Introduction

Establishing validity requires a deliberate, multi-step process that includes operationalization, expert input, pilot testing, statistical analysis, and careful design and administration. These strategies work together to reduce error, improve construct alignment, and ensure the instrument accurately measures what it intends to.

Measurement: Seeking Standardization

Accuracy and consistency are foundational principles in measurement. Consistency, better known as reliability, refers to the stability of an instrument, meaning it produces similar results under consistent conditions. Accuracy, or validity, refers to the extent to which an instrument truly measures what it is intended to measure, rather than capturing unrelated or confounding variables. While reliability and validity address different aspects of measurement, both are essential for ensuring replicability and confidence in research findings. Reliable and valid instruments enable researchers to test, refine, and challenge ideas in a standardized way. Without these principles, it would be difficult to interpret or build upon others’ work with any degree of certainty.

Although reliability and validity work together, they are distinct concepts. Improving reliability focuses on reducing random error, while improving validity addresses systematic bias and ensures the instrument is measuring the intended construct. Validity, in particular, is critical when developing or refining an instrument because it directly impacts the accuracy of the conclusions drawn.[1] To strengthen the validity of an instrument, researchers can apply specific strategies throughout the development process. In an effort to share some of these strategies, this guide outlines five key approaches to help mitigate bias, control for confounding factors, and ultimately enhance the validity of the instruments you design.

5 Strategies for Enhancing Validity

1. Operationalization
   As with any research-oriented task, the foundation for establishing validity begins with rigorous preliminary work, particularly in how you choose to operationalize your construct. This initial stage requires collecting and synthesizing both theoretical and empirical information related to the concept you aim to measure. To fully define the construct, it is essential to examine not only closely related concepts but also those that are theoretically unrelated, in order to clarify its boundaries and scope. This process leads to the development of operational definitions, which specifies exactly what variables are being measured, how they will be measured, and how the results should be interpreted. While this might appear straightforward, operationalization is often a nuanced and context-dependent task. Factors such as the target population, setting, and purpose of measurement can all influence how a construct should be defined. Without careful consideration, this stage can introduce construct underrepresentation or construct-irrelevant variance, both of which compromise validity by allowing bias or confounding factors to distort the results.[2]
2. Subject Matter Expert (SME) Involvement
   While validity refers to the overall accuracy of an instrument, it is composed of several types, each addressing a different aspect of measurement. One of the most critical among these is content validity, which ensures that the instrument fully represents the construct it is intended to measure. Although content validity begins during operationalization, involving subject matter experts (SMEs) provides a necessary layer of theoretical and practical insight. SMEs can help evaluate whether the items adequately reflect the full range of the construct and whether anything essential is missing or misrepresented. Their input helps refine item wording, identify gaps, and confirm that the instrument aligns with established knowledge in the field. In doing so, SME involvement strengthens the content foundation of the instrument and guards against construct underrepresentation and bias introduced by researcher assumptions alone.[3]
3. Pilot Test
   Shifting slightly from earlier strategies, pilot testing is a valuable and practical method for ensuring the validity of research instruments. Essentially, conducting a pilot test means administering the instrument to a small group of individuals who share similar characteristics with the target population. If the instrument performs well in this context, it suggests that it will likely be effective when used on the broader population. Conversely, poor performance in a pilot test raises concerns about the instrument’s psychometric properties and, by extension, the validity of the study’s results. Additionally, pilot tests also offer another key advantage: they allow researchers to identify and correct issues on a smaller, more manageable scale. Errors are often more noticeable in these small samples, making it easier to revise the instrument, re-test, and reassess. This iterative process continues until the research team is confident in the instrument’s effectiveness, ultimately strengthening validity by enhancing the design and overall quality of the study.[4]
4. Statistical Techniques
   In addition to the methods previously discussed, several statistical techniques can be employed to investigate and enhance the validity of a research instrument. While background knowledge and theoretical research form the foundation for instrument development, statistical analyses provide empirical evidence that can refine and strengthen its psychometric properties, particularly validity. One of the most widely used techniques is factor analysis, which includes both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is used in the early stages of instrument development when the factor structure is unknown. It identifies patterns among items and suggests how many underlying constructs may exist. CFA, on the other hand, is applied when a theoretical structure is already proposed. It tests how well the data fit that model. While EFA is exploratory and data-driven, CFA is confirmatory and theory-driven. Used together, they provide strong, complementary evidence for an instrument’s validity.[5]
5. Design and Administration
   Even with sound theoretical foundations and rigorous statistical validation, the success of a research instrument ultimately depends on how it is designed and administered. A poorly designed instrument can obscure results even in best conditions, while a well-designed instrument can still yield invalid data if administered inconsistently. Thoughtful design involves ensuring that language is clear, the reading level is appropriate, and that population-specific characteristics such as age, cultural background, or income level are taken into account. Just as careful design supports validity, so too does consistent administration. Standardization is essential; all participants should experience as similar testing conditions as possible to reduce external sources of error. For example, an identical assessment may produce different results if one participant takes it in a quiet, comfortable room while another is in an uncomfortably hot or noisy environment. Overlooking these practical details can introduce unintended variability that undermines the instrument’s validity, regardless of how well it was constructed.[6]

Validity: A Closing Note

Designing a valid research instrument is not a one-time decision but an ongoing process that requires deliberate attention at every stage. From clearly operationalizing constructs to involving subject matter experts, each step works to refine the instrument’s ability to capture the intended variable accurately. Pilot testing offers a valuable opportunity to detect and address weaknesses before full-scale implementation, while statistical techniques like factor analysis provide empirical support for the instrument’s structure. Finally, thoughtful design and standardized administration ensure that validity is not compromised during implementation. Taken together, these five strategies serve as a framework for minimizing bias, controlling for confounding variables, and strengthening the accuracy of measurement. By approaching validity as a process rather than a checkbox, researchers can improve the quality, credibility, and replicability of their work. Ultimately, valid instruments do more than yield clean data—they support meaningful, trustworthy conclusions that contribute to scientific progress.

Take Away

Validity doesn’t happen by accident; it’s built through intentional decisions made throughout the instrument development process. Each strategy enhances a specific aspect of measurement, from construct clarity to environmental control. When combined, they lead to stronger, more trustworthy research findings.

[1] Johnson, C. (2025, March 9). Validity and Reliability in Research | Types and How to Improve them. Dissertation Data Analysis Help. https://dissertationdataanalysishelp.com/validity-and-reliability-in-research/

[2] Press, M. O. (2018). 10.3 Operational definitions. Doctoral Research Methods in Social Work; Pressbooks. https://uta.pressbooks.pub/advancedresearchmethodsinsw/chapter/10-3-operational-definitions/

[3] Purpose and Value of Subject Matter Experts. (2022, September 16). Industrial/Organizational Solutions. https://iosolutions.com/purpose-value-subject-matter-experts/

[4] In, J. (2017). Introduction of a pilot study. Korean Journal of Anesthesiology, 70(6), 601. https://doi.org/10.4097/kjae.2017.70.6.601

[5] Fein, E. C., Gilmour, J., Machin, T., & Hendry, L. (2022, June 16). Section 8.2: EFA versus CFA. Statistics for Research Students; University of Southern Queensland. https://usq.pressbooks.pub/statisticsforresearchstudents/chapter/efa-versus-cfa/

[6] Sullivan, G. M., & Artino, A. R. (2017). How to Create a Bad Survey Instrument. Journal of Graduate Medical Education, 9(4), 411–415. https://doi.org/10.4300/jgme-d-17-00375.1