Investigating Gender Pay Gap: Education Level and Age

Statistics
Student Name:
Instructor Name:
Course Number:
9th June 2019

Description of research study
This study seeks to investigate the extent to which differences in the education level and age by
female and male employees contributes to the gender pay gap (McDonald & Thornton, 2017).
Statement of the problem
Variation in the pay for the male and female employees has been a topic of discussion and a
matter of great concern in many parts of the world in the recent past (Jagsi, et al., 2012).
Research hypothesis
Three hypothesis are to be tested in the study. The following are the three hypotheses that will be
tested in this study;
1. Null hypothesis (H0): There is no significant difference in the salaries for the male and
female employees.
Alternative hypothesis (HA): There is significant difference in the salaries for the male
and female employees.
2. Null hypothesis (H0): There is no significant difference in the salaries based on the
highest level of education.
Alternative hypothesis (HA): There is significant difference in the salaries based on the
highest level of education.
3. Null hypothesis (H0): There is no significant relationship between employee salary and
age of the employee.
Alternative hypothesis (HA): There is significant relationship between employee salary
and age of the employee.

List of variables
The following are the variables to be used in the study;
 Employee salary
 Age of employee
 Gender of employee
 Highest education level of employeeTable 1: Variable description
Variable name Variable type Scale measurement
Employee salary Continuous Ratio
Age of employee Continuous Ratio
Gender of employee Categorical Nominal
Highest education level of
employee
Categorical Nominal
Data
A fictitious data with 50 observations was created for this study. The data is presented in table 2
below;Table 2: dataset
S/No. salary Gende
r Age Educati
on level S/No. salary Gende
r Age Educati
on level
1 1095 1 30 2 26 2983 1 48 4
2 1001 1 29 1 27 1160 2 26 2
3 1122 1 31 1 28 3844 1 41 4
4 578 2 25 1 29 476 2 23 1
5 1368 1 30 3 30 1492 1 30 4
6 1145 1 36 2 31 1024 2 29 2
7 1078 1 35 2 32 1593 1 34 3
8 1094 1 28 2 33 427 2 23 1
9 1237 1 35 1 34 829 2 29 1
10 833 2 24 1 35 797 2 26 1
11 567 2 25 1 36 577 2 25 1
12 933 2 24 1 37 1342 1 28 4
13 1339 1 37 3 38 1774 1 31 4
14 937 2 28 2 39 709 2 23 1
15 2011 1 40 4 40 860 2 24 2
16 1585 1 38 4 41 1336 1 30 3
17 905 2 30 1 42 516 2 24 1
18 1058 1 31 2 43 931 2 25 1

19 922 2 25 2 44 815 2 24 1
20 1220 2 26 3 45 1681 1 34 4
21 1022 2 27 2 46 568 2 22 1
22 759 2 25 1 47 775 2 25 1
23 1414 1 29 3 48 1188 2 26 2
24 1041 1 26 3 49 782 2 23 1
25 1688 1 39 4 50 1170 1 28 1
Where we have the following;
1
Diplom
a 1 Male
2
Bachelo
r 2 Female
3 Masters
4 PhD
Descriptive data analysis
In this section, we present the descriptive statistics for the continuous variables (salary and age).
Measures of central tendency
For the measures of central tendency, we consider, the mean, median and the mode. As can be
seen from the table, the average age was found to be 29.08 years old with the oldest person being
48 years of age and the youngest being 22 years of age. The median age was 28 years old. For
the salary, it was established that the average salary was 1152.02 with the highest salary being
$3844 and the lowest salary being $427.
Measures of dispersion
The measures of dispersion considered include the range and the standard deviation. The
standard deviation for salary was found to be 593.52 (a value less than the mean), this suggests
that the distribution of salary is not widely spread out. This can be confirmed form the range