Case Study: Integrative Inferential Methods in a Quasi-Experimental PhD Study

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Background & Research Objectives for this quasi‑experimental PhD inferential methods study

In a quasi‑experimental phd inferential methods PhD study, researchers sought to evaluate a school-based intervention’s impact on knowledge and attitude scores, explore baseline relationships among demographic and psychological variables, and compare subgroup responses. To address these multifaceted goals, the following inferential techniques were deployed:

1. Chi-Squared Test – Assess associations between categorical demographics and baseline scores.
2. Paired t-Test – Examine within-subject changes pre- to post-intervention.
3. Repeated Measures ANOVA – Evaluate durability of effects across multiple post-tests.
4. Pearson Correlation – Quantify linear relationships between knowledge and attitude scores.
5. Independent t-Test – Compare mean scores between children and adolescent cohorts at baseline.

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Study Design & Data for quasi‑experimental PhD inferential methods study

• Sample: N = 217 (Children vs. Adolescents) from urban and rural schools.
• Measures:
  • Knowledge Score: 0–20 scale (pre-, post-, and follow-up at O2, O3, O4).
  • Attitude Score: 0–72 scale (pre-, post-).
• Demographics: Area of residence, gender, parents’ education, family income.
• Subgroups:
  • Children Group: n = 120
  • Adolescent Group: n = 97

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Analytic Workflow & Key Findings for this quasi‑experimental PhD inferential methods study

1. Baseline Associations (Chi-Squared Test)

Objective: Identify which demographic factors relate to initial knowledge and attitude levels.

• Test: χ² on 2×2 tables (e.g., high vs. low pre-test knowledge by area).
• Example Finding: Pr-intervention knowledge differed significantly by residence (χ²(1)=9.39, p=0.00218), parental education (χ²(1)=5.33, p=0.021), and income category .

2. Within-Subject Change (Paired t-Test)

Objective: Quantify immediate intervention effects on each participant.

• Test: Paired comparisons of pre- vs. post-test means separately for each age group.
• Example Finding (Adolescents): Knowledge gain was significant (t=13.84 > 1.98, p<0.05), as was attitude improvement (t=17.91 > 1.98, p<0.05) .

3. Durability of Effect (Repeated Measures ANOVA)

Objective: Determine if gains persisted, increased, or waned over three subsequent follow-ups.

• Test: One-way repeated measures ANOVA across O1–O4 scores.
• Example Finding (Children): No significant change beyond the immediate post-test (F=0.24 < 3.09, p>0.05), indicating stable knowledge retention .

4. Inter-Measure Relationship (Pearson Correlation)

Objective: Assess the linear association between knowledge and attitude at baseline.

• Test: Pearson’s r on paired scores for all participants.
• Example Finding: A moderate, positive correlation (r≈0.56, p<0.001) suggested that participants with higher initial knowledge also held more positive attitudes.

5. Between-Group Comparison (Independent t-Test)

Objective: Compare baseline means between children and adolescents to identify group differences.

• Test: Two-sample t-test assuming equal variances (verified via Levene’s test).
• Example Finding: Adolescents had a significantly higher mean pre-test knowledge (M=12.3, SD=3.1) than children (M=10.1, SD=3.7); t(215)=4.67, p<0.001.

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Research Implications

• Comprehensive Insight: Layering categorical, within-subject, between-group, correlational, and longitudinal analyses yields a multidimensional understanding of program efficacy.
• Tailored Interventions: Baseline demographic associations inform targeted program adjustments (e.g., augmenting content for lower-income or rural subgroups).
• Evidence of Retention: Stable follow-up scores underscore the intervention’s durability, justifying resource allocation for sustained delivery.
• Inter-measure Dynamics: Correlation between knowledge and attitude guides combined pedagogical strategies—enhancing cognitive content alongside affective components.

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Best Practices for Methodology-Centered Research

1. Align Tests with Objectives: Choose inferential techniques that directly address each research question—don’t force a one-size-fits-all approach.
2. Validate Assumptions: For t-tests and ANOVA, confirm normality, homogeneity of variances, and sphericity (or apply corrections).
3. Report Transparently: Include test statistics, degrees of freedom, critical values, p-values, and effect sizes to support reproducibility.
4. Integrate Automated Workflows: Use annotated R scripts and macros to standardize analyses—minimizing manual error and accelerating iteration.
5. Document Ethical Compliance: Embed data-audit steps (missing-data checks, outlier protocols, consent verification) throughout your pipeline.

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This methodology-centered case study exemplifies how a strategic combination of inferential methods can fulfill distinct research objectives within a quasi-experimental PhD thesis.

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