Background & Research Context
This Educational Psychology Inferential Analysis case study explores a PhD project assessing the effectiveness of a targeted educational intervention on children’s and adolescents’ knowledge and attitudes, the dissertation’s Chapter 4 divides its analytical work into descriptive and inferential components.
Data & Study Design of this Educational Psychology Inferential Analysis case study
- Sample: 217 participants (120 children, 97 adolescents) from urban and rural schools.
- Measurements: Pre- and post-intervention knowledge scores (0–20) and attitude scores (0–72).
- Demographics: Area of residence, gender, parents’ education/occupation, family type, and household income.
Inferential Techniques Employed
- Chi-Squared Tests
- Examined associations between baseline knowledge/attitude categories and demographic variables.
- Example: Children’s pre-test knowledge vs. area of residence, χ²(1)=9.39, p=0.00218, indicating a significant relationship.
- Paired t-Tests
- Assessed within‐subject changes by comparing mean scores before and after the intervention.
- Example (adolescents, n=97): Mean knowledge increase was significant (t=13.84 > 1.98, p < 0.05).
- Repeated Measures ANOVA
- Evaluated sustainability of effects across multiple follow‐up assessments.
- Example (adolescents’ knowledge): F=0.24 < 3.09 critical, p>0.05, indicating stability of the post-intervention gains.
Key Findings from this Educational Psychology Inferential Analysis case study
- Baseline Associations:
- Significant demographic influences on baseline knowledge and attitudes (e.g., area of residence, parental education, income).
- Intervention Impact:
- Both age groups showed statistically significant improvements from pre- to post-intervention on knowledge (t ≈ 13.8–16.0, p < 0.05) and attitudes (t ≈ 13.6–17.9, p < 0.05).
- Long-Term Stability:
- No further significant changes in follow‐up assessments (F < critical, p > 0.05), indicating the intervention’s effects were maintained over time.
Implications for Educational Psychology
- Customized Programming: Baseline demographic disparities highlight the necessity of tailoring interventions for different subgroups.
- Efficacy & Durability: The combined use of t-tests and ANOVA confirms both the immediate effectiveness and long-term stability of the educational program.
- Robust Methodology: Employing a suite of inferential methods yields a nuanced understanding of both who benefits and how benefits evolve.
Takeaways for Dissertation Researchers
- Multi-Method Inferential Strategy: Use categorical (chi-square) and continuous (t-tests, ANOVA) analyses for comprehensive evaluation.
- Sample Size & Power: Ensure adequate subgroup sizes to bolster statistical power and the reliability of your findings.
- Transparency & Reproducibility: Report test statistics, degrees of freedom, critical values, and p-values in detail—and accompany with annotated analysis scripts—to facilitate peer review and publication.
This anonymized case study demonstrates how rigorous inferential analysis drives actionable insights in a PhD thesis, without revealing sensitive subject matter.
Want to explore more PhD-level case studies? Check out our Comprehensive Case Studies on PhD Statistical Analysis guide page.
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