Analyzing Employee Satisfaction Levels with t-Test Data

Planning

The current study intends to explore the relationship between managers and individual contributors to employee satisfaction. A proposed explanation and hypothesis for the difference in satisfaction scores between the two groups can be determined. While the alternative hypothesis proposes that there is a relationship between the two variables, the null hypothesis rejects it. Therefore, after conducting the hypothetical test, it will be easy to determine whether there is a relationship between the two variables or not. A co-worker provided the data on the variables, which can be analyzed for results and discussion.

Analysis

Raw data is meaningless since it is difficult to determine the data set characteristics from it. Data analysis is crucial in getting significant meaning out of the raw data. The analysis procedures involve sorting the data, cleaning it, and conducting descriptive statistics: mean, count, mode, median, and standard deviation (Greenland, 2022). The count helps determine the number of participants whose responses will be analyzed. Meanwhile, the mean provides an average of the responses given. The total number of participants for the provided data was 53, with an average response of 50.33962.

The mode allows one to know the most frequent responses from the study participants. The median is the middle value of the data given if arranged from the smallest to the largest. On the other hand, the standard deviation helps in knowing how much the values differ from the average satisfaction score. The current study’s mode, median, and standard deviation are 23, 25, and 28.69397. The data provided was a sample from a larger population whose standard deviation and mean are unknown. Therefore, the data analysis procedure will involve conducting a two-sample t-statistical test using Microsoft Excel 2019.

Results and Discussion

The data given represented a population whose standard deviation and average were unknown. Consequently, the t-test assuming unequal variances was appropriate in proving and disproving the null hypothesis. The p-value for the test was 0.031143122, which was less than the significance level of 0.05. Therefore, the null hypothesis was rejected, proving the alternative one. There was a difference between the manager and individual contributor satisfaction scores. The organization needs to adopt employee welfare programs to reduce the difference in satisfaction scores.

Appendix

Table 1 – Raw Data on Satisfaction

Participant ID Role Satisfaction Score
1 Manager 78
2 Manager 54
3 Manager 75
4 Manager 54
5 Manager 89
6 Manager 56
7 Manager 34
8 Manager 56
9 Manager 87
10 Manager 12
11 Manager 67
12 Manager 98
13 Manager 100
14 Manager 78
15 Manager 23
16 Manager 67
17 Manager 4
18 Manager 76
19 Manager 86
20 Manager 82
21 Manager 39
22 Manager 36
23 Manager 23
24 Manager 72
25 Manager 35
26 Individual Contributor 36
27 Individual Contributor 84
28 Individual Contributor 26
29 Individual Contributor 84
30 Individual Contributor 21
31 Individual Contributor 45
32 Individual Contributor 73
33 Individual Contributor 14
34 Individual Contributor 34
35 Individual Contributor 23
36 Individual Contributor 1
37 Individual Contributor 23
38 Individual Contributor 67
39 Individual Contributor 23
40 Individual Contributor 74
41 Individual Contributor 13
42 Individual Contributor 42
43 Individual Contributor 12
44 Individual Contributor 41
45 Individual Contributor 22
46 Individual Contributor 23
47 Individual Contributor 78
48 Individual Contributor 89
49 Individual Contributor 98
50 Individual Contributor 23
51 Individual Contributor 63
52 Individual Contributor 12
53 Individual Contributor 43

Table 2 – Results of t-Test: Two-Sample Assuming Unequal Variances

Variable 1 Variable 2
Mean 59.24 42.39285714
Variance 738.7733333 790.1732804
Observations 25 28
Hypothesized Mean Difference 0
df 51
t Stat 2.216510453
P(T<=t) one-tail 0.015571561
t Critical one-tail 1.67528495
P(T<=t) two-tail 0.031143122
t Critical two-tail 2.00758377

Cite this paper

Select style

Reference

StudyCorgi. (2025, August 5). Analyzing Employee Satisfaction Levels with t-Test Data. https://studycorgi.com/analyzing-employee-satisfaction-levels-with-t-test-data/

Work Cited

"Analyzing Employee Satisfaction Levels with t-Test Data." StudyCorgi, 5 Aug. 2025, studycorgi.com/analyzing-employee-satisfaction-levels-with-t-test-data/.

* Hyperlink the URL after pasting it to your document

References

StudyCorgi. (2025) 'Analyzing Employee Satisfaction Levels with t-Test Data'. 5 August.

1. StudyCorgi. "Analyzing Employee Satisfaction Levels with t-Test Data." August 5, 2025. https://studycorgi.com/analyzing-employee-satisfaction-levels-with-t-test-data/.


Bibliography


StudyCorgi. "Analyzing Employee Satisfaction Levels with t-Test Data." August 5, 2025. https://studycorgi.com/analyzing-employee-satisfaction-levels-with-t-test-data/.

References

StudyCorgi. 2025. "Analyzing Employee Satisfaction Levels with t-Test Data." August 5, 2025. https://studycorgi.com/analyzing-employee-satisfaction-levels-with-t-test-data/.

This paper, “Analyzing Employee Satisfaction Levels with t-Test Data”, was written and voluntary submitted to our free essay database by a straight-A student. Please ensure you properly reference the paper if you're using it to write your assignment.

Before publication, the StudyCorgi editorial team proofread and checked the paper to make sure it meets the highest standards in terms of grammar, punctuation, style, fact accuracy, copyright issues, and inclusive language. Last updated: .

If you are the author of this paper and no longer wish to have it published on StudyCorgi, request the removal. Please use the “Donate your paper” form to submit an essay.