- EduMod-64: Imputation and Regression - v138 User:Leonchoi? - famine changed from v678 foodstress
- EduMod-66: Imputation and Regression - v1721 User:Dante Anton Thought provoker: comment here. wealthy NUMBER OF RICH PEOPLE. Careful: Correlated with NUMBER OF POOR PEOPLE, i.e., social stratification or wealth differences
- EduMod-65: Imputation and Regression - v90 Nick User:roblesn - Police Nick your 7.1 results are great. Also your edited xR Restricted variable (just delete tree)
- EduMod-68: Imputation and Regression - v646 Nick User:roblesn - beliefs about illness: theories of stress) You suggest Beliefs in Illness: Theories of stress, but its a secondary theory and the various stresses are diverse, so it is not likely that you will find predictors. Change your depvar again
- EduMod-70: Imputation and Regression - v1742 Jeff User:Flackj - "FormalEd"
EduMod54#No_Ecorich_depvar_v860_General_polygyny Doug White
(Intercept) 1.30991693 3.727191 50.48191 0.05915963 NA
fydd 0.64103071 19.776366 2610.38533 0.00000907 1.461
fem_agr 0.00788192 4.014696 27.77204 0.05494911 1.213 v821
femproduceND -0.42537314 2.977787 15.21428 0.10466136 1.068 v658
fratgrpstr 0.21299425 10.277943 160.06718 0.00162559 1.289 v570
marrcaptives 0.30173690 7.797192 15.34408 0.01344416 1.370 v870
plunder -0.49951835 7.395354 95.90840 0.00776331 1.212 v912
plow -0.46496245 3.581363 435734.70593 0.05843187 1.459 v243>1
R2:final model R2:IV(distance) R2:IV(language)
0.4360774 0.9859294 0.9829130
Fstat df pvalue
RESET 0.060 470.824 0.806
Wald on restrs. 1.935 134.346 0.167
NCV 1.584 88.414 0.212
SWnormal 0.098 30027.880 0.755
lagll 0.790 165.058 0.375
lagdd 3.391 172601.883 0.066
Obsolete EduMod54#A.21.21_good_version_-52_xR_Restricted_model_for_v860_Polygyny._For_Unrestricted_model_copy_xUR.3C_vars_over_xR.3C_to_let_others_use_the_new_indep_vars_.23.23.23.23_speedup Program to test final Polygyny model
EduMod54#B_no_effect_of_old_or_new_ecorich v860 Polygyny depvar
R2:final model R2:IV(distance) R2:IV(language)
0.4401096 0.9858793 0.9822109
Fstat df pvalue
RESET 0.004 19.131 0.950
Wald on restrs. 1.410 90.211 0.238
NCV 2.048 655.072 0.153
SWnormal 0.099 83.039 0.754
lagll 1.399 401.408 0.238
lagdd 3.637 6134.099 0.057
mnb fst v pval VIF
(Intercept) 1.442 4.223 53.112 0.045 NA
fydd 0.594 13.773 34.762 0.001 1.578
ecorich 0.118 0.368 518.762 0.544 1.215
fem_agr 0.007 3.473 34.929 0.071 1.166
femproduceND -0.487 4.827 52.566 0.032 1.050
fratgrpstr 0.234 7.819 6.113 0.031 1.305
marrcaptives 0.299 9.234 621.481 0.002 1.420 v
plunder -0.435 5.697 415.512 0.017 1.242 v912
plow -0.505 4.015 208.236 0.046 1.459
EduMod73 General polygyny
EduMod90#B_marriage_of_captives_v870_depvar Doug White
coef Fstat ddf pvalue VIF
(Intercept) -0.32914150 0.1253232 1.796688e+06 0.72333130 NA
fyll 1.01789767 2.9949287 2.885603e+04 0.08353628 1.804
fydd 0.52968968 6.2258034 3.237171e+03 0.01263962 1.832
money -0.10737967 4.2558485 4.716308e+02 0.03966223 1.545 v155
wealthy 0.17690396 3.9297347 1.454133e+01 0.06666304 1.096 v1721
plunder -0.61781334 19.9444656 1.024362e+04 0.00000806 1.079 v912
cultints 0.09516534 3.9502091 9.021819e+03 0.04689573 1.654 v232
popdens -0.08552260 1.9880242 3.848818e+02 0.15935520 2.072
R2:final model R2:IV(distance) R2:IV(language)
0.3109248 0.9859640 0.9693994
Fstat df pvalue
RESET 1.808 164.158 0.181
Wald on restrs. 2.632 11.296 0.132
NCV 4.833 238.283 0.029
SWnormal 1.471 44.949 0.231
lagll 0.781 3582.573 0.377
lagdd 1.160 39190.858 0.282
EduMac-7#B predictors of female agriculture v821 Doug White
coef range effect Fstat ddf pvalue VIF
(Intercept) 50.8897 NA NA 5.1140 79302.76 0.0237 NA
language 0.6127 NA NA 1.4513 58126.45 0.2283 2.9807
distance 0.0140 NA NA 0.0021 1303811.49 0.9635 2.7444
ndrymonth -2.2183 12 -26.6196 11.9329 20028.22 0.0006 1.9234 v196
roots 8.1005 1 8.1005 3.3514 443303.03 0.0671 1.5134 v233==5
animals -2.7563 9 -24.8069 4.4688 610842.08 0.0345 1.9377 v206
plow -10.4342 1 -10.4342 3.7722 54416.27 0.0521 2.0597 v243>1
polygyny 2.1674 4 8.6694 2.8942 35763.16 0.0889 1.3491 v860
bantu 23.2919 1 23.2919 7.8320 14049272.01 0.0051 1.9886 v200-201 (computed)
AnnPrecip07 -0.0038 4818 -18.1198 3.4085 10938.27 0.0649 1.8219 v189
LandSlope18 -2.6644 4 -10.6575 4.6144 183116.86 0.0317 1.3046 v922
lat02 -8.1721 1 -8.1721 3.6310 1137861.40 0.0567 1.5379 v180
R2:final model R2:IV_language R2:IV_distance
0.4230393 0.9828679 0.9676901
Fstat df pvalue
RESET 0.035 1204775.0 0.852
Wald.on.restrs 0.906 3847335.4 0.341
NCV 1.849 110779.2 0.174
SW.normal 0.005 141137.6 0.944
lag..language 1.078 4428583.2 0.299
lag..distance 1.668 1821575.9 0.197
