Burnout Syndrome Among ICU Nurses in Riyadh Hospitals
Simulated Case Study
This case study uses simulated data to demonstrate the statistical methodology, analysis workflow, and reporting standards we apply to real client projects. No actual patient or institutional data is represented.
Logistic Regression · CFA · SEM · Mediation Analysis
Confidentiality Notice
All data presented in this case study is simulated and replicates the statistical structure of the original dataset. The researcher's identity and institutional affiliation remain confidential. No real participant data is disclosed.
Study design
Cross-sectional survey
Sample
412 ICU nurses
Sites
14 tertiary hospitals, Riyadh
Instrument
Maslach Burnout Inventory (MBI-HSS)
Burnout prevalence
61.4% (high emotional exhaustion)
SEM fit
CFI = 0.96, RMSEA = 0.047
Project Overview
This project provided full statistical analysis for a PhD dissertation in nursing sciences submitted to King Saud University, Riyadh. The researcher conducted a cross-sectional survey across 14 tertiary-care hospitals in the Riyadh metropolitan region, enrolling 412 ICU nurses through proportional stratified random sampling. The Maslach Burnout Inventory — Health Services Survey (MBI-HSS) quantified emotional exhaustion, depersonalization, and personal accomplishment.
Naggar Analytics delivered the complete analysis pipeline: data cleaning and normality testing (Shapiro-Wilk), reliability analysis (Cronbach's α = 0.89), binary logistic regression with stepwise backward selection and Hosmer-Lemeshow goodness-of-fit, mediation analysis (bootstrapped 95% CI, 5,000 resamples) testing job satisfaction as a mediator between workload and burnout, and a full structural equation model (CFA + path analysis in AMOS) with CFI = 0.96 and RMSEA = 0.047. All tables and figures were formatted to APA 7th edition standards.
Analytical Methods
- ▹Sample size calculation — a priori power analysis (G*Power 3.1, α = 0.05, power = 0.80)
- ▹Data cleaning & management in SPSS 28
- ▹Descriptive statistics — frequencies, means, SD; normality testing (Shapiro-Wilk)
- ▹Reliability analysis — Cronbach's α for MBI-HSS subscales (overall α = 0.89)
- ▹Binary logistic regression — stepwise backward selection; Hosmer-Lemeshow goodness-of-fit
- ▹Mediation analysis — Baron-Kenny approach + bootstrapped 95% CI (5,000 resamples)
- ▹Confirmatory Factor Analysis (CFA) — 3-factor MBI-HSS model (AMOS 26)
- ▹Structural Equation Modeling (SEM) — CFI = 0.96, RMSEA = 0.047, SRMR = 0.051
- ▹APA 7th edition formatting — all tables, figures, and methodology chapter reviewed