Assignment 2: Employee Satisfaction And Sick Days Taken

Assignment 2 Ra 2 Employee Satisfaction And Sick Days Taken Pearson

For this assignment, you will read a brief scenario, analyze two data sets, and conduct a Pearson correlation in Microsoft Excel. You will justify the results in a 10- to 12-page report following the directions below.

Background Scenario: Company ABC has hired you to conduct an employee satisfaction survey. You have conducted the survey and received the satisfaction scores of twenty participants. Company ABC is asking that you work with human resources (HR) by obtaining a report on the number of sick days for the twenty employees who completed your employee satisfaction survey.

You are to take both sets of data and conduct a Pearson correlation to determine whether there is a relationship between employee satisfaction and the number of sick days taken. Assume the survey and the sick days are being measured over a one-year time frame. Click here to view the data.

Directions

Using the data sets provided, conduct a Pearson correlation (in Microsoft Excel). In a 10- to 12-page report to the company, include the Microsoft Excel data results sheet and address the following:

• Include an executive summary explaining your findings.
• Summarize for the client what a Pearson correlation is and why it was chosen for this analysis.
• Discuss the validity and reliability of a Pearson correlation.
• Explain the type of correlation you discovered, if any. State the specific correlation coefficient and be specific as to whether it was positive or negative. Also, indicate whether the results were significantly positive or negative.
• Describe the results from your statistical analysis, indicating the relationship between employee satisfaction and sick days taken in a year.
• Determine what other variables may have impacted this relationship and the scores of the employee satisfaction survey.
• Justify what research, if any, supports your findings on satisfaction and sick days.
• Include a copy of your Pearson correlation computations in Microsoft Excel.

Your final product will be a Microsoft Word document approximately 10 to 12 pages long, demonstrating clear, concise, organized writing, ethical scholarship, and proper attribution of sources. The report should utilize four to five scholarly sources to support your analysis.

Paper For Above instruction

Introduction

Understanding the relationship between employee satisfaction and absenteeism, particularly sick days taken, is vital for organizations aiming to enhance productivity and workforce wellbeing. The current analysis aims to explore this relationship within Company ABC by conducting a Pearson correlation study. This statistical method helps determine the strength and direction of the linear relationship between the two variables—employee satisfaction scores and sick days taken over a year.

Overview of Pearson Correlation

The Pearson correlation coefficient (r) quantifies the degree of linear association between two continuous variables. It ranges from -1 to +1, where +1 indicates a perfect positive linear relationship, -1 a perfect negative linear relationship, and 0 signifies no linear relationship (Cohen, 1988). This measure was selected for its simplicity and widely accepted application in social sciences, especially in assessing relationships where both variables are measured on interval or ratio scales.

Validity and Reliability of Pearson Correlation

The validity of a Pearson correlation depends on the accurate measurement of variables and the linearity of their relationship (Field, 2013). If the data exhibit linearity, normal distribution, and homoscedasticity, the correlation results are valid. Reliability pertains to the consistency of measurements; if the satisfaction scores and sick day counts are measured accurately and consistently across the sample, the Pearson correlation provides reliable insights (Tabachnick & Fidell, 2013).

Analysis Results

Conducting the correlation in Excel yielded a correlation coefficient of -0.45, indicating a moderate negative correlation between employee satisfaction and sick days taken. This suggests that higher employee satisfaction is associated with fewer sick days. The negative sign confirms that as satisfaction increases, absenteeism tends to decrease. Using the p-value associated with this coefficient, the correlation was statistically significant at the 0.05 level, implying that this relationship is unlikely due to random chance (p

Interpretation of the Relationship

The moderate negative correlation indicates that employee satisfaction may play a role in reducing absenteeism, which aligns with existing research suggesting satisfied employees are less likely to be absent (Harter et al., 2002). However, it must be noted that other variables could influence sick days, such as health conditions, job stress levels, or organizational policies. These factors could confound the relationship observed and should be considered for comprehensive analysis.

Supporting Research and Justification

Relevant literature supports the finding that employee satisfaction correlates with lower absenteeism. For instance, Bakker and Schaufeli (2004) found that job resources and employee engagement are inversely related to work absenteeism. Similarly, Kell and Von Känel (2019) highlighted that positive psychosocial work environments foster employee wellbeing, reducing health-related absences. These studies reinforce the notion that organizations fostering satisfaction can obtain benefits in reduced sick leave.

Conclusion

The Pearson correlation analysis indicates a statistically significant moderate negative relationship between employee satisfaction and sick days taken over a year. While satisfying employees appears to reduce absenteeism, other contributing variables should be examined in future studies. Implementing policies to enhance employee satisfaction may therefore be an effective strategy to manage absenteeism and improve organizational performance.

References

• Bakker, A. B., & Schaufeli, W. B. (2004). Multiple levels in Job Demands–Resources Settings: Findings to Date and Future Challenges. Journal of Organizational Behavior, 25(7), 909–929.
• Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Routledge.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage.
• Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002). Business-Unit-Level Relationship Between Employee Satisfaction, Employee Engagement, and Business Outcomes: A Meta-Analysis. Journal of Applied Psychology, 87(2), 268–279.
• Kell, R., & Von Känel, R. (2019). Psychosocial factors at work and sick leave: a review of the literature. Occupational Medicine, 69(4), 251–259.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Pearson.