Ensuring Validity and Reliability in Your Findings

by Collette Dowd on September 3, 2025

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Establishing Trustworthiness in Your Findings

Establishing Trustworthiness in Your Research Data

The value of your entire dissertation is built upon the soundness of your findings. A perfectly structured dissertation is worthless if your reader has reason to doubt the truthfulness of your results. This is why the twin pillars of scientific inquiry—validity and reliability—are not just academic terms; they are the non-negotiable foundation upon which scholarly contribution is built. Demonstrating that your study is both valid and reliable is a critical task that must be woven into every stage of your research design. This deep dive will demystify these fundamental principles and provide a practical roadmap for ensuring and documenting them in your dissertation.

1. The Core Concepts Demystified

Before you can ensure something, you must understand it. These concepts are often confused but are separate and unique. <img src="https://persiancampus.com/wp-content/uploads/2022/06/5.png"; style="max-width:410px;float:left;padding:10px 10px 10px 0px;border:0px;" alt="" />

Reliability: Refers to the repeatability of your data collection. If you administered your test again under the same conditions, would you get the similar results? A reliable measure is consistent and not overly influenced by chance.

Analogy: A reliable scale gives you the same weight if you step on it three times in a row.

Validity: Refers to the correctness of your interpretations. Are you actually measuring what you intend to measure? A valid measure is accurate and free from systematic error.

Analogy: A valid scale gives you your correct weight, not just a consistent wrong one.

To summarize: Reliability is about getting the same result repeatedly; Validity is about getting the right result.

2. Ensuring Reliability

You must proactively address reliability throughout your research <a href="https://healthtian.com/?s=design%20phase">design phase</a>. Key strategies include:

For Survey Data:

Internal Consistency (Cronbach's Alpha): For questionnaires, this statistic measures how closely related a set of items are as a group. A generally accepted rule of thumb is that an alpha of .70 or higher indicates acceptable reliability. You should report this statistic for any scales you use.

Test-Retest Reliability: Giving the same survey to the same participants at two different points in time and comparing the scores between them. A high correlation indicates the measure is stable over time.

Inter-Rater Reliability: If your study involves human judgment, have multiple people code the same data independently. Then, use statistics like Intraclass Correlation Coefficient to measure the level of agreement between them. A high level of agreement is crucial.

For Qualitative Research:

Code-ReCode Reliability: The researcher analyzes the same transcript at two different times and checks for consistency in their own application of codes.

Peer Debriefing: Talking through your interpretations with a supervisor to check for potential biases.

Audit Trail: Keeping a detailed record of every decision you take during data collection and analysis so that another researcher could, in theory, follow your path.

3. Measuring the Right Thing

Validity is multifaceted and comes in several important forms that you should address.

For Quantitative Research:

Content Validity: Does your measure fully represent the domain of the concept you're studying? This is often established through review by specialists who evaluate your survey items.

Criterion Validity: Does your measure perform consistently against a well-accepted measure of the same concept? This can be measured at the same time or measured in the future.

Construct Validity: The umbrella term. Does your measure behave as expected with theoretical predictions? This is often established by showing your measure is unrelated to dissimilar constructs.

Internal Validity: For experimental designs, this refers to the certainty that the manipulation caused the change in the dependent variable, and not some other confounding variable. Control groups, random assignment, and blinding are used to protect internal validity.

External Validity: The extent to which your results can be applied to other settings. This is addressed through how you select participants.

For Qualitative Research:

Credibility: The qualitative equivalent of internal validity. Have you accurately represented the participants' perspectives? Techniques include prolonged engagement.

Transferability: The qualitative equivalent of external validity. Instead of generalization, you provide rich, thick description so readers can decide if the findings transfer to their own context.

<a href="https://ajt-ventures.com/?s=Dependability">Dependability</a>; & Confirmability: Similar to reliability. Dependability refers to the stability of the findings over time, and confirmability refers to the objectivity of the data (i.e., the findings are shaped by the participants, <a href="http://www.andreagorini.it/SalaProf/profile/penneysager3344/">IGNOU project approval</a> not researcher bias). The detailed documentation is key here.

4. What to Do and Report

You cannot just claim your study is valid and reliable; you must provide evidence for it. Your analysis section should include a clear discussion on these issues.

For Reliability: Report reliability coefficients for any scales used. Describe steps taken to ensure consistency in coding and report the agreement statistic.

For Validity: Cite previous literature that have established the validity of your measures. If you created a new instrument, describe the steps you took to ensure its face validity (e.g., expert review, pilot testing). Acknowledge potential limitations in your design (e.g., sampling limitations that affect external validity, potential confounding variables).

For Qualitative Studies: Explicitly describe the techniques you used to ensure rigor (e.g., "Member checking was employed by returning interview transcripts to participants for verification," "Triangulation was achieved by collecting data from three different sources," "An audit trail was maintained throughout the analysis process.").

5. Acknowledging Limitations

No study is perfectly valid and reliable. There are always trade-offs. Increasing control might weaken external validity. The key is to be aware about these limitations and discuss them openly in your dissertation's discussion chapter. This transparency actually strengthens your credibility as a researcher.

Conclusion

Validity and reliability are not items on a checklist to be addressed at the end. They are fundamental concerns that must inform every decision, from choosing your measures to analyzing your data. By meticulously planning for them, meticulously testing for them, and transparently reporting them, you do more than just pass a methodological hurdle; you construct a compelling argument around your findings. You assure your reader that your hard-won conclusions are not a fluke but a trustworthy, accurate, and reliable contribution to knowledge.

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