Gender Discrimination - a Statistical Analysis
Gender Discrimination: A Statistical Analysis
Gender discrimination, or sex discrimination, may be characterized as the unequal treatment of a person based solely on that person 's sex. .
It is apparent that gender discrimination is pervasive in the modern workplace, however, its presence and effects are often misrepresented and misunderstood. Statistical testing plays an important role in cases where the existence of discrimination is a disputed issue and has been used extensively to compare expected numbers of members of a protected group, to the actual number of members of that protected group that have been involved in a significant employment action. This paper will use statistical testing and analysis, including a multiple regression model, to estimate the effects that various independent variables have upon the dependent variable, salary level.
This analysis utilized a data sample consisting of 46 employees and variables relating to each of those employees. These variables include: gender, age, level of education, length of employment, job type, and weekly salary. Each of these variables is further broken as follows: gender was divided between males and females; age was listed as the age of the employee; education was broken down to reflect the last level of education obtained by the employee, some high school, high school, college, and graduate school; employment length was valued as the number of months the employee had been employed; job type reflected different positions, clerical, technical and managerial; and weekly salary reflected the weekly salary of each employee in the sample.
In order to make inferences about the sample data, SPSS was used to generate a multiple regression model. The purpose of this multiple regression model is to predict a dependent variable based on the values of multiple independent variables. In this case, the initial multiple regression model was produced using weekly salary as the dependent variable. After an
Gender discrimination, or sex discrimination, may be characterized as the unequal treatment of a person based solely on that person 's sex. .
It is apparent that gender discrimination is pervasive in the modern workplace, however, its presence and effects are often misrepresented and misunderstood. Statistical testing plays an important role in cases where the existence of discrimination is a disputed issue and has been used extensively to compare expected numbers of members of a protected group, to the actual number of members of that protected group that have been involved in a significant employment action. This paper will use statistical testing and analysis, including a multiple regression model, to estimate the effects that various independent variables have upon the dependent variable, salary level.
This analysis utilized a data sample consisting of 46 employees and variables relating to each of those employees. These variables include: gender, age, level of education, length of employment, job type, and weekly salary. Each of these variables is further broken as follows: gender was divided between males and females; age was listed as the age of the employee; education was broken down to reflect the last level of education obtained by the employee, some high school, high school, college, and graduate school; employment length was valued as the number of months the employee had been employed; job type reflected different positions, clerical, technical and managerial; and weekly salary reflected the weekly salary of each employee in the sample.
In order to make inferences about the sample data, SPSS was used to generate a multiple regression model. The purpose of this multiple regression model is to predict a dependent variable based on the values of multiple independent variables. In this case, the initial multiple regression model was produced using weekly salary as the dependent variable. After an