Welcome to the first week of the second course in Coursera’s Data Management and Visualization specialization. In order to utilize ANOVA and post hoc testing, I needed to examine a different explanatory variable than in the previous course. This analysis will examine whether or not a person’s gender and race is associated with their support of the death penalty, as punishment for murder. I will still be using the Outlook on Life (OOL) surveys, made available by ICPSR for Coursera students.
Hypotheses to be tested:
Null hypothesis: the death penalty is supported equally among all gender-racial groups.
Alternate hypothesis: support for the death penalty varies among gender-racial groups.
The categorical response variable was a combination of two groups: those who know someone who has been arrested for a crime, and those who have a friend or relative who has been convicted of a crime. This variable had two categories: yes and no.
The new categorical explanatory variable contains four (4) categories: white men, white women, men of color, and women of color. The gender choices were extremely limited in the dataset: male, female, and no response. Because it was not possible to know if a “no response” was a refusal to answer or identifying as a transgender or nonbinary person, these individuals were omitted from the sample. For more comprehensive data analysis, the survey should have used “masculine” and “feminine” instead of “male” and “female,” and included options for transgender, nonbinary, and possibly other gender identities.
An analysis of variance (ANOVA) revealed that among this sample, the gender and race of an individual (collapsed into 4 categories, as the categorical explanatory variable) is significantly associated with a preference for the death penalty. Utilizing an ordinary least squares (OLS) approach, the following results were obtained: F-statistic = 32.57, p = 2.10e-20. Tukey’s Honestly Significant Difference post hoc test was conducted to determine which groups were significantly different from each other. There was no significant difference in the results between white men and white women, therefore we accept the null hypothesis; however, there were significant differences between white women and men of color, white men and men of color, men of color and women of color, white women and women of color, and white men and women of color, and we accept the alternate hypothesis for these groups.
The punishment preferences among the groups are as follows: 66.7% of white men favor the death penalty, 58.8% of white women favor the death penalty, 55.6% of men of color favor imprisonment, and 64.7% of women of color favor imprisonment.
I was unable to calculate standard deviation for these results. I do not think this is possible, because the explanatory variable has 4 categories, and the response variable has 2 categories: neither are quantitative. After spending many hours (at least 16!) trying to find and code quantitative variables relevant to my original thesis, I was unsuccessful. I understand the code involved in calculating means and standard deviations, but I was unable to show that in this assignment. If any of my classmates have some input or resources, I’d welcome the assistance. I would very much like to calculate the deviation between each of the four gender-ethnic groups.
Click to read the rest….Data Analysis Tools, week 1: hypothesis testing and ANOVA