To examine and clarify background conditions for understanding variables which affect salary, the salary and compensation programs at two industrial and three educational organizations were subjected to a statistical audit. Data were available on 272 employees. Ten compensation variables were studied as having direct or indirect effects on salary: sex, race, military service, educational attainment, relevant experience, organization (industrial or educational), years of service, performance history, salary grade and time in grade. These variables were labeled independent variables for a multiple regression analysis. Path analysis was also performed, with these variables labeled causal. A third model for statistical analysis emerged, which deleted the variable, time in grade. A bivariate correlation matrix was prepared for the third statistical technique. The most important variables, based on multiple regression, were organization, grade, years of service, and military service. Based on path analysis, they were organization, educational level, military service, and grade. Results suggested that current path analytic methods are more effective than multiple regression. (GDC)