Handling Conflicting Evidence in Dissertation Discussions
Written By
Marcus Whitfield
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Contents
- How to Write About Conflicting Evidence in Your Dissertation
- Presenting Conflicting Evidence in the Literature Review
- Addressing Conflicting Evidence in Your Discussion
- Distinguishing Between Equivocation and Genuine Analysis
- Why Engaging With Contradictions Raises Your Grade
- Common Mistakes to Avoid
How to Write About Conflicting Evidence in Your Dissertation
Every dissertation has a story. Yours does too. Tell it well. Start with a clear problem. Build your case. Present your evidence. Draw your conclusion. It sounds simple. With guidance, it becomes simple. We provide that guidance every day.
Most students treat contradictory findings as a problem to glosse over. They present conflicting studies without acknowledging the conflict or attempting to explain it. This approach costs marks. Conflicting evidence is the most intellectually interesting part of any field. Your dissertation should engage with it, not avoid it.
When Smith's 2018 study found X, but Jones's 2020 study found Y, that's not a mess. That's your actual dissertation topic. What might explain the different findings? Your job is to figure that out and explain it clearly.
Presenting Conflicting Evidence in the Literature Review
Acknowledge the conflict directly. Don't hide it in vague language. Write something like: "Recent research produces contradictory findings about the relationship between social media use and adolescent mental health. Twenge et al. (2019) found a considerable positive correlation between daily screen time and depressive symptoms in a nationally representative sample. Conversely, Oxford et al. (2020) found no association between social media use and depression in a longitudinal study of 500 UK adolescents."
Now you've presented the conflict clearly. Your reader knows the field contains contradictory findings, not a settled consensus.
Identify what might explain the contradiction. Methodological differences are often the culprit. Maybe Smith used a survey with self-report measures of depression (vulnerable to bias). Jones used clinical diagnostic interviews (more rigorous). Maybe Smith measured correlation. Jones measured causation. Maybe Smith's sample was American teenagers. Jones's was British children. Sample differences matter.
Time period matters too. If one study was conducted in 2005 and another in 2021, social media has changed profoundly. Instagram, TikTok, and Discord were nonexistent in 2005. The technology studied is different, so different effects are entirely plausible.
Theoretical disagreements explain some conflicts. Maybe some researchers assume social media is intrinsically harmful (addiction model). Others assume effects depend on how people use it (agency model). The same data might support both views depending on theoretical framing.
Avoid equivocation. Don't write: "Some studies find social media harms mental health, while others find no effect." This presents the conflict but doesn't analyse it. It's lazy. It abandons your responsibility to think through the evidence.
Addressing Conflicting Evidence in Your Discussion
If your findings agree with some studies and disagree with others, explain why. This requires genuine analytical engagement. Maybe your sample is different. Maybe you measured variables differently. Maybe you used more rigorous methods. Maybe the field has changed since earlier research was published.
There's no shame in asking for help. In fact, it's one of the smartest things you can do when you're working on something as important as your dissertation. The students who do best aren't always the ones who know the most at the start; they're the ones who've got the support they need and who've learnt to ask the right questions. You're already doing that by being here. Let's take it from there.
Suppose you found that social media use correlates with depression, consistent with Twenge but inconsistent with Oxford. Your discussion might read: "These findings align with Twenge et al.'s large-scale survey but contradict Oxford et al.'s longitudinal analysis. The discrepancy is likely explained by measurement differences. Twenge used single-item depression screening; Oxford used multiple clinical diagnostic criteria. Single-item measures are more vulnerable to overlap between constructs. Participants reporting heavy social media use may rate themselves as depressed partly because they're tired or because social media use is socially constructed as harmful, not purely because they're clinically depressed. Oxford's more stringent diagnostic approach may have excluded false positives that Twenge's measure captured."
This's genuine analysis. You're not just noting the contradiction. You're explaining a plausible mechanism.
Distinguishing Between Equivocation and Genuine Analysis
Equivocation sounds like: "The literature is mixed about whether homework improves achievement. Some studies show benefits, while others show no effect." This tells readers nothing useful. It's a cop-out.
Genuine analysis sounds like: "Kohn (1999) argues homework harms motivation and increases inequality; his review emphasises studies showing no achievement gains. Bennett and Kalish (2006) report considerable achievement gains from homework in meta-analysis; however, their analysis includes studies where homework intensity was substantially higher than typical UK practice. The conflicting conclusions partly reflect studies examining different homework types and durations. UK research using typical homework levels (one to two hours weekly) generally shows modest achievement gains, around 0.2 standard deviations. This's consistent with Bennett and Kalish but smaller than Kohn's pessimism suggests. The contradiction is largely explained by differences in homework intensity across studies."
See the difference? The second version actually engages with why the studies differ.
Choosing an appropriate research methodology is one of the most consequential decisions you will make during your dissertation, as the methods you select will shape every aspect of your data collection and analysis process. Qualitative research methods are generally most appropriate when you are trying to understand the meanings, experiences, and perspectives of participants, while quantitative methods are better suited to testing hypotheses and measuring relationships between variables. Many dissertations combine both qualitative and quantitative approaches in what is known as a mixed-methods design, which can provide a richer and more complete picture of the research problem than either approach could achieve alone. Whatever methodology you choose, you must be able to justify your selection clearly and demonstrate that your chosen approach is consistent with your research question, your philosophical assumptions, and the practical constraints of your study.
Why Engaging With Contradictions Raises Your Grade
Examiners expect you to handle a complex, contested field. If you pretend your field is settled, you show shallow engagement. If you acknowledge complexity and work through it, you show sophisticated understanding.
Don't underestimate this. Your dissertation's abstract, introduction, and conclusion are the sections your marker will read most carefully, and they need to work together to convey a coherent research narrative in which your question, your methodology, your findings, and your interpretation of those findings all connect logically and build towards a genuinely meaningful conclusion. We'll help you get all of that right.
Conflicting evidence gives you material to demonstrate critical thinking. You must evaluate studies, consider methodology, judge which findings are more credible and why. This's exactly what postgraduate research demands.
Avoiding contradictions makes your work look defensive. Engaging with them openly shows confidence. You're not worried contradictory findings will undermine you. You can explain them.
Common Mistakes to Avoid
Don't dismiss studies because they contradict your expectations. Instead, figure out why they found different results. Maybe they're wrong. Maybe you're wrong. Maybe you're both investigating different aspects of the same phenomenon.
Don't create false equivalence. Not all studies are equally rigorous. A study with 50 participants and self-report measures deserves less weight than a study with 5,000 participants and validated diagnostic instruments. When addressing conflicting evidence, acknowledge that some evidence is stronger than other evidence.
Don't ignore contradictions and hope examiners don't notice. They'll notice. They'll mark you down for it. Acknowledging contradictions head-on is far smarter.
Don't attribute all contradictions to methodology. Sometimes researchers genuinely disagree theoretically. They're not studying different things. They're interpreting the same phenomenon through different frameworks. That's intellectually legitimate. Acknowledge it.
Frequently Asked Questions
Q: What if I don't understand why studies contradict each other? A: Say so. Write something like: "The reasons for this contradiction are unclear. Both studies used similar samples and measures, yet reached opposite conclusions. This may reflect publication bias, where non-considerable findings are less likely to be published, or it may reflect genuine variability in the phenomenon across contexts." Admitting uncertainty is better than inventing explanations.
Q: Should I take a side, or remain neutral about contradictory evidence? A: You should evaluate the evidence and state which findings you find more credible and why. Neutrality is false when evidence quality differs. It's intellectual cowardice. State plainly which evidence you find more convincing and justify that judgement.
Q: Can conflicting evidence mean my research question is unanswerable? A: Possibly. But that's intellectually valuable knowledge. If the evidence genuinely contradicts, that might mean the phenomenon is complex, context-dependent, or that key variables are being missed. That could shape your discussion into something more interesting than pretending the field is settled.
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