| Statistical Analysis | HR Analytics | Data Tools & Visualization |
|---|---|---|
| t-tests | Engagement Predictors | IBM SPSS |
| Correlation Analysis | Gender & Work Status Studies | Inferential Statistics |
| Multiple Regression | Learning & Overload Factors | Data-driven Insights |
This professional HR analytics report investigates the key drivers of employee work engagement using SPSS. It examines statistical differences by gender and employment type, and identifies the most significant predictors contributing to work engagement.
Key Research Objectives:
- Analyze whether employee engagement differs based on gender and employment status.
- Identify which workplace factors—such as learning opportunities, job overload, and support systems—are most predictive of engagement.
Importance: Work engagement is central to employee retention, motivation, and organizational performance. Understanding its predictors enables HR professionals to design evidence-based interventions that boost workforce morale and productivity.
Data was gathered from 100 employees via a structured survey. Variables included engagement scores, gender, employment status (permanent vs. casual), job characteristics, and workplace factors.
Analytical Tools: IBM SPSS was used to perform:
- Descriptive Statistics
- Correlation Analysis
- Independent Samples t-tests
- Multiple Linear Regression
The data was preprocessed for outliers and missing values before analysis.
Initial descriptive analytics reveal variability in engagement across participants. A correlation matrix highlighted relationships among engagement and its predictors.
- Male employees reported significantly higher engagement.
- t(98) = -4.22, p < .001, r = –0.55 (moderate effect size).
- Interpretation: Gender inequality, lack of workplace support, and social expectations may influence female engagement.
- Permanent employees had higher engagement than casuals.
- t(98) = -2.40, p = .018, r = –0.24 (small to moderate effect).
- Interpretation: Job security, benefits, and role identity foster greater connection among permanent staff.
- Model: F(8,91) = 52.76, p < .001
- R² = 0.823 → 82.3% of variance in engagement explained
Significant Predictors:
- Gender (B = –0.433, p = 0.009): Females reported lower engagement.
- Overload (B = –0.182, p = 0.010): Excess workload negatively impacted engagement.
- Learning (B = 0.306, p < 0.001): Development opportunities were the strongest positive predictor.
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Address Gender Gaps
Implement equitable opportunities, mentorship, and flexible policies to boost female engagement. -
Manage Work Overload
Provide better time management tools and realistic task delegation. -
Enhance Learning & Growth
Learning opportunities are the top driver of engagement—HR must prioritize employee development programs.
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- Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600–619.
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- Shuck, B., & Wollard, K. (2010). Employee engagement and HRD. Advances in Developing Human Resources, 12(4), 429–446.
Author: Ramanav Bezborah
Tool: IBM SPSS Statistics
Year: 2025