SCCS factors of culture

Introduction to single factor dimensions of culture

See, for example, <some dimensions of culture>. These were picked because they had previoulsy been identified as having multiple measures that correlated to support the idea of identifying a reliable dimension of variation. For cross-cultural readings see http://eclectic.ss.uci.edu/~drwhite/courses/JSTORSearchResults2.htm

SINGLE FACTOR MODELS OF CULTURE are identified by clusters of correlated variables that form a single dimension of covariation such as these. If we take the dimension of external war, for example, and search the <SCCS index of variables>, we can find seven entries under the searching for "warfare" that are clearly external war (693, 774, 2650, 1746, 1747, 1776), each having from 72-93 cases coded, and two possibly external war variables (679, 133 cases; 111, 53 cases). If we search fpor the keyword "External" we find two more: 892, "Attacking" with 157 cases, and 893, "Attacked" with 153 cases. Along the way we note one variable with "Interethnic violence" and searching again fine variable 1778 for interethnic "Attacking" with 89 cases coded.

When we select all these variables for "Data Reduction" in Spss using the "pairwise" option, we find that they do not form a single factors, and we have to decide which to eliminate to get a <single factor structure>.

Katy Carew found and quite liked this site by Prof. Dave Garson about factor analysis: http://www2.chass.ncsu.edu/garson/PA765/factspss.htm

The following lists of variables under our <dimensions of culture> topics are preliminary results of searches for single factors. For some other factors, see <single factor tutorial>.

Factor 1 ExternalWar130: Doug White Results using R

1. v893 FREQUENCY OF EXTERNAL WAR - BEING ATTACKED
2. V894 FORM OF MILITARY MOBILIZATION
3. v892 FREQUENCY OF EXTERNAL WAR - ATTACKING N=134 p=.002
4. V900 MILITARY EXPECTATIONS II-STATE N=128 p=.002
5. V895 DECISION TO ENGAGE IN WAR N=134 p=.001

ExternalWar50: 774 780 783 892 894 903 (weak: 877) pairwise 50 cases CORRELATES with 1764 893 weakly 860 876 878 NOT with 1655 1656

• Alternate Factor 1 ExternalWar13 774 780 783 892 894 903 1764 pairwise 13 (too few) cases
• Are these missing: 693 ? 1650 ? 1747 ? 1746 ? 1778 ? Or missing too many cases?

Jason Harmon (N=53) (+ below = new finding) (-v=Neg Corr w factor) (Low)R=Code categories run opposite to variable label LOW EXTERNAL WAR FACTOR Factor Low=ExtWar Hi=NoExtWar

• v774 (Low)R External Warfare
• v775+ Compliance of individuals w/ community norms (see SCCS_test_of_hypotheses#Sample_table for a very interesting cross-tabulation result for this variable with external war)
• v780 Hostility toward other societies
• v783 Acceptability of violence toward people in other societies
• v892 (Low)R Frequency of External War - Attacking

Correlates

• v767+ (Low)R Conflict (Soc or Pol) in the local community (N=53)
• v787+ (Low)R Contact with other societies (N=53)
• v894+ (Low)R Form of Military Mobilization (N=51)
• -v679+ Warfare or Fighting (with factor N=50)
• -v666+ Moderate or Frequent Interpersonal Violence (N=49)
• v903 (Low)R Prestige Associated with Being a Soldier or Warrior (with factor N=49)
• -v664+ Ideology of Male Toughness (N=44)

Factor 2 InternalWar46: 891 1969 1776 1777 pairwise 42 cases CORRELATES with 773 1649 1665 1764 NOT with 1748 1749 1773

Factor 3 MaleAggression50: 664- 665 666 667 668 669 CORRELATES with IntWar46Bands NOT with ExtWar50Bands and NEGATIVELY with FemalePower

Factor 4 FraternalInterestGroups60: 562 564 568 569 570 571 572 pairwise 60 cases CORRELATES with ExtWar50Bands 752 765 561 563 565 566 567 860 86 862 863 864 865 866 867 869 871 872 874 NOT with IntWar46Bands

Factor 5 FemalePower107: 657 658 659 660 661 NOT 662 Factors1-3

Factor 6 Factor 1,2,3 Combined NOT 4,5

Factor 7 Modernization102: 149,151-153,155-158,Statehood Index CORRELATES with Agricultural Systems, Land Transport, Fixity of Settlements, NOT with Factors 1,2(v891),3,4,5

Factor 8 Political Integration82: 157 237 158 777 156 90 153 151 152 155 776 19 1740 Levels of Political Hierarchy (=237) correlates at .778 but leaves only 52 cases 1738 Presence of Formal Education Within Local Community correlates at .711 but leaves only 45 cases. 711 Societal Complexity (Guttman Scale - Freeman & Winch 1957) correlates at .856 but leaves only 21 cases.

alternate to Factor 1 ExternalWar13 774 780 783 892 894 903 1764 pairwise 13 (too few) cases

Examples of Factors for exploration 2008

Male Aggression 2008

Austin Lin

• 664. IDEOLOGY OF MALE TOUGHNESS
• 665. MALE SEGREGATION: ONE OR MORE PLACES WHERE MALES CONGREGATE ALONE, OR
• 666. MODERATE OR FREQUENT INTERPERSONAL VIOLENCE
• 667. RAPE: INCIDENTS REPORTS, OR THOUGHT OF AS MEANS OF PUNISHMENT WOMEN, OR
• 668. AT LEAST SOME WIVES TAKEN FROM HOSTILE GROUPS

THESE ADD UP TO 669 BUT 669 and 670 SHOULD NOT BE INCLUDED

```669.  MALE AGGRESSION GUTTMAN SCALE CONSTRUCTED FROM 664-669
670.  COMPOSITE OF MALE DOMINANCE CONSTRUCTED FROM 663 PLUS 669 (657-669)
```
• 2.9 58% Listwise

Pathogen Stress

Steve Sanchez

• 1254. TRYPANOSOMES
• 1255. MALARIA
• 1256. SCHISTOSOMES
• 1257. FILARIAE
• 1258. SPIROCHETES
• 1259. LEPROSY
```1260.  TOTAL PATHOGEN STRESS (exclude this as it adds up the others)
```
• 2.7 55% listwise (not 3.7 61% ??)

Kimberly Sethavanish

• 529. INITIATION OCCURRENCE: BOYS
• 542. PRIMARY PHYSICAL COMPONENTS: GIRLS
• 543. SECONDARY PHYSICAL COMPONENTS: BOYS
• 547. SECONDARY COGNITIVE OR PERFORMANCE COMPONENTS: BOYS

2.99 75% ??

• 783
• 903
• 905
• 907
• 3.1 77% ??

Violence against Outsiders

Nick Mosey

• 780. (LOW) HOSTILITY TOWARD OTHER SOCIETIES (VAR LABEL REVERSED)
• 783. (UN)ACCEPTABILITY OF VIOLENCE TOWARD PEOPLE IN OTHER SOCIETIES
• 905. REWARDS (VAR LABEL REVERSED) (SPECIAL GIFTS, PRAISES, OR CEREMONIES) for a man who killed an enemy
• 907. VALUE OF WAR: VIOLENCE/WAR AGAINST NON-MEMBERS OF THE GROUP
• 3.4 68% Exclude pairwise

Female Autonomy (Economic/Marriage)

Kimberly Ling

• 657. FLEXIBLE MARRIAGE MORES (DIVORCE FOR BOTH MEN AND WOMEN: OR MILD
• 658. FEMALES PRODUCE GOODS FOR NONDOMESTIC DISTRIBUTION
• 659. DEMAND FOR FEMALE PRODUCE BEYOND HOUSEHOLD
• 660. FEMALE ECONOMIC CONTROL OF PRODUCTS OF OWN LABOR
• 661. FEMALE POLITICAL PARTICIPATION, AT LEAST INFORMAL INFLUENCE
• 2.7 54% ??

Famine

YooNa Kim

• 571. RESOURCE BASE
• 1265. OCCURRENCE OF FAMINE
• 1267. SEVERITY OF FAMINE
• 1268. PERSISTENCE OF FAMINE
• 1269. RECURRENCE OF FAMINE
• 1270. CONTINGENCY OF FAMINE
• 4.1 70% ??

Socially organized aggression

Albert Jin

• 1665. INDIVIDUAL AGGRESSION - HOMICIDE (CODES NOT ORDERED)
• 1666. INDIVIDUAL AGGRESSION - ASSAULT (CODES NOT ORDERED)
• 1675. SOCIALLY ORGANIZED HOMICIDE (CODES NOT ORDERED)
• 1676. SOCIALLY ORGANIZED ASSAULT (CODES NOT ORDERED)
• 3.2 81% ??

Principal non-parental caretaker

Victoria Piar

• 365. PRINCIPAL CATEGORY OF NON-PARENTAL CARETAKER: EARLY BOY
• 366. PRINCIPAL CATEGORY OF NON-PARENTAL CARETAKER: EARLY GIRL
• 367. PRINCIPAL CATEGORY OF NON-PARENTAL CARETAKER: LATE BOY
• 368. PRINCIPAL CATEGORY OF NON-PARENTAL CARETAKER: LATE GIRL
• 3.2 80% listwise
• 3.2 79% pairwise
• 3.0 74% replace with mean

Settlement fixity

Vivian Liu

• 61. FIXITY OF SETTLEMENT
• 150. SCALE 2- FIXITY OF RESIDENCE
• 234. SETTLEMENT PATTERNS
• 705. SETTLEMENT TYPE
• 3.4 84% listwise
• 3.4 85% pairwise
• 3.1 78% replace with mean

Cultivation

Marlo Pabon

• 3. AGRICULTURE- CONTRIBUTION TO LOCAL FOOD SUPPLY
• 232. INTENSITY OF CULTIVATION
• 234. SETTLEMENT PATTERNS
• 235. MEAN SIZE OF LOCAL COMMUNITIES
• 246. SUBSISTENCE ECONOMY
• 3.6 73% listwise
• 3.7 73% pairwise
• 3.6 71% replace with mean

Hunting v Agriculture

Janling Liu

• 9. HUNTING- CONTRIBUTION TO FOOD SUPPLY
• 204. DEPENDENCE ON HUNTING
• 246. SUBSISTENCE ECONOMY
• 727. IMPORTANCE OF AGRICULTURE IN SUBSISTENCE, INCLUDING GARDENING
• 3.2 79% Listwise
• 3.1 78% pairwise

Soc&Indiv Aggression

Jon Park

• 1666. INDIVIDUAL AGGRESSION - ASSAULT (CODES NOT ORDERED)
• 1667. INDIVIDUAL AGGRESSION - THEFT (CODES NOT ORDERED)
• 1676. SOCIALLY ORGANIZED ASSAULT (CODES NOT ORDERED)
• 1677. SOCIALLY ORGANIZED THEFT (CODES NOT ORDERED)
• 3.2 80% listwise
• 3.0 76% pairwise
• 24. 60% replace with mean

Aggression training in children

Theresa Hoang

• 298. AGGRESSION: EARLY BOY
• 299. AGGRESSION: EARLY GIRL
• 300. AGGRESSION: LATE BOY
• 301. AGGRESSION: LATE GIRL
• 3.3 83% listwise
• 3.3 82% pairwise
• 3.1 79% replace with mean

Examples of Factors for exploration 2007

Religion and Expressive Culture

• 1188 Evil Eye Scaled Rating
• 884 Priest
• 238 High Gods (moral)
• 713 Religion (Non-Classical)
• Islam - found toward the end of the dataset
• GamesOfStrategy - found toward the end of the dataset

Fraternal Interest Groups and Expressive Culture

• 1188 Evil Eye Scaled Rating
• 884 Priest
• Islam
• GamesOfStrategy
• 562 Circumcision
• 568 Compensation Demands
• 570 Fraternal Interest Group Strength
• 571 Resource Base
• 572 Residence Pattern

Gender and Politics

• 591 Ownership or Control of use of Dwellings
• 592 Control of the Labor done by Men
• 669 Male Aggression Guttman Scale
• 793 Female Participation in Public Political Arenas, Relative to Males
• 794 Female Participation in Private Political Arenas, Relative to Males

Men and Boys

• 1760 Frequency of Interactions Between Boys (early childhood) and Male Adults
• 1675 Corporal Punishment of Boys in Late Childhood
• 1776 Socially Organized Homicide
• 664 Ideology of Male Toughness

• v774 External Warfare
• v270 Class Stratification
• v1764 Reaction of Socializing Agents Towards Violent Behavior of Boys in Late Childhood

Physical Violence and type II military expectations

• 895 Decision to Engage in War
• 1770 Attitude Towards Physical Violence Against Members of Other Ethnic Groups
• 1772 Hostility Towards Other Ethnic Groups
• 900 Military Expectations II (SUBJUGATION, TRIBUTE)

Type I State military expectations

• 899 Military Expectations I
• 908 Military Success: Is Political Community/ Cultural Unit Winning or Losing in the Long Run
• 1725 Possibility of Peaceful Territorial Expansion
• 1773 Prestige of Warriors

Father and child

• v54 Father's closeness
• v369 or 379 (use one only) Sex of parental caretaker
• v991 Father's caretaking role
• v614 Male caretaking role

Annie Dihn correlates

• v614 Final authority over upbringing (r=-.44)
• v353 Sex of parents in residence: Early boy (r=-.31)

Male dominance, extended family, patri-descent

John Reitzell (N=53)

• v210 Domestic organization
• v211 Domestic organization (Composition:Extended family)
• v212 Domestic organization (Marital Composition:Polygyny)
• v621 View that men dominant wives (N=53)
• v53 Role of Father, Late Childhood (Distant) (N=53 joins factor, weak)
• Correlates: Katy Ritter (paper 3)
• UnequalGender (Ritter factor)
• Patrilocal-UnequalGender
• v54 Role of Father, Early Childhood (Distant)
• v626 Belief that women are inferior to men

Setareh Faghani

• v210 Domestic organization
• v211 Domestic organization (Composition:Extended family)

v80 Size of Family (N=185)

• v621 View that men dominant wives (N=63)
• v626 Belief that women are inferior to men (N=92)

Sandy Vuong: correlate v79 Polygamy (>20%) v169 Extramarital sex: Double Standard for men (categorical Fisher=.004)

Monogamy, dowry

Ngoc Lena Phung

• v605 Dowry
• v606 Monogamy Pfd
• v607 Monogamy
• v629 Female Kin Power (N=76)
• Correlates
• v628 (Fe)male Property Control
• v631 High Value of Women's Labor

Divorce

• v743 Attitute toward Divorce
• v744 Freq of Divorce
• v667 Ease of Divorce

Correlates: Grounds for Divorce

• v1136 Infidelity in Polygamy
• v1144 Sterility
• v1151 Personality: Mutual Consent

v754 Male Divorce with Grounds only

Taboos

John Reitzell-drw (factor 55%) 1.6

• v565 Menstrual Segregation
• v599 Menstrual Taboos
• v561 Menarcheal Ceremonies

Correlates:

• v9 % Hunting

v671 Number of Menstrual Taboos (fits factor but does not correlated with hunting)

• v

Agriculture/Animal Husbandry/Population

Laura Chamberlain

Agriculture Factor

• v108 Land Clearance (present or not present)
• v109 Soil Preparation (present or not present)
• v110 Planting (present or not present)
• v111 Crop Tending (present or not present)
• v112 Harvesting (present or not present)

Correlates

• v61 Fixity of Settlement
• v62 Compactness of Settlement (Not correlated)
• v63 Community Size
• v64 Population Density
• v66 Large or Impressive Structures
• v234 Settlement Patterns
• v235 Mean Size of Local Communities
• Animal Husbandry Factor
• v114 Large Domestic Animals (present or not present)
• v115 Milking (present or not present)
• v244 Predominant Type of Animal Husbandry
• v245 Milking of Domestic Animals

Correlates

• v61 Fixity of Settlement
• v62 Compactness of Settlement (Not correlated)
• v63 Community Size
• v64 Population Density
• v66 Large or Impressive Structures (Not correlated)
• v234 Settlement Patterns
• v235 Mean Size of Local Communities

Other

See clusters of variables such as those at http://eclectic.ss.uci.edu/~drwhite/courses/stdsvars.html

Measuring modernization (=complexity?)

Read the Trevor Denton article on Modernization (Complexity Revisited): http://ccr.sagepub.com/cgi/content/abstract/38/1/3 This topic is especially interesting because of the article called “Measurement of Cultural Complexity” that presented these variables and constructed a sum of these variables as a single measure of societal complexity. Inside the article, however, the authors identified their choice of variables on the basis of ones whose rank order categories brought us closer to modernization, without any necessary rise in complexity. Denton’s article takes off from there.

Modernization Scale and Social Motivation

• ModernizationFactor 149|151-153|155|158
• GamesOfChance (thought to favor turntaking of political leadership or election)
• sexrestr
• selfrely
• obedience

Modernization Scale elements and Differentiation

Alex Chan single factor 56% (N=186)

• 149 Writing and Records
• 151 Agriculture
• 152 Urbanization
• 153 Technological Specialization
• 155 Money
• 156 Density of Population
• 157 Political Integration
• 158 Social Stratification
• obedience

Correlates

• v774 External war (r=-.27)
• v895 Decision to engage in war (r=-.36)

Stratification

Nate Adamski single factor 66% (N=186)

• 152 Urbanization
• 153 Technological Specialization
• 156 Density of Population

correlates

• v158 Social Stratification (r=.64)
• v1721 Number of Rich (r=.33)
• v1722 Number of Poor (r=.30)

Inequality

• 1740 Levels of Political Hierarchy
• 1721 Extent of Burden Caused by Tribute Payments or Taxation
• 1723 Number of Rich people (wealthy)
• 1737 Number of Poor
• 1724 Number of Dispossessed

Political Differentication

• 776 formal sanctions and enforcement for community decisions
• 777 Enforcement specialists (police, tax collectors)
• 784 Taxation paid to community
• 237 Jurisdictional Hierarchy Beyond Local Community
• 90 Police
• 19 Preservation and Storage of Food
• 63 Community Size
• 158.1 Sum of Cultural Complexity (v149-158)

Modernization and Political Complexity

• 157 Political Integration
• 237 Jurisdictional Hierarchy Beyond Local Ccommunity
• 158 Social Stratification
• 784 Taxation paid to community
• 156 Density of Population
• 90 Police
• 153 Technological Specialization
• 151 Agriculture
• 152 Urbanization
• 155 Money
• 777 Enforcement specialists (police, tax collectors)
• 19 Preservation and Storage of Food

Other ideas for dimensions

Small Scale Loyalties and Compliance with Norms

Variables suggested by Dominic D. P. Johnson God’s Punishment and Public Goods

• 779 Loyalty to the wider society
• 775 Compliance of individuals w/ community norms
• 778 Loyalty to the local community
• 63 Community Size
• (these have been tested for single-factor - may want to add other variables)
•  ? 18 Credit Source

Family Trust and Affection

Christine Black N=59 Factor

• v335 Trust
• v492 Warmth and Affection
• v53 Role of Father, Infancy
• v54 Role of Father, Early Childhood (N=59
• v59 General Indulgence, Early Childhood (N=53)

Correlates

• v1759 Affection during early childhood (N=28)
• v336 Honesty (N=25)
• Fortitude (N=47)
• Competitiveness (N=44)

MORE CORRELATES - paper 3 > I wanted to show that a person who does not grow up in a loving environment could shows signs of aggressive behavior later in life.

• v1665 Individual Aggression - Homicide
• v1772 Hostility Towards Other Ethnic Groups
• v1778 Frequency of Intraethnic Violence
• v892 Frequency of External War - Attacking
• (now figure out how to make graphs in which the categorical variables are on the x axis and the factor score averages are plotted on the y axis.)

Scarification

• v1692
• v1693
• v1694
• v1695

N=126

• v170 Frequency of Extramarital Sex - Males (N=32) fit the factor structure

Sexual Permissiveness89

There are quite a few variables under Sexual Attitudes & Behavior 159,-178, 240,282, 563,-567, 596,-602, 671,672, 827,-830, 938,-940, 958,-967

• Colin Marshall single factor 83% (N=89)
• v165 Premarital Sex Attitudes - Female
• v167 Frequency of Premarital Sex-Female
• v333 Sexual restraint, late girl
• Sandy Vuong, Shawn Gillespie's factor (doctored by DRW) are the only ones that work here
• v165 Premarital Sex Attitudes - Female
• v166 Frequency of Premarital Sex - Male
• v167 Frequency of Premarital Sex-Female
• v282 Norms of Premarital Sex-Girls

N=77

• Correlates
• v739 (r=.46) Arranged Marriage (N=73) weakly fits the factor
• v172 (r=.43) Wifesharing (N=55) weakly fits the factor
• v170 (r=.49) Frequency of Extramarital Sex - Male (N=31)
• v173 (r=-.36) Classical Religion (N=38)
• v176 (r=.50 Homosexuality (N=24)
• v242 (r=-.25) Segregation of Adolescent Boys (N=69) does NOT fit the factor
• v161 r=-.42) Sex believed dangerous
• Other correlates Shawn Gillespie (factor includes v739, N=40, v166 correlate only)
• v667 Rape
• v827 Sexual Expression in Adolescents
• v43 COVERING GENITALS- AGE
• v52 NON-MATERNAL RELATIONSHIPS, EARLY CHILDHOOD
• v163 Age for clothing - Male
• v164 Age for clothing - Female
• v170 Extramarital sex
• v172 Wifesharing
• v174 Rape

Homosexuality

MeanGene 48% single factor (N=33)

• v176 (-) Homosexuality disapproved
• v177 Homosexuality Frequency
• v242 Segregation of Adolescent boys

Unequal Gender Differentiation63 ?

Karina Ritter

• v626 Belief that women are generally inferior to men
• v625 High value on males being aggressive, strong, and sexually potent
• v661 Low) Female Political Participation, at least informal influence
• Patrilocal (N=63 42% variance)
• or
• v630 Value of life scale (N=63; 39% variance without Patrilocality)
• (weak) v169 Extramarital Sex (N=43)

There is strong mix of elements in Karina's factor (with v630), good sample size, and surprisingly strong correlations with four other variables. Only one of these (v169) fits into a single factor structure but pulls the factor in a slightly different direction while losing sample size, so you lose significant correlations with other variables. The exercise works fine as it stands. But coding for Patrilocality as a replacement for v630 gives a stronger factor and adds to the negative correlations with sexual permissiveness. Strong correlates:

• v165 Premarital Sex Attitudes - Female (N=50)
• v169 Extramarital Sex (N=43)
• v168 Initiator of Premarital Sex (N=11)
• v174 Frequency of Rape (N=11)

Because the factor variables are so diverse it is not a single variable but an assemblage that might be called Unequal Gender Differentiation that disadvantages women and seems in its sexual correlates to involve sexual exploitation, but at this stage this is still highly inferential. Before using a rather value-laden interpretation, such as this, however, the cross-tabs among these variables need to be carefully checked. The issue of exploitation (Derek Freeman) versus permissiveness (Mead) was exactly the basis of differing interpretation that led to the Freeman-Mead controversy.

===Paying attention to Coding Categories: Introducing Complexity, Control Variables, Mapping Variables, and Factor Recodes for Mapping and Crosstabs===

v625 626 630 661 in the standard cross cultural database. Low values are high inequality, high values more gender equality.

Learn how to Recode single-factors or Spss variables with many coding values if you want to map your factors or equally complicated variables. If you want to make a map like the one above with a binned variable (or any variable with few categories), follow the instructions under the next heading.

We now come to one of the most interesting parts of cross-cultural studies: Complexity. My simplified stereotypes of what the Unequal Gender Differentiation factor and its correlates MIGHT mean without looking at the actual coding categories for these variables now need to be revised. Premarital sex attitudes (v165), in which low (-) scores are for Permissiveness, for example, is negatively correlated with the factor scores, for which high scores are relative equality or (-)Differentiation/Inequality, so the product of (-)permissiveness(-)inequality(-)correlation equates to the product of three negative signs for the two concepts, permissiveness(-)inequality, which is negative, to permissiveness and inequality are negatively related. Another way of stating this is that the gender inequality/differentiation factor is associated with PROTECTIVENESS for unmarried females, and restrictions against premarital sex. The significance of the correlation is p=.02, with only a 2% chance of occurring at random if all the characteristics were assigned to ethnographic cases randomly.

This is where the complexity comes in, because the ethnographic cases are not random but structured geographic-cultural configurations, but tend to have historical and geographic commonalities. So, let's go further and look at all of Karina's significant correlates, colored in red in the PDF. Applying the rules for MULTIPLICATION OF SIGNS (two negatives make a positive) then male initiators of premarital sex (+ correlation with factor) (+ for high values=men initiate) and (-)Differentiation/Inequality for the factor, we get a (-) for Inequality with male initiators, which gives advantage to females and fits the idea of PROTECTEDNESS for unmarried females. This seems to put us, for this pole of the factor polarity, into the territory of male-dominant female-protected societies such as Islam. Extramarital affairs (- for single standard) and its (+) factor correlation, with (-)Differentiation/Inequality give us Single Standard extramarital correlates for the protected/protection societies.

Complexity includes nonindependence of cases. Is it possible that this polarity is associated for the protected/protection societies with the historical variable ISLAM, which is coded in the database? (NO, actually) Or is it associated with the Fraternal Interest Factor? (Also No). (But it is correlated with large animals!) More fundamentally, if we CONTROL for Islam of Cattle as a third variable, do the factor variable and the sexual variables correlated WITHIN the Islamic societies? WITHIN the non-Islamic societies, or are these contrasts simply the result to divergent branches of major world or local religions? (No, Islam makes no difference and is irrelevant). Islam is a binary variables (and so coded, Cattle (perhaps also other large animals plus salmon fishing, could be recoded as binary), so they are easy to apply as control variables, which we will do to illustrate issues of Cultural Complexity in the third paper assignment where pairs of variables are cross-tabbed and control variables can be utilized. The Fraternal Interest Factor can be dichotomized at its average value and also be used as a control. Lets add complexity by the possibility that, given empirical rejection of the Islam and F-I-Group speculations, there remains the possibility, looking at the median values of this factor for Islamic societies, the role of Islam historically might have been to reduce the gender bias of originally pastoral societies. The map for shown here for the geographic distribution of these factor scores does tend to show regional clustering rather than a single cluster or a random spatial distribution. Red represents greater equality for women (value 7) while dark purple/brown represents more inequality in terms of factor scores.

Now is a good time to review the first of my Lectures: HSC and World Cultures, especially the Complexity in human behavior wiki lecture that deals with complexity. By wiki lectures I mean ones that you can read now at any time in this course, and reread several times later on. They represent the major challenge for what this course is about, and the challenges of the human and social sciences.

Meta Factors: Single-Factors composed of Single-Factors

State System Properties

75% common variance (N=48)

• v Jurisdictional Hierarchy123
• v Modernization102
• v Political Integration82
• v (N=25, weak) Sexual Permissiveness77 (negative)

Correlates

• v Afro Eurasia
• v Old World

Permissiveness and Female Power

N=26

• v Sex_Permissive77
• v Female Power107
• v Unequal Gender (negative)
• v Patrilocal (negative)

Correlates

• v Fraternal Interest Groups (N=12) (Made a new variable for patrilocal residence above)
• v New World

State and Prestate Analogies

These first explorations of single-factors and meta-factors (single-factors of single-factors) show that some analogous features:

• For the "state-building" and some major "pre-state" dimensions there are several (perhaps more) meta-factors
• In each case there are "meta-factors" or single-factor structures linking constituent single-factors
• In each case the factors within the "meta-factors" do not themselves form a single-factor
• These represent, then, dimensions of organization that are correlated, but not unidimensional
• Within factor analysis proper, these would correspond not to multiple orthogonal or independent factors...
• ... but rather multiple factors what can be represented as "rotated" to form interdependent dimensions, each loading of different sets of variables
• The optimal rotated factors tend to recover each of the single-factor structures.

If these represented evolutionary pathways, they would be ones with multiple lines of development, with branching paths and converging paths rather than a ladder of development.

• This pattern is equally strong for the organizational components of state systems, and for prestate systems.
A prestate "meta-factor" structure provides avenues for strengthening female power, male/female inequality, and permissive/strict sexual behaviors, each intercorrelated with the others.
Patrilocality tends to correlate with greater male/female inequality variables (plus a 4th factor with low value given to female life)
Patrilocality substituted for v630 in the GenderInequality63 variable creates stronger correlations with restrictive sexuality (negative of permissiveness)
A state-level "meta-factor" structure provides avenues for strengthening of jurisdictional levels, political integration, and modernization, with branching paths and converging paths

rather than a ladder of development.

Other correlations

Shawn Gillespie

• v892 External war - attacking
• vv33 Childhood pain infliction (r=-.21, p=.006) mild with war

Cornelius Phanthanh

• v892 External war - attacking
• v152 Urbanization (r=-.18, p=.01)
• v153 Technological specialization (r=-.24, p<.001)
• v157 Political integration

Brian Bayless

• v700 Crimes against person punished by govt (not by group or person wronged)
• v891 Frequency of internal war (r=.?? p=??) i.e. basically correlate of feuding

If this is not at the Anth174 site, the right click and save to c:\My Documents\--your name here as subdirectory (make it first)

Galton's problem

Note that none of the single-factor projects involve the problem on <nonindependence (see<Complexity in human behavior>) because they involve measures of the same concept for the same cultures.

Non-linear graphs

In Spss after making factors, try for a pair of factors: Graphs / Chartbuilder / simple error bars

• e.g., Interactive graph + error bar
• use Main menu: Help / Topics / e.g. Graph