Case Study: Inferential Analysis in a Nursing Management PhD Thesis

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Background & Study Context for this inferential analysis nursing thesis case study

An inferential analysis Nursing Management PhD thesis evaluated staff awareness, knowledge, and training needs regarding critical operational protocols in both public and private hospitals. A total of 413 respondents (147 public, 266 private) completed a structured questionnaire covering:

• Recognition of key procedural guidelines
• Familiarity with standard operating procedures
• Self-assessed training requirements

Demographic profiling (hospital type, gender, age, education, designation, years of service) informed subgroup analyses .

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Research Objectives & Hypotheses

The thesis tested three hypotheses via Chi-squared analyses:

1. Awareness Hypothesis
   • H₀: Staff awareness of operational protocols is independent of hospital type.
   • H₁: Awareness differs between public and private hospital staff.
2. Knowledge Hypothesis
   • H₀: Self-rated knowledge of procedures is independent of hospital type.
   • H₁: Knowledge levels differ between public and private hospital staff.
3. Training-Needs Hypothesis
   • H₀: Reported training needs are independent of hospital type.
   • H₁: Training requirements differ between public and private hospital staff.

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Inferential Techniques & Findings

1. Awareness of Protocols (Chi-Squared Test)

• Data: Categorical “aware” vs. “not aware,” after scoring and exclusion of supervisory personnel .
• Results:
  • Public: 15 aware, 31 not aware
  • Private: 40 aware, 37 not aware
  • χ²(1)=4.36, p=0.037 → Reject H₀ .

Interpretation: Private-hospital staff showed significantly higher awareness of the protocols than public-hospital staff.

2. Knowledge of Procedures (Chi-Squared Test)

• Data: Categorical “knowledgeable” vs. “not knowledgeable,” based on respondents’ self-assessment .
• Results:
  • Public: 34 knowledgeable, 68 not knowledgeable
  • Private: 76 knowledgeable, 90 not knowledgeable
  • χ²(1)=4.05, p=0.044 → Reject H₀.

Interpretation: Staff in private hospitals reported significantly greater knowledge of the procedures compared to those in public hospitals.

3. Training Needs (Chi-Squared Test)

• Data: Categorical “needs training” vs. “does not need training,” derived from self-assessment items .
• Results:
  • Public: 55 need training, 17 do not
  • Private: 72 need training, 43 do not
  • χ²(1)=3.86, p=0.049 → Reject H₀.

Interpretation: Public-hospital staff were significantly more likely to indicate a need for additional training than private-hospital staff.

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Implications for Nursing Management from this inferential analysis case study

• Targeted Education: Disparities in awareness and knowledge suggest customized training modules for different hospital settings.
• Resource Allocation: High training-need signals in public hospitals justify prioritizing workshops and simulations there.
• Policy Development: Administrators can use these insights to standardize procedural guidelines and ensure equitable staff competence.

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Takeaways for PhD Researchers from this inferential analysis nursing thesis case study

1. Robust Categorization: Carefully transform scaled responses into meaningful categorical outcomes.
2. Assumption Verification: Even with chi-squared tests, confirm expected cell counts or apply corrections (e.g., Yates’).
3. Hypothesis Pre-Registration: Define H₀ and H₁ clearly to streamline your analytical workflow.
4. Practical Significance: Interpret effect sizes alongside p-values to assess real-world impact.
5. Audit Documentation: Log each data-processing decision (exclusions, recoding) for reproducibility.

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This anonymized field-specific deep dive demonstrates how stratified chi-square analyses can yield actionable insights in Nursing Management research—guiding targeted training and policy decisions without disclosing study-specific content.

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Want to explore more PhD-level case studies? Check out our Comprehensive Case Studies on PhD Statistical Analysis guide page.