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Viva and Thesis Defence Questions by Research Design: Quantitative, Qualitative and Mixed Methods (UAE & GCC, 2026)
For master’s, PhD and DBA candidates preparing a defence: how examiners probe each research design, the questions to expect, what a strong answer demonstrates, and how to prepare your own answers.
Read this first: examiners test your design decisions
Most methodology questions in a viva ask the same two things: does your method fit your research question, and do you understand the consequences of the choices you made? What they probe depends on your design.
- You answer the examiners. This page shows what a strong answer demonstrates, not a script. Memorised answers break at the first follow-up.
- Examiners read before they ask. Research with 30 experienced examiners (Mullins and Kiley, 2002) describes the judgements examiners make as they read a thesis, so viva questions usually target what they noticed on the page.
- General preparation is in our free guide to preparing your thesis for review and defence. This page covers design-specific questions.
Quantitative theses: sampling, power, measurement and effect sizes
Examiners of survey and experimental theses follow a chain: the population you meant to study, the sample you reached, measures that work, tests whose assumptions hold, and conclusions no stronger than the design allows.
| Likely question | What a strong answer demonstrates |
|---|---|
| 1. Who is your sample, and who does it represent? | You name the population, sampling frame and method, and say how far the results generalise. If you used a convenience or snowball sample, as many workplace surveys do, you state what that limits before the examiner does. |
| 2. How did you decide the sample size? | A rationale set before data collection: a power analysis with the test, alpha, target power (conventionally .80, Cohen, 1992) and expected effect size, for example in G*Power (Faul et al., 2007), or the requirement of your model. A number with no parameters is the weak answer. |
| 3. Was the study powered to detect the effects you tested? | You know that a non-significant result from an underpowered study is inconclusive, not proof of no effect, and you word your conclusions that way. |
| 4. How do you know the instrument measures what you claim? | The origin of each scale, how you adapted or translated it (for example English–Arabic with back-translation), the pilot, and construct validity evidence. With PLS-SEM, expect questions on discriminant validity: Henseler, Ringle and Sarstedt (2015) showed the Fornell–Larcker criterion often misses problems and proposed the HTMT ratio. |
| 5. How reliable are your measures? | Reliability for each scale, what the figure does and does not mean, and what you did about any scale that fell short. |
| 6. Did your data meet the assumptions of your tests? | You name the assumptions that matter (for regression: linearity, independence, equal variance and normality of residuals, multicollinearity), how you checked each, and what you did when one failed. |
| 7. What are your effect sizes, and do they matter in practice? | Effect sizes and confidence intervals, not only p-values. The American Statistical Association states that a p-value does not measure the size of an effect or the importance of a result. Cohen’s benchmarks (Cohen, 1988) are a fallback; say what your effect means in your setting. |
| 8. All your data came from one questionnaire at one time. What about common method bias? | The procedural steps you took (anonymity, separated scales, different sources where possible) and the statistical check you ran, knowing that a single-factor test alone is weak evidence (Podsakoff et al., 2003). |
| 9. How did you handle missing data and outliers? | A stated rule, the number of cases affected, and whether the main results change when those cases are treated differently. |
| 10. Can you claim that X causes Y? | Causal language that matches the design. A cross-sectional survey supports “is associated with”; stronger claims need an experimental or longitudinal design. |
Know your key numbers without looking: sample size, response rate, scale reliabilities and your main coefficients.
Qualitative theses: sampling logic, saturation, trustworthiness, reflexivity and coding
Qualitative examiners rarely ask “is the sample big enough?” in statistical terms. They ask whether your sampling, analysis and quality claims are consistent with the approach you said you used.
| Likely question | What a strong answer demonstrates |
|---|---|
| 1. Why a qualitative design, and why this approach? | The question asks about meaning, experience or process, and your approach (thematic analysis, interpretative phenomenological analysis, grounded theory, case study) fits both the question and your stated philosophical position. |
| 2. How and why did you choose these participants? | A purposive logic: the criteria, why these people hold the information you need, how you reached them, and whose voice is missing. |
| 3. How did you decide how many participants? Did you reach saturation? | Say which kind of saturation you mean; Saunders et al. (2018) identify four approaches. Or give another rationale, such as information power (Malterud et al., 2016). Guest et al. (2006) found saturation within twelve of sixty interviews in their dataset: a finding about one study, not a rule. For reflexive thematic analysis, Braun and Clarke (2021) argue saturation does not fit. |
| 4. Why should I trust your findings? | The criteria of Lincoln and Guba (1985) (credibility, transferability, dependability, confirmability), each matched to a technique you actually used, such as member checking, thick description, peer debriefing or an audit trail. |
| 5. Where are you in this research? | Reflexivity with specifics. If you studied your own organisation, as many DBA candidates do, explain how insider status shaped access, what participants told you and what you noticed, and how you managed it. |
| 6. Walk us from a transcript to one of your themes. | You trace one theme back through codes to quotes, following a recognisable process such as the six phases of Braun and Clarke (2006). Software organised the data; you did the analysis. |
| 7. Did anyone else code your data? Why is there no inter-coder agreement? | An answer consistent with your approach. Coding-reliability approaches may report agreement; in reflexive thematic analysis the researcher’s subjectivity is a resource, so coding reliability does not apply (Braun and Clarke, 2024). |
| 8. Are these themes, or just topics? | Each theme is a pattern of shared meaning around a central idea, not a heading such as “Challenges” that collects everything said on a topic (Braun and Clarke, 2024). |
| 9. How did language and translation affect your data? | If interviews were in Arabic and the thesis is in English: who translated, how you protected meaning, and how you checked the quotes you present. |
| 10. Can your findings be generalised? | Transferability, not statistical generalisation: enough context for readers to judge whether the findings apply to theirs. |
The commonest weakness is inconsistency: an interpretive approach reported with “themes emerged” and inter-coder percentages, or saturation cited without saying what saturated.
Mixed methods theses: rationale, sequencing, priority and integration
Expect the strand questions above, plus questions on how the strands work together. Two results chapters that never meet read as two studies.
| Likely question | What a strong answer demonstrates |
|---|---|
| 1. Why did you need both kinds of data? | A reason neither strand meets alone: explaining survey results, building an instrument from interviews, or comparing two views of one problem. “More data is better” is the weak answer. |
| 2. Which mixed methods design did you use? | You name it, for example one of the three core designs in Creswell and Plano Clark (convergent, explanatory sequential, exploratory sequential), and show that your study follows it. |
| 3. Why this sequence? | The timing follows the purpose: qualitative after quantitative to explain results, or before it to develop a measure. |
| 4. Which strand had priority? | Equal or dominant, and visible in the research questions, analysis depth and chapter weight. In Morse’s (1991) notation, capitals mark priority and arrows mark sequence: QUAN → qual. |
| 5. Where exactly did integration happen? | The central question. Fetters, Curry and Creswell (2013) describe integration at the design, methods (connecting, building, merging, embedding) and interpretation levels (narrative, data transformation, joint displays). Point to the page where the strands meet. |
| 6. How did the first phase shape the second? | Concrete links: participants selected by survey score, or questionnaire items drawn from interview themes. |
| 7. What did you do when the strands disagreed? | Discordance reported and examined, not hidden. Fetters et al. describe the fit of integration as confirmation, expansion or discordance. |
| 8. How did you judge the quality of each strand? | Quantitative criteria for one strand, qualitative criteria for the other, and how conclusions from both were checked against each other. |
| 9. How are your two samples related? | Same participants, a subset or a different group, and what that means for comparing results. |
Before the viva, find the page where your integrated conclusion is stated.
Universal questions, answered through your design
Every viva asks about contribution, limitations and what you would change. A strong answer is specific to your design.
| Question | Quantitative | Qualitative | Mixed methods |
|---|---|---|---|
| What is your contribution? | A relationship, estimate or model tested in a population where it was not established. | A new understanding, concept or explanation of how something happens. | What the integrated result shows that neither strand shows alone. |
| What are the limitations? | Sampling, measurement and design limits, and the direction in which each may bias the results. | Who was not heard, your position as researcher, and the limits of transferability. | Limits in each strand, plus how well the strands were integrated. |
| What would you do differently? | For example a probability sample, a longitudinal design or a pre-registered analysis. | For example wider sampling, a second coder for discussion, or earlier member checking. | For example integrating earlier, or a joint display planned from the start. |
On “what would you do differently?”, give one or two real changes with reasons, without implying the thesis is unsound.
How to prepare: build a question map from your own thesis
- List every design decision in your methodology chapter: paradigm, design, sampling, sample size, instrument or interview guide, analysis, quality checks, ethics.
- For each decision, write four things: what you chose, the main alternative, why you chose yours, and the consequence for your findings. Add the page number where the thesis says it.
- Turn the tables above into your questions. Rewrite each relevant row with your own details, for example “Why 212 respondents from three free-zone companies?” rather than “How did you decide the sample size?”.
- Mark the weak spots. Where a decision is hard to defend, prepare an honest answer: what you did, why, and what it limits.
Rehearse out loud, with follow-ups
Answer each question aloud in one to two minutes: a direct answer, your reason, the evidence or page, then any limitation. Ask your rehearsal partner to follow every answer with “Why?” at least twice; that is how examiners test understanding. Rehearse with someone in your field and someone outside it, who will show you where you rely on jargon. Record a session and listen for over-long answers.
When you cannot answer a question
- Pause and clarify. Ask for the question to be repeated or rephrased. A few seconds of thought is normal.
- Say what you do know, then mark the edge of it: “I did not test that. I would expect…, and I would check it by…”.
- Never invent a figure, a result or a reference. A confident wrong answer does more harm than an honest gap.
- Treat it as a possible correction. If the question exposes something missing, note it; it may reappear in the examiners’ report.
What the defence format is at your university
Formats differ between universities, degrees and programmes: who examines, whether you present first, whether the defence is public or closed, how long it lasts, and which outcomes examiners can recommend. We do not list formats here. Read your graduate handbook or thesis regulations, and confirm the details with your supervisor or graduate studies office before you rehearse.
How Labeeb can help, and where we stop
You answer the examiners. Labeeb runs mock vivas with examiner-style questions drawn from your own thesis and design, and gives feedback on the answers you give, so you can rehearse under realistic follow-up questioning. We do not write answers for you to memorise, we never attend or answer on your behalf, and we do not guarantee any outcome.
Frequently asked questions
What questions are asked in a viva for a quantitative thesis?
Expect questions on who your sample represents, how you set the sample size, statistical power, the validity and reliability of your measures, test assumptions, and what your effect sizes mean in practice. Examiners also check that your causal language matches your design.
How do I answer “Did you reach saturation?” in a qualitative viva?
Say which kind of saturation you mean and how you judged it, or give the rationale you actually used, such as information power. If you used reflexive thematic analysis, know why Braun and Clarke argue that saturation does not fit it. Never quote a fixed number of interviews as a rule.
What do examiners mean by integration in a mixed methods thesis?
They want to see where your quantitative and qualitative strands meet: in the design, in how one phase built on the other, and in a discussion or joint display that combines both results.
What should I do if I cannot answer a viva question?
Pause, ask for the question to be repeated or clarified, and say what you do know. If you did not test something, say so and explain how you would check it. Never invent a figure or a reference.
Can Labeeb help me prepare for my viva?
Yes, with mock vivas using examiner-style questions drawn from your own thesis, and feedback on the answers you give. You answer every question yourself. We do not write answers for you to memorise, we never attend or answer on your behalf, and we do not guarantee any outcome.
How to cite this page (APA 7)
Labeeb Writing & Designs. (2026, September 28). Viva and thesis defence questions by research design: Quantitative, qualitative and mixed methods (UAE & GCC, 2026). https://labeeb.ae/viva-questions-by-research-design/
Sources
Last checked: 28 September 2026. Journal articles were checked against Crossref; books against the publisher’s page.
- Mullins, G., & Kiley, M. (2002). ‘It’s a PhD, not a Nobel Prize’: How experienced examiners assess research theses. Studies in Higher Education, 27(4), 369–386. https://doi.org/10.1080/0307507022000011507
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates (now Routledge). https://www.routledge.com/Statistical-Power-Analysis-for-the-Behavioral-Sciences/Cohen/p/book/9780805802832
- Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108
- Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
- Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
- Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
- Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: Exploring its conceptualization and operationalization. Quality & Quantity, 52(4), 1893–1907. https://doi.org/10.1007/s11135-017-0574-8
- Malterud, K., Siersma, V. D., & Guassora, A. D. (2016). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753–1760. https://doi.org/10.1177/1049732315617444
- Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications. https://us.sagepub.com/en-us/nam/naturalistic-inquiry/book842
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
- Braun, V., & Clarke, V. (2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Research in Sport, Exercise and Health, 13(2), 201–216. https://doi.org/10.1080/2159676X.2019.1704846
- Braun, V., & Clarke, V. (2024). A critical review of the reporting of reflexive thematic analysis in Health Promotion International. Health Promotion International, 39(3), daae049. https://doi.org/10.1093/heapro/daae049
- Creswell, J. W., & Plano Clark, V. L. Designing and conducting mixed methods research (4th ed.). SAGE Publications (published December 2025; the 3rd edition, 2018, describes the same three core designs). https://us.sagepub.com/en-us/nam/designing-and-conducting-mixed-methods-research/book269004
- Morse, J. M. (1991). Approaches to qualitative-quantitative methodological triangulation. Nursing Research, 40(2), 120–123. https://doi.org/10.1097/00006199-199103000-00014
- Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs: Principles and practices. Health Services Research, 48(6 Pt 2), 2134–2156. https://doi.org/10.1111/1475-6773.12117
Want to rehearse the questions your examiners are likely to ask about your design? Chat with Labeeb. You give every answer; we run the mock viva and the follow-up questions.