1. © 2009 Hiu Kwan Chiu Anthro 174AW

2. Originally  Dependent Variable: Divorce However, after many trial and errors with my hypothesis, it is very difficult to find any significant variables. Therefore….

3. Revised Hypothesis “Wife Beating” At first my hypothesis was that I believe that wife beating was a result of male aggression, or to give male a sense of superiority. Thus I thought that issues like women’s participation in things that would increase their power or status would then increase the occurrence of wife beating.

4. Several Independent Variables I ran a lot of variables of which here are several…  913. Trophies and Honors  615. Wife to Husband Institutionalized Deference  616. A Stated Preference for Children of One Sex  626. (No) Belief that Women are Generally Inferior to Men  902. Leadership During Battle  661. Female Political Participation, at least informal influence  664. Ideology of Male Toughness  667. Rape: Incidents reports, or thought of as means of punishment women, or part of ceremony. And many more….

5. Result of My xUR Unrestricted Model > bbb coef Fstat ddf pvalue VIF (Intercept) 7.713 11.559 360.710 0.001 NA fyll -3.789 9.586 255.631 0.002 2.614 fydd 0.503 2.388 648.065 0.123 2.402 cultints -0.026 0.181 754.777 0.671 6.824 roots -0.472 2.841 890.211 0.092 8.889 cereals -0.503 3.204 1190.427 0.074 14.086 gath 0.019 0.139 519.364 0.710 4.311 plow -0.142 0.461 365.253 0.498 4.189 hunt -0.117 3.635 526.576 0.057 6.229 fish 0.041 0.926 852.857 0.336 4.166 anim -0.028 0.285 647.551 0.594 11.225 pigs -0.122 0.382 224.630 0.537 3.795 milk 0.215 1.134 313.969 0.288 6.529 bovines -0.167 0.784 342.844 0.377 5.722 tree -0.357 1.220 716.111 0.270 5.744 foodtrade -0.010 3.905 875.290 0.048 2.236 foodscarc 0.021 0.220 356.545 0.639 2.213 ecorich 0.049 0.942 4844.304 0.332 3.504 popdens 0.134 3.801 435.325 0.052 8.373 pathstress 0.001 0.001 228.183 0.974 4.561 exogamy -0.092 3.169 305.657 0.076 2.246 ncmallow -0.029 1.877 896.493 0.171 1.953 famsize 0.001 0.000 118.496 0.993 4.424 settype -0.074 2.915 751.705 0.088 7.515 localjh 0.131 1.724 265.293 0.190 2.678 superjh 0.077 0.822 180.347 0.366 5.691 moralgods -0.066 1.412 448.603 0.235 3.244 fempower 0.011 0.085 221.102 0.771 2.985 femsubs 0.002 0.001 1175.421 0.970 3.493 sexratio 0.155 1.896 56.560 0.174 2.484 war 0.002 0.073 591.350 0.788 2.374 himilexp 0.134 0.732 150.337 0.394 3.713 money 0.031 0.301 464.961 0.583 4.578 wagelabor 0.001 0.000 49.862 0.989 2.848 migr -0.130 0.982 181.859 0.323 2.599 brideprice 0.013 0.007 395.386 0.931 3.971 nuclearfam 0.036 0.032 130.422 0.858 4.994 pctFemPolyg 0.004 2.233 396.586 0.136 4.154 > r2 R2:final model R2:IV(distance) R2:IV(language) 0.7410985 0.9817955 0.9538128

6. Significant Ones 754. Wife-Beating : coef Fstat ddf pvalue VIF (Intercept) 6.771 21.208 191450.71 0.000 NA fyll -3.496 16.528 256429.91 0.000 1.193 fydd 0.663 7.158 395480.74 0.007 1.213 roots -0.250 4.243 137572.27 0.039 1.413 cereals -0.274 7.336 807071.90 0.007 1.546 superjh 0.119 9.039 20485.63 0.003 1.183 pctFemPolyg 0.003 3.779 26892.49 0.052 1.165 I did find several significant variables but I wanted to know what other variables would lead to wife beating.

7. Article  “Women’s Status and Mode of Production” by Lewellyn Hendrix and Zakir Hossain  “The Relationship Between Male Dominance and Militarism:Quantitative Tests of Several Theories” by Andrew R. Hoy  “A General Theory of Gender Stratification” By Rae Lesser Blumberg Reading these articles, they talk a lot about male dominance and how it relates to male and female relationship which would include wife beating. Many of the issues discussed were similar to what I had in mind. So I picked some other variables to test out…

8. NEWLY reformatted by DRW: IN COURIER, FROM EDUMOD-18 (Intercept) 8.375 30.828 3281.511 0.000 NA Example of a crosstab: fyll -4.218 24.327 5069.418 0.000 1.331 No Hunting Hunting fydd 0.766 11.177 54241.457 0.001 1.196 No Wife Beating 2 12 roots -0.323 7.525 3299.034 0.006 1.494 Wife Beating 27 29 cereals -0.385 13.531 7519.183 0.000 1.877 93% 71% livingnew -0.070 3.937 207.998 0.049 1.158 Fisher exact p=.03 techspecial 0.068 4.645 14160.229 0.031 1.379 It was surprising to me that hunt -0.095 11.334 7697.674 0.001 1.307 hunting societies had less wife > r2 beating (71%) than others (93%) R2:final model R2:IV(distance) R2:IV(language) but also surprising to see so 0.4506234 0.9847481 0.9552661 much (80%) wife beating! > ccc Fstat df pvalue RESET 7.942 4269.387 0.005 Wald on restrs. 3.245 39.497 0.079 NCV 10.443 3988.997 0.001 SWnormal 5.971 957.688 0.015 lagll 0.136 618237.992 0.712 lagdd 0.193 469070.437 0.661 Old (saved as a picture, impossible to format) coef Fstat ddf pvalue VIF (Intercept) 8.686 32.427 1540.501 0.000 NA fyll -4.368 26.005 2307.377 0.000 1.338 fydd 0.749 11.026 75808.371 0.001 1.180 roots -0.305 7.182 84131.525 0.007 1.480 cereals -0.370 12.918 41867.594 0.000 1.876 livingnew -0.078 5.109 325.540 0.024 1.189 techspecial 0.067 4.448 3421.509 0.035 1.378 hunt -0.093 10.780 2375.331 0.001 1.306 Restricted Model (xR results) My Crosstabs are in my EduMod…

9. Significance with Wife beating: Neg. Coeff  fyll (language)  MAJOR CROP TYPE: Roots or tubers Cereal Grains  Dependence on Hunting  Living Arrangements for Newlyweds Pos. Coeff  fydd (distance)  Technological Specialization Ex. If there is more dependence on Hunting, it could mean that men are hunting more and would lower the chance of wife beating because they have already established their status. Ex. When men are specialized into a type of job role like: smiths, weavers, potter etc. there is a higher chance of wife beating, this could be the case because they don’t feel like the job gives them power or status thus must maintain it by hitting their wife.

10. Evaluation I am planning to expand this research by using these variables and do more research on the topic of “wife beating”. Also try to get some more information as to why the variables “Roots and Cereal” are significant as negative correlates of wife beating.