Material Resource Investments at the Time of Marriage: Biological, Social, and Ecological Perspectives By Brad R. Huber Presented at the 41st annual meetings.

Slides:



Advertisements
Similar presentations
Overview of Techniques Case 1 Independent Variable is Groups, or Conditions Dependent Variable is continuous ( ) One sample: Z-test or t-test Two samples:
Advertisements

Childbearing Intentions and Attitudes Towards Children among Childless Sexual-Minority and Heterosexual Men and Women. Nola du Toit Department of Sociology.
Infidelity in Heterosexual Couples: Demographic, Interpersonal, and Personality-Related Predictors of Extradyadic Sex Kristen P. Mark, M.Sc., 1 Erick Janssen,
Value on Virginity Property Schlegel. Schlegel uses HRAF Cross-cultural comparison Standard ethnographic sample Statistical correlation of cultural features.
Chapter 8: Prediction Eating Difficulties Often with bivariate data, we want to know how well we can predict a Y value given a value of X. Example: With.
CTS401 ANALYZING AND INTERPRETING DATA FROM THE REVISED CONFLICT TACTICS SCALES AND THE INTERNATIONAL DATING VIOLENCE STUDY Murray A. Straus Family Research.
Econ 140 Lecture 151 Multiple Regression Applications Lecture 15.
Brad R. Huber’s Summary of Menelaos Apostolou’s "Sexual selection under parental choice: The role of parents in the evolution of human mating." Evolution.
Lindsay Chase-Lansdale, Andrew Cherlin and Kathleen Kiernan
QUANTITATIVE DATA ANALYSIS
Discriminant Analysis To describe multiple regression analysis and multiple discriminant analysis. Discriminant Analysis.
Multiple Regression Involves the use of more than one independent variable. Multivariate analysis involves more than one dependent variable - OMS 633 Adding.
CTS401 ANALYZING AND INTERPRETING DATA FROM THE REVISED CONFLICT TACTICS SCALES AND THE INTERNATIONAL DATING VIOLENCE STUDY Murray A. Straus Family Research.
Statistical Analysis SC504/HS927 Spring Term 2008 Week 17 (25th January 2008): Analysing data.
CHAPTER 30 Structural Equation Modeling From: McCune, B. & J. B. Grace Analysis of Ecological Communities. MjM Software Design, Gleneden Beach,
Elaboration Elaboration extends our knowledge about an association to see if it continues or changes under different situations, that is, when you introduce.
Correlation 1. Correlation - degree to which variables are associated or covary. (Changes in the value of one tends to be associated with changes in the.
Multiple Regression Research Methods and Statistics.
10. Introduction to Multivariate Relationships Bivariate analyses are informative, but we usually need to take into account many variables. Many explanatory.
Review Regression and Pearson’s R SPSS Demo
Topics: Significance Testing of Correlation Coefficients Inference about a population correlation coefficient: –Testing H 0 :  xy = 0 or some specific.
Human Mating Strategies. Some relevant facts: 1. Female investment in offspring – very high Male investment in offspring – variable 2. Reproductive life.
@ 2012 Wadsworth, Cengage Learning Chapter 5 Description of Behavior Through Numerical 2012 Wadsworth, Cengage Learning.
ASSOCIATION BETWEEN INTERVAL-RATIO VARIABLES
Correlation and Regression
Correlation Indicates the relationship between two dependent variables (x and y) Symbol: r (Pearson correlation coefficient) -1< r < 1.
Evidence-Based Medicine 3 More Knowledge and Skills for Critical Reading Karen E. Schetzina, MD, MPH.
Agenda Review Association for Nominal/Ordinal Data –  2 Based Measures, PRE measures Introduce Association Measures for I-R data –Regression, Pearson’s.
Math 260 Final Project //name //major //picture (optional)
Correlation and Regression PS397 Testing and Measurement January 16, 2007 Thanh-Thanh Tieu.
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
Chapter 3 Section 3.1 Examining Relationships. Continue to ask the preliminary questions familiar from Chapter 1 and 2 What individuals do the data describe?
1 Further Maths Chapter 4 Displaying and describing relationships between two variables.
Copyright © 2010 Pearson Education, Inc Chapter Seventeen Correlation and Regression.
Production Planning and Control. A correlation is a relationship between two variables. The data can be represented by the ordered pairs (x, y) where.
Panel Study of Entrepreneurial Dynamics Richard Curtin University of Michigan.
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Do Long-Lived Individuals Maintain Their Capacity for Well-Being Over Time? 2-Year Longitudinal Analyses from the Chinese Longitudinal Healthy Longevity.
Introduction to Correlation Analysis. Objectives Correlation Types of Correlation Karl Pearson’s coefficient of correlation Correlation in case of bivariate.
Qatar World Health Survey Socio demographic Risk Factors Morbidity Health State Valuation Health System Responsiveness.
Writing up results. Results are divided into two main sections, usually Descriptive Statistics – Include frequencies for nominal/categorical variables.
Marriage and fatherhood associated with lower testosterone in males Gray, P. B., Kahlenberg, S. M., Barrett, E. S., Lipson, S. F. & Ellison, P. T. (2002).
10. Introduction to Multivariate Relationships Bivariate analyses are informative, but we usually need to take into account many variables. Many explanatory.
10. Introduction to Multivariate Relationships Bivariate analyses are informative, but we usually need to take into account many variables. Many explanatory.
Scatter Plots. Scatter plots are used when data from an experiment or test have a wide range of values. You do not connect the points in a scatter plot,
General and Feeding Specific Behavior Problems in a Community Sample of Children Amy J. Majewski, Kathryn S. Holman & W. Hobart Davies University of Wisconsin-Milwaukee.
The Effect of Social Media Use on Narcissistic Behavior By Mariel Meskunas.
Biostatistics Regression and Correlation Methods Class #10 April 4, 2000.
Kinship, Family, and Marriage
The influence of forgetting rate on complex span and academic performance Debbora Hall, Chris Jarrold, John Towse and Amy Zarandi.
Chapter 8 Multivariate Regression Analysis 8.3 Multiple Regression with K Independent Variables 8.4 Significance tests of Parameters.
Unit 4 LSRL.
LSRL.
Least Squares Regression Line.
Dr. Siti Nor Binti Yaacob
Chapter 5 LSRL.
APPROACHES TO QUANTITATIVE DATA ANALYSIS
Chapter 3.2 LSRL.
Dr. Siti Nor Binti Yaacob
Krystle Lange & Regan A. R. Gurung University of Wisconsin, Green Bay
Chapter 9, Marriage, Family, and Residence
Two Way ANOVAs Factorial Designs.
Inferential Statistics
Least Squares Regression Line LSRL Chapter 7-continued
Marriage.
Chapter 5 LSRL.
Chapter 5 LSRL.
Chapter 5 LSRL.
Fig. 3 Plumage brightness (PC1) for each sex in relation to morphological, ecological, and behavioral traits. Plumage brightness (PC1) for each sex in.
Presentation transcript:

Material Resource Investments at the Time of Marriage: Biological, Social, and Ecological Perspectives By Brad R. Huber Presented at the 41st annual meetings of the Society for Cross-Cultural Research (SCCR), February 20-25, 2013, Mobile, Alabama.

Data Collection: Data on first marriages in rural settings. The most common type of marriage HRAF Probability Sample (60 Societies from Around the World)

Eleven (11) different types of marriage transactions were identified. Bride Wealth, for example, is when the groom, groom's parents, or groom's relatives make a non- food, material transfer to the bride's parents or relatives. For each type, I calculated the amount of material resources individuals: Provide Receive

Net Level of Marriage Investment the net amount of material resources that the bride and groom, and both sets of parents accrue or expend at the time of marriage. the investment received - the investment provided net marriage investment level Net investment scores can be positive or negative. A negative score, for example, means that an individual provided a greater amount of resources than he or she received.

6

Independent Variables Paternal Certainty Level based on four items: (a) frequency of premarital (b) frequency of extramarital sex (c) strength of the sanctions against premarital, and (d) extramarital sex. Each scored from 1 to 5 Scores can range from 4 to 20, with 20 representing very high paternal certainty.

8

Polygyny Rate. The polygyny rate variable is Douglas White’s Standard Polygamy code. Scores for this variable can range from: 0 to 4, with 4 representing “20% or more of married males” in a polygynous marriage.

10

Pathogen Stress. The level of pathogen stress was coded by Bobbi Low (1988). A total of seven pathogens (leishmanias, trypanosomes, malaria, schistosomes, filariae, spirochetes, and leprosy) were each rated on a 3-point scale for frequency The individual scores were summed for a total pathogen stress score.

Relative Marriage Age The author coded the women’s and men’s mean age at the time of their first marriage. Some ethnographers report average first marriage ages but most specify an age range for men’s and women’s first marriages, e.g., years old.

Divorce Rate Originally coded by Broude & Greene (1983) for the 186 societies of the SCCS. 40 societies are found in both SCCS and the 60 society HRAF probability sample. The author coded the remaining societies of the probability sample, and Reversed Broude & Greene’s original values such that they range from “1” to “5”, Where “5” indicates divorce is “universal or almost universal”.

16

Predictions: Polygyny Rate Pathogen Stress Relative Ages of Spouses Paternal Certainty Negatively Correlated with Groom’s and Groom’s Parents’ Net Investment Positively Correlated with Bride’s and Bride’s Parents’ Net Investment Divorce Rate: Correlated; Direction ???

Table 4; The Bivariate and Partial Correlations of the Predictors with Groom’s Net Investment Predictors Bivariate Correlation Partial Correlation Divorce Rate Polygyny Rate-.59**-.55** Pathogen Stress Relative Ages of Spouses-.33*-.22 Paternal Certainty Multiple Regression Analyses

Table 5 The Bivariate and Partial Correlations of the Predictors with Groom’s Parents’ Net Investment Predictors Bivariate Correlation Partial Correlation Divorce Rate Polygyny Rate.18.37* Pathogen Stress Relative Ages of Spouses Paternal Certainty-.50**-.53**

Table 6 The Bivariate and Partial Correlations of the Predictors with Bride’s Net Investment Predictors Bivariate Correlation Partial Correlation Divorce Rate Polygyny Rate ** Pathogen Stress.32*.43* Relative Ages of Spouses Paternal Certainty.35*.41*

Table 7 The Bivariate and Partial Correlations of the Predictors with Bride’s Parents’ Net Investment Predictors Bivariate Correlation Partial Correlation Divorce Rate Polygyny Rate.57**.60** Pathogen Stress Relative Ages of Spouses Paternal Certainty-.31*-.45*