A manager randomly assigned several employees to join a program in order to increase their job performance. After six months, all the employee’s job performance were tested. The manager aim to test four different null hypothesis.
Hypothesis 1 There is no significant differences of job performance between employees who join the program and those who did not.
The job performance’s scores for those who join the program were 80, 81, 82, 83, 84 and 82 and scores for those who did not join the program were 78, 84, 80, 74, 82 and 70. The manager then compare the scores from both groups to determine which group scores significantly higher in order to determine the effect of the program.
Based on the situation above, carry out an independent t-test with the 0.05 level of significance at two-tailed to test the hypothesis.
Answer: t-critical=2.228 > t-value=1.813, null hypothesis is accepted, the program has no effect on job performance.
Hypothesis 2 There is no significant differences of employee’s job performance before and after the program.
The job performance’s scores before the program were 78, 82, 75, 85, 78 and 80 and scores for six month after the program were 80, 81, 82, 83, 84 and 86. The manager then compare the scores from both conditions to determine which one is significantly higher in order to determine the effect of the program after six months.
Based on the situation above, carry out a paired sample t-test with the 0.05 level of significance at two-tailed to test the hypothesis. Answer: t-critical=2.571 > t-value=1.884, null hypothesis is accepted, the program did not has significant effect on job performance after six months.
Hypothesis 3 There is no significant differences of employee’s job performance based on department.
There are three major departments in the company and the manager aims to compare the job performance scores between the employees in different department. The data were shown in the table below:
| Human Resource | Research | Production | |
| 80 | 84 | 82 | |
| 78 | 76 | 74 | |
| 88 | 86 | 88 | |
| 76 | 76 | 84 | |
| 88 | 83 | 72 | |
| M = 82, T = , SS1 = | M = 81, T = , SS2 = | M = 80, T = , SS3 = |
Based on the data above, carry out a one way ANOVA test with the 0.05 level of significance at one-tailed to test the hypothesis.
Answer: Refer to Note.
Hypothesis 4 There is no significant difference of the days employees absent from work based on department.
The manager also want to compare the days of employees absent from their work across different department in the company. The data of attendance from last year shows that the total employees absent from HR department were 13, research department were 15 and production department were 29.
Based on the situation above, carry out a Chi-Square test with a 0.05 level of significance to test the hypothesis.
Answer: Xsquare critical = 5.99 < Xsquare value = 8, reject null hypothesis, there is one department which is significantly differ in terms of total employees absent from last year.
Hypothesis 5 There is no significant relationship between commitment and job performance.
The manager also want to determine the relationship between commitment and job performance by distributing questionnaires to measure both variables. The score from respondents were shown below:
| Respondent | Commitment | Job Performance |
| A | 20 | 70 |
| B | 22 | 72 |
| C | 23 | 74 |
| D | 24 | 73 |
| E | 25 | 74 |
| F | 24 | 75 |
Based on the situation above, carry out a Pearson correlation analysis with a 0.05 level of significance at two-tailed to test the hypothesis.
Answer: r-critical = 0.811 < r-value = 0.875, reject null hypothesis, there is a significant relationship between commitment and job performance.