Many of the independent variables included in the analyses in this report are related, and to some extent the pattern of differences found in the descriptive analyses reflect this covariation. For example, when examining the salary of the faculty by gender, it is possible that some of the observed relationship is due to differences in other factors related to gender, such as institution type, tenure status, and so on. However, if nested tables were used to isolate the influence of these other factors, cell sizes would become too small to identify the significant differences in patterns. When the sample size becomes too small to support controls for another level of variation, other methods must be used to take such variation into account. The method used in this report estimates adjusted means with a regression model, an approach sometimes referred to as communality analysis.

For the analysis of faculty salaries, multiple linear regression was used to obtain means that were adjusted for covariation among a list of control variables.⁸ Variables that showed significant gender or racial/ethnic differences in the crosstabular analyses were selected for inclusion in the regression model. Some eliminations were then made for variables that were highly correlated; in those cases, different combinations of variables were estimated in models not shown in the report to determine which combinations of variables demonstrated robust results. The final list of variables included in the regression is as follows: gender, race/ethnicity, type of institution, teaching field, level of instruction, tenure status, rank, highest degree, years since highest degree, age, time spent teaching, number of classes taught, time spent engaged in research, and number of total publications or other permanent creative works in the previous 2 years. Each independent variable is divided into several discrete categories. To find an estimated mean value on the dependent variable for each category of an independent variable, while adjusting for its covariation with other independent variables in the equation, substitute the following in the equation: (1) a one in the category's term in the equation, (2) zeroes for the other categories of this variable, and (3) the mean proportions for all other independent variables. This procedure holds the impact of all remaining independent variables constant, and differences between adjusted means of categories of an independent variable represent hypothetical groups that are balanced or proportionately equal on all other characteristics included in the model as independent variables.

For example, consider a hypothetical case in which two variables, age and gender, are used to describe an outcome, Y (such as salary). The variables age and gender are recoded into a dummy variable representing age, A, and a dummy variable representing gender, G:

The following regression equation is then estimated from the correlation matrix output from the DAS as input data for standard regression procedures:

To estimate the adjusted mean for any subgroup evaluated at the mean of all other variables, one substitutes the appropriate values for that subgroup's dummy variables (1 or 0) and the mean for the dummy variable(s) representing all other subgroups. For example, suppose Y represents faculty salary, which is being described by age (A) and gender (G), coded as shown above. Suppose the unadjusted mean values of these two variables are as follows:

Next, suppose the regression equation results are as follows:

(6)

To estimate the adjusted value for younger faculty, one substitutes the appropriate parameter estimates and variable values into equation 6.

This results in the following equation:

In this case, the adjusted mean for younger faculty is 47,945 and represents the expected outcome for younger faculty who resemble the average faculty member across the other variables (in this example, gender). In other words, the adjusted salary of younger faculty, controlling for gender, is $47,945.

It is relatively straightforward to produce a regression model using the DAS, since one of the DAS output options is a correlation matrix, computed using pairwise missing values. In regression analysis, there are several common approaches to the problem of missing data. The two simplest are pairwise deletion of missing data and listwise deletion of missing data. In pairwise deletion, each correlation is calculated using all of the cases for the two relevant variables. For example, suppose you have a regression analysis that uses variables X1, X2, and X3. The regression is based on the correlation matrix between X1, X2, and X3. In pairwise deletion, the correlation between X1 and X2 is based on the nonmissing cases for X1 and X2. Cases missing on either X1 or X2 would be excluded from the calculation of the correlation. In listwise deletion, the correlation between X1 and X2 would be based on the nonmissing values for X1, X2, and X3. That is, all of the cases with missing data on any of the three variables would be excluded from the analysis.

The correlation matrix can be used by most statistical software packages as the input data for least squares regression. That is the approach used for this report, with an additional adjustment to incorporate the complex sample design into the statistical significance tests of the parameter estimates (described below).⁹

Most statistical software packages assume simple random sampling when computing standard errors of parameter estimates. Because of the complex sampling design used for the NSOPF survey, this assumption is incorrect. A better approximation of their standard errors is to multiply each standard error by the design effect associated with the dependent variable (DEFT)¹⁰, where the DEFT is the ratio of the true standard error to the standard error computed under the assumption of simple random sampling. It is calculated by the DAS and produced with the correlation matrix output.