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# Dependent variable variable: Index of conservative attitudes about gender roles  Our dependent variable was people's attitudes towards gender roles. To operationalize these attitudes, we used 7 questions from the GSS that asked for people's opinions on gender roles (see below). To arrive at a single outcome measure, we performed a Principal Components Analysis (PCA) as a method of dimensionality reduction. 
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Before fitting the PCA, we standardized the answers to each question to have variance of 1 and mean of 0.  To handle missing data, we excluded every respondent who answered only four or fewer of the 7 questions. Otherwise, missing values were imputed using the average response to a given question by other respondents in the same year.   Principle Components Analysis performs a total least squares regression on a number of variables to find the dimensions that can explain the most (or least) amount of variance in the data. Each principle component is an eigenvector of the data's covariance matrix (D^TD) and the amount of variance it explains is the corresponding eigenvalue.By projecting the data back onto one or several of these principle components they can now be expressedn with respect to these new latent dimensions  ## Resulting measure  The first principle component of the 7 gender questions from the GSS explained 36% of the variance in the data.  
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| Favor preferential hiring of women 2 | -0.02 |  | Family life suffers if men work too much | 0.54 |  After projecting the question data on this component, participants with more traditional views on gender roles (assigning more domestic responsibilities to women and more professional ones to men) score higher values on this measure. In the rest of this report we will refer to this dependent variable as our c  # We also tried: