PhD Interview Questions

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PhD admission interviews and viva voce examinations frequently test your understanding of research methodology. This guide provides detailed answers to the most commonly asked questions, explained at the depth expected of doctoral-level candidates. Understanding these concepts thoroughly demonstrates research readiness.

Foundational Questions

Q1: What is the difference between research methodology and research methods?

Research methods are the specific techniques and tools used to collect and analyze data—surveys, interviews, experiments, statistical tests, thematic analysis. They are the "what" you use.

Research methodology is the broader framework that guides your choice of methods—including your philosophical assumptions about knowledge (epistemology), the nature of reality (ontology), and the logical reasoning connecting your research design to your questions. It is the "why" behind your methodological choices.

For example, a positivist methodology assumes objective reality that can be measured, leading to quantitative methods. An interpretivist methodology assumes socially constructed reality, leading to qualitative methods. The methodology justifies the methods.

Q2: Explain the differences between positivism and interpretivism.

Positivism holds that reality is objective, observable, and measurable. Knowledge comes from empirical observation and logical analysis. The researcher is detached and neutral. This paradigm underpins most quantitative research—experiments, surveys, hypothesis testing.

Interpretivism holds that reality is socially constructed—people create meaning through their interactions and experiences. Knowledge is subjective and context-dependent. The researcher is involved and interprets meaning. This underpins qualitative research—interviews, ethnography, phenomenology.

Key differences:

Q3: What is validity and reliability? How do they relate?

Reliability = consistency of measurement. If you measure the same thing repeatedly, do you get the same results? Assessed through Cronbach's alpha, test-retest correlation, and inter-rater agreement.

Validity = accuracy of measurement. Does the instrument measure what it claims to measure? Assessed through content validity (expert judgment), construct validity (factor analysis, convergent/discriminant), and criterion validity (correlation with outcomes).

Relationship: Reliability is necessary but NOT sufficient for validity. A scale can be highly reliable (consistent) while measuring the wrong construct (invalid). However, an unreliable instrument cannot be valid—random measurement error prevents consistently capturing any construct.

Q4: How do you determine sample size for your study?

Sample size depends on:

1. Analysis type: Different tests require different minimum samples
2. Expected effect size: Smaller effects need larger samples to detect
3. Desired power: Typically 0.80 (80% chance of detecting real effects)
4. Significance level: Usually α = 0.05

For quantitative studies, use G*Power software or formulas:

• t-test, medium effect (d=0.5): ~64 per group
• Correlation, medium (r=0.3): ~85 total
• Regression, 5 predictors, medium R²: ~92 total

For qualitative studies, use data saturation—continue until new participants add no new themes. Typical ranges: phenomenology (5-25), grounded theory (20-60), case study (1-5 cases).

Q5: What is triangulation? Why is it important?

Triangulation means using multiple sources, methods, or perspectives to study the same phenomenon, increasing confidence in findings.

Types:

• Data triangulation: Multiple data sources (interviews + documents + observations)
• Method triangulation: Multiple methods (survey + interview)
• Investigator triangulation: Multiple researchers analyzing the same data
• Theory triangulation: Multiple theoretical lenses interpreting findings

Importance: No single method is perfect. Triangulation compensates for individual method weaknesses, strengthens validity, and provides richer understanding. If multiple methods converge on the same conclusion, confidence increases substantially.

Advanced Questions

Q6: Explain the difference between mediating and moderating variables.

Mediator: Explains HOW or WHY the IV affects the DV. It is the mechanism or pathway. Example: Exercise (IV) → Self-efficacy (Mediator) → Academic performance (DV) Exercise improves performance BECAUSE it builds confidence. Statistical test: Mediation analysis (Baron & Kenny, PROCESS macro bootstrapping)

Moderator: Changes WHEN or FOR WHOM the IV affects the DV. It is the boundary condition. Example: Feedback (IV) → Performance (DV), moderated by self-esteem. Feedback improves performance MORE for high self-esteem individuals. Statistical test: Interaction term in regression, moderation analysis.

Q7: What are Type I and Type II errors?

Type I (α): False positive—concluding an effect exists when it does not. Probability = α level (usually 0.05). Consequence: Acting on a finding that is not real.

Type II (β): False negative—missing a real effect. Probability = β (typically 0.20, giving power = 0.80). Consequence: Concluding no effect when one actually exists.

Trade-off: Reducing Type I error (stricter α) increases Type II error risk, and vice versa. The only way to reduce both simultaneously is to increase sample size.

Q8: When would you use non-parametric tests?

Use non-parametric tests when:

• Data violates normality assumptions (significantly skewed)
• Sample size is very small (n < 30) and normality cannot be assumed
• Data is ordinal (ranked categories, single Likert items)
• Outliers are present and cannot be removed
• Distribution-free inference is preferred

Common substitutions:

Q9: What is your understanding of research ethics?

Research ethics encompasses principles ensuring research is conducted responsibly:

• Respect for persons: Informed consent, autonomy, privacy
• Beneficence: Maximize benefits, minimize harm, favorable risk-benefit ratio
• Justice: Fair distribution of research benefits and burdens

Practical applications include: ethics committee approval, informed consent documentation, confidentiality measures, right to withdraw, honest data reporting, proper authorship attribution, and avoiding fabrication/falsification/plagiarism.

Q10: How would you handle contradictory findings in your results?

1. Report them transparently—never hide non-significant or contradictory results
2. Examine possible methodological explanations (measurement issues, sample characteristics)
3. Consider theoretical explanations (boundary conditions, moderating factors)
4. Compare with existing literature—do other studies show similar contradictions?
5. Discuss as a contribution (identifying when a theory does NOT apply is valuable)
6. Suggest future research to resolve the contradiction

Conclusion

PhD interviews assess not just knowledge but research thinking. Examiners want to see that you understand WHY methodological choices matter, not just WHAT the choices are. Practice articulating the reasoning behind your decisions—this demonstrates the intellectual maturity expected of doctoral researchers.