Honor (what it means to be a man) and condom use of men

Prevention programs are least succesful in situations in which a person's personal or cultural values prevent them from engaging in safe sex practices (Herdt and Lindenbaum, 1992). These values generally involve misconception of what safe sex means. Some men refuse to wear condoms because doing so would detract from their conception of what it means to be a man.

STUDY TO DO (A MUST): Which Part of Masculinity???

EXAMPLE ABSTRACT:
Two studies distinguished between aspects of traditional masculinity (gender role conformity versus sexism) in predicting sexual risk behavior among heterosexual males. Hostile sexism was associated with sexual risk taking indirectly, through temptation for unsafe sex. Masculinity and benevolent sexism did not predict temptation for unsafe sex.


Run a correlational study:

- Masculine and Feminine Honour endorsement
- Gender role beliefs (gender conformity/equality)
- Sexism (hostile and benevolent sexism)
- masculinity

Which scale predict masculine honour and feminine honour?
- is it Gender role beliefs(gender conformity)? or Sexism? Or both?


- Also test the strength of correlations between honour and ASI in Turkey vs. UK
- Test the strength of correlations between honour and Gender role beliefs in Saudi vs. UK



Gender expression/conformity scales here:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783339/

I had already looked at BSRI (as gender conforming personality traits) and honour!

Conformity to Masculine Norms Inventory. The Conformity to Masculine Norms Inventory (CMNI) measures the extent to which a male individual conforms to traditional masculine norms (Mahalik et al., 2003). The CMNI consists of 94 items, including “It bothers me when I have to ask for help”, “I tend to keep my feelings to myself”, “I like fighting”, and “In general, I must get my way”. The CMNI contains 11 Masculinity Norms subscales: Winning, Emotional Control, Risk-Taking, Violence, Dominance, Playboy, Self-Reliance, Primacy of Work, Power Over Women, Disdain for Homosexuals, and Pursuit of Status. Male participants rated their agreement with items on a 4-point Likert-type scale ranging from 0 (strongly disagree) to 3 (strongly agree). Scores on the CMNI were computed by averaging subscale items, and then summing subscale means for a total CMNI, with higher total scores indicating higher conformity to masculine norms. In developing the scale, Mahalik et al. (2003) found that internal consistency for the Masculinity Norms subscales ranged from .72 to .91, with a Cronbach’s a of .94 for the CMNI Total score, with a sample of 752 men. Scores on the CMNI were related significantly and positively to the Gender Role Conflict Scale and Male Gender Role Stress Scale, indicating strong construct validity (Mahalik et al., 2003). In the present sample, Cronbach’s a was .92, indicating strong internal consistency reliability.

            Conformity to Feminine Norms Inventory. The Conformity to Feminine Norms Inventory (CFNI) measures women’s conformity to an array of traditional feminine norms (Mahalik et al., 2005). The CFNI consists of 84 items, including “Being thin is important”, “I would feel extremely ashamed if I had many sexual partners”, “Caring for children adds meaning to one’s life” and “I try to be sweet and nice”. The CFNI contains 8 Femininity Norms subscales: Nice in Relationships, Thinness, Modesty, Domestic, Care for Children, Romantic Relationship, Sexual Fidelity, and Invest in Appearance. Participants rated their agreement with items on a 4-point Likert-type scale ranging from 0 (strongly disagree) to 3 (strongly agree). Scores on the CFNI were computed by averaging subscale items, and then summing subscale means for a total CFNI score, with higher scores indicating higher conformity to feminine norms. In developing the scale, Mahalik et al. (2005) obtained a Cronbach’s a of .88 for the CFNI Total score, and a range from .77 to .92 for the Femininity Norms subscales, with a sample of 733 women, indicating strong internal consistency reliability. Scores on the CFNI correlated significantly and positively with the Bem Sex Role Inventory Femininity scores and Feminist Identity Composite Passive Acceptance subscale scores, indicating strong construct validity (Mahalik et al., 2005). In the present sample, Cronbach’s a was .87, indicating strong internal consistency reliability.

Comparing 2 correlations

http://psych.unl.edu/psycrs/statpage/biv_corr_comp_eg.pdf

Comparing bivariate correlations across populations Another common question is whether two variables are equally correlated in two different populations. In this example we will ask if the correlation between depression (BDI) and family social support (FASS) is the same for males and females. To do this in SPSS we must first split the file into two subfiles (males and females) and obtain the desired correlation from each subfile. Data  Split File Move the variable or variables into the “Groups Based on:” window and click “OK”. All subsequent analyses we request will be performed and presented separately for each of the resulting groups.

A significance test will require that we find the difference between these two correlations, relative to the expected variability in correlations for this sample size. The common Z-test is useful for this, but assumes that the values being compared are normally distributed, and we know that r is not normally distributed. Fisher, however, determined a way to transform r-values so that they will be normally distributed -- called Fisher's Z-transformation. Z1 - Z2 Z-critical is 1.96 for p < .05 The Z-test is computed as Z = ---------- 2.58 for p < .01 SEZD SEZD =  [1 / (n1-3) + 1 / (n2-3)] On the right is the portion of the FZT program used for Fisher’s Z-test, with the values for this group comparison shown. As with other correlation comparisons, you must decide if you want to test for “correlation differences” (including the sign of the correlations) or the “predictive utility differences’ (using |r| for both correlations). In this case, the results from comparing the “utility” of the predictor for this criterion in the two groups was Z=1.258, p > .05. Remember, the tests are equivalent if the signs of the two correlations are the same. Family social support was correlated with depression for females, r (168) = - .289, p < .001, for women, but not for men, r(64) = .111. The difference between these correlations was statistically significant, Z = 2.776, p < .01.

Comparing two correlation coefficients & Multiple regression with 2 IVs and a moderator

comparing two correlation coefficients

https://groups.google.com/forum/#!topic/comp.soft-sys.stat.spss/hMtXkcCIDYM

Testing two correlations coefficients within the same sample:

I need to compare two correlation coefficients within the same correlation matrix of
a single group. For example, is the correlation between beliefs and behavior
significantly different than the correlation between attitudes and behavior in
a single group of teenagers? Any help would be greatly appreciated.


In SPSS, standardize all of the variables using DESCRIPTIVES,
so that testing slope equalities will imply testing
correlations, then either use beliefs and attitudes as
dependents in a repeated measures ANCOVA with behavior as
the covariate and look at the interaction term, which will
test the null of equal correlations, or take a difference
variable using the standardized values of those two, and
regress it on the standardized behavior variable, which
will give the same test.

Multiple regression with 2 IVs and a moderator in SPSS

http://stats.stackexchange.com/questions/141325/multiple-regression-with-2-ivs-and-a-moderator-in-spss

I am running a multiple regression with 2 continuous independent variables and one continuous dependent variable and a categorical moderator. I am doing this in SPSS. I am not sure how to proceed. Please help!! My question is how do (1st continuous variable) and (2nd continuous variable) differentially predict (Continuous Dv) for (categorical variable split into it's 2 categories.

Likely what you want to do is to test whether the 'interaction term' is significant. This will test whether the slopes are significantly different from each other in the two groups.

In SPSS go to Analyze-->General Linear Model-->Univariate.

Put your DV in the DV slot. Put your continuous variables in the 'Covariates' box and put your categorical variable in the 'fixed factor' box. Click on the 'Model' tab and choose 'Custom'. Then move all of your IV over from the left to the right box. Then choose the 'interaction' option and move the continuous IV, together with the categorical IV over to the right hand box.

When you run your model the output will include the interaction term.

Here is a nice walkthrough that looks at various ways of checking if the relation between two continuous variables is the same or different for two different groups (it's all brought together on the last page so you might want to skim through that first):

http://psych.unl.edu/psycrs/statpage/rhtest_eg2a.pdf

Masculinity of STEM + stats from the UK, female numbers in HE

http://contexts.org/articles/what-gender-is-science/

https://www.hesa.ac.uk/intros/stuintro1213


http://www.hup.harvard.edu/catalog.php?isbn=9780674955394&content=toc

• THE WOMEN THAT NEVER EVOLVED
• Preface, 1999: On Raising Darwin’s Consciousness
• 1. Some Women That Never Evolved
• 2. An Initial Inequality
• 3. Monogamous Primates: A Special Case
• 4. A Climate for Dominant Females
• 5. The Pros and Cons of Males
• 6. Competition and Bonding among Females
• 7. The Primate Origins of Female Sexuality
• 8. A Disputed Legacy
• Afterword
• Taxonomy of the Primate Order
• Notes
• Bibliographical Updates, 1999
• Index

sexual promiscuity,female agression

http://www.nytimes.com/2013/11/19/science/a-cold-war-fought-by-women.html?pagewanted=all&_r=0

http://psych.mcmaster.ca/vaillancourt/Selected_Publications.html

http://rstb.royalsocietypublishing.org/content/368/1631/20130080.full

http://www.hup.harvard.edu/catalog.php?isbn=9780674955394&content=toc