Gender and Occupation: A Data-Driven Learning Guide
For this exercise, you will be exploring the distribution of men and women by occupation within firms using summary statistics.
First, to simplify analyses, we created new variables for all variables of interest to exclude "don't know/no opinion" (99998) and "refused/missing/skipped" (99999) answers. These variables are called D2new, D2Anew,...D4anew.
Next we created a variable, "ratiocler," for the proportion of clerical employees at a firm that is female by dividing the number of female clerical workers by the number of clerical workers overall. This variable ranges from 0 to 1, therefore a frequency distribution of ratiocler is a bit unwieldy.
Summary statistics provided below the table will help you to see the information more clearly. First look at the mean which represents the average proportion of a company's clerical workforce that is female. Next look at the value that divides the distribution in half (median) and the most frequently occurring value, the mode. Note the value of the standard deviation, which helps give a sense of how closely the values cluster around the mean. What can you say about these companies' clerical workforce -- does it tend to be more or less female than male?
Next we created a similar variable for the proportion of sales employees at the firm that is female, called "ratiosales."
Now examine the summary statistics for ratiosales. Again look below the frequency table at the summary statistics such as the mean, median, mode, and standard deviation. How does this distribution compare to the one you just saw for clerical workers?
The last created variable is similar to the first two and represents the proportion of blue-collar or manual employees at the firm that is female (Ratiomanual).
Now look at the summary statistics for ratiomanual. As before, examine the mean, median, mode, standard deviation, and the range of values. What do you find? What is the average proportion of female manual or blue-collar workers? What is the most common value among the firms in the sample (the mode)? Compare these statistics to those you had above. What can you say about the gender distributions of these companies?
Finally, given the discrepencies in the proportion of various job categories that are women, one might think that employers feel that men are just better at some tasks than women and vice versa. Is this the case? Look at the frequency distribution of answers to this question. What percent of respondents answered that they feel there are, on average, tasks for which men and women are better suited? Are the answers what you expected? Why or why not?
Note: The online data analysis system (DAS) used on this site uses a system called Survey Documentation and Analysis (SDA), developed and maintained by the Computer-assisted Survey Methods Program (CSM) at the University of California, Berkeley. Documentation for DAS/SDA can be found on their Web site.
CITATION: Inter-university Consortium for Political and Social Research. Gender and Occupation: A Data-Driven Learning Guide. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2009-04-16. Doi:10.3886/genderoccupation
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.