qualitative research using validity

Relevance of Reliability and Validity in Qualitative Research

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Traditional, quantitative concepts of validity and reliability are frequently used to critique qualitative research, often leading to criticisms of lacking scientific rigor, insufficient methodological justification, lack of transparency in analysis, and potential for researcher bias.

Alternative terminology is proposed to better capture the principles of rigor and credibility within the qualitative paradigm :

Validity in Qualitative Research

Validity focuses on the truthfulness and accuracy of findings.

Quantitative research, with its focus on objectivity and generalizability, prioritizes internal validity to establish cause-and-effect relationships between variables.

This involves carefully controlling extraneous factors to ensure the observed effects can be confidently attributed to the independent variable.

Qualitative research embraces a different epistemological framework, emphasizing subjectivity, contextual understanding, and the exploration of lived experiences.

In this paradigm, validity focuses on faithfully representing the perspectives, meanings, and interpretations of the participants.

The underlying goal remains to produce research that is rigorous, credible, and insightful, contributing meaningfully to our understanding of complex social phenomena.

This involves ensuring the research process and findings are trustworthy, authentic, and rigorous.

1. Trustworthiness

Validity in qualitative research, often referred to as trustworthiness , assesses the accuracy of findings as representations of the data, participants’ lives, cultures, and contexts.

Trustworthiness is an overarching concept that encompasses both credibility and transferability , reflecting the overall quality and integrity of the research process and findings. It signifies that the research is conducted ethically, rigorously, and transparently.

A central concept in achieving trustworthiness is methodological integrity , which emphasizes the importance of using methods and procedures that are consistent with the research question, goals, and inquiry approach.

Methodological integrity focuses on two key components: fidelity to the subject matter and utility of research contributions .

Fidelity to the Subject Matter

Fidelity to the subject matter emphasizes collecting data that capture the diversity and complexity of the phenomenon under study.

Qualitative research underscores the commitment to representing participants’ authentic perspectives and experiences faithfully and respectfully.

This goes beyond simply recording their words; it involves capturing the depth, complexity, and meaning embedded within their narratives.

Fidelity to the subject matter must demonstratee that the data is adequate to answer the research question and that the researcher’s perspectives were managed during both data collection and analysis to minimize bias.

Researchers should show that the findings are grounded in the evidence by using rich quotes and detailed descriptions of their engagement with the data. This is also referred to as thick, lush description.

Thick description involves going beyond surface-level observations to provide rich, detailed accounts of the data. This includes not just what participants say but also the context of their utterances, their emotional tone, and the nonverbal cues that contribute to meaning.

Thick description enhances authenticity by painting a vivid picture of the participants’ lived experiences, allowing readers to grasp the nuances and complexities of their perspectives.

For instance, if studying a phenomenon like “pain,” researchers should acknowledge whether they perceive it as a real, tangible experience or a socially constructed one.

This understanding shapes data collection and analysis, ensuring the findings remain true to the participants’ realities.

Utility of Research Contributions

Utility refers to the usefulness and value of the research findings.

Studies with high utility introduce new insights, expand upon existing knowledge, or offer practical applications for researchers and practitioners.

The utility of a study’s findings is evaluated in relation to its aims and tradition of inquiry. For example, studies with a critical approach should contribute to an awareness of power dynamics and oppression.

A study might have high fidelity by providing compelling descriptions of student study challenges, but if it only offers obvious or commonly known study strategies, it would have low utility.

Ideally, a study would possess both high fidelity and utility, providing a clear understanding of the phenomenon while also offering valuable contributions to the field.

Strategies to enhance trustworthiness and methodological integrity:

• Using rigorous research methods: Selecting and justifying the chosen qualitative method based on its established rigor enhances credibility and demonstrates a commitment to methodological soundness.
• Reflexivity : Critically examining personal biases, values, and experiences helps researchers identify potential influences on their interpretations and ensure that findings are not solely a product of their own perspectives.
• Promoting authentic voice: Researchers should strive to create conditions that allow participants to express themselves openly and honestly.
• Truth Value: Acknowledging the existence of multiple perspectives and ensuring that the findings accurately represent the participants’ views and experiences.
• Member checking : Involving participants in the research process by sharing findings with them to confirm the accuracy of interpretations.
• Triangulation : Utilizing multiple data sources, methods, or researchers to corroborate findings and provide a more comprehensive understanding of the phenomenon.
• Prolonged engagement : Spending sufficient time in the field to develop a deep understanding of the context and build rapport with participants, which can lead to more insightful and trustworthy data.
• Thick description : Providing detailed narratives, representative quotes, and thorough descriptions of the context helps readers understand the phenomenon and assess the credibility and transferability of the findings.
• Ensuring continuous data saturation : Immersing oneself in the data, constantly refining understanding, and remaining open to gathering more data if needed ensure that the data adequately captures the complexity and diversity of the phenomenon under study.

2. Transferability

Transferability in qualitative research is similar to external validity in quantitative research. It refers to the extent to which the findings can be applied or transferred to other contexts, settings, or groups.

While generalizability in the statistical sense is not a primary goal of qualitative research, providing sufficient details about the study context, sample, and methods can enhance the transferability of the findings.

Qualitative research prioritizes transferability over generalizability. Transferability acknowledges the context-specific nature of findings and encourages readers to consider the potential applicability of the research to other settings.

Researchers can promote transferability by providing thick descriptions of the context, the participants, and the research process.

Transferability is an external consideration, inviting readers to evaluate the potential applicability of the findings to other settings.

Promoting Transferability :

• Providing thick description: Offering detailed contextual information about the setting, participants, and findings, allowing readers to assess the potential relevance to other settings.
• Purposive sampling: Selecting participants who represent a range of perspectives and experiences relevant to the research question. This can enhance the applicability of the findings to a broader population.
• Discussing limitations: Openly acknowledging the specificities of the research context and the potential limitations of applying the findings to other settings.

Barriers to Validity in Qualitative Research

Researchers should be aware of potential threats to validity and take steps to mitigate them. Some common pitfalls include:

Researcher Bias and Perspective

Researchers’ own beliefs, values, and assumptions can influence data collection, analysis, and interpretation, potentially distorting the findings.

Acknowledging and managing these perspectives is crucial for ensuring fidelity to the subject matter.

This aligns with the concept of reflexivity in qualitative research, which encourages researchers to critically examine their own positionality and its potential impact on the research process.

Inadequate Sampling and Representation

If the sample of participants is not representative of the population of interest or if the data collected are incomplete or insufficiently detailed, the findings might lack conceptual heterogeneity and fail to capture the full range of perspectives and experiences relevant to the research question.

This emphasizes the importance of purposive sampling in qualitative research, aiming to select participants who can provide rich and diverse insights into the phenomenon under study.

Superficial Data and Lack of Thick Description

When data are presented in a cursory or overly simplistic manner, without sufficient detail and context, the validity of the findings can be questioned.

This reductionism can stem from a lack of thorough data analysis or a tendency to prioritize brevity over depth in reporting the results

Thick description , a cornerstone of qualitative research, involves providing rich, detailed accounts of the data, capturing the nuances of the participants’ experiences and the context in which they occur.

Selective Anecdotalism and Cherry-Picking

Choosing to focus on specific anecdotes or data points that support the researcher’s preconceived notions while ignoring contradictory evidence can severely undermine validity.

This selective reporting distorts the overall picture and presents a biased view of the findings.

Qualitative researchers are expected to analyze and present data comprehensively, acknowledging all relevant themes and perspectives, even those that challenge their initial assumptions.

Perceived Coercion and Power Dynamics

In qualitative research, especially when dealing with sensitive topics or vulnerable populations, power imbalances between the researcher and participants can influence the data obtained.

If participants feel pressured or coerced to provide certain answers, their responses might lack authenticity and fail to reflect their genuine perspectives.

This underscores the importance of establishing trust and rapport with participants, ensuring they feel safe and comfortable to share their experiences openly and honestly.

Attrition in Longitudinal Studies

In qualitative studies that involve multiple data collection points over time, participant attrition can threaten validity.

If participants drop out of the study for reasons related to the research topic, the remaining sample might become biased, and the findings might not accurately reflect the experiences of the original group.

Addressing attrition requires careful planning and implementation of strategies to maintain participant engagement and minimize drop-out rates.

Reliability in Qualitative Research

Traditional quantitative definition, focused on the replicability of results, is not directly applicable to qualitative inquiry.

This is because qualitative research often explores complex, context-specific phenomena that are influenced by multiple subjective interpretations.

In qualitative research, reliability refers to the consistency and stability of the research proces s and findings.

Reliability in qualitative research concerns consistency and dependability in data collection, analysis, and interpretation.

Dependability

Instead of striving for replicability, qualitative research prioritizes dependability , which focuses on the consistency and trustworthiness of the research process itself.

This involves demonstrating that the methods used were appropriate, that the data were collected and analyzed systematically, and that the interpretations are well-supported by the evidence.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

Strategies for promoting reliability in qualitative research:

• Standardized procedures: Establishing clear and consistent protocols for data collection, analysis, and interpretation can help ensure that the research process is systematic and replicable.
• Rigorous training for researchers in qualitative methodologies, data analysis techniques, and reflexive practices to manage their own perspectives and biases.
• Audit trails : An audit trail provides evidence of the decisions made by the researcher   regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. This includes maintaining detailed field notes, documenting coding decisions, and preserving raw data for future reference.
• Transparency in reporting: Clearly articulating the research design, data collection methods, analytical procedures, and the researcher’s own reflexivity allows readers to assess the trustworthiness of the findings and understand the logic behind the interpretations.
• Interrater reliability (optional): While not universally employed in qualitative research, involving multiple coders to analyze the data can provide insights into the consistency of interpretations. However, it’s important to note that complete agreement might not be the goal, as differing perspectives can enrich the analysis. Discrepancies can be discussed and resolved, leading to a more comprehensive understanding of the data.

Barriers to Reliability in Qualitative Research

Subjectivity in data collection and analysis.

One of the main barriers to reliability stems from the subjective nature of qualitative data collection and analysis.

Unlike quantitative research with its standardized procedures, qualitative research often involves a deep engagement with participants and data, relying on the researcher’s interpretation and judgment.

This introduces potential for inconsistency in data coding and interpretation, especially when multiple researchers are involved.

Researchers’ personal backgrounds, experiences, and theoretical orientations can influence their interpretation of the data.

What one researcher considers significant or meaningful may differ from another researcher’s perspective.

This subjectivity can lead to variations in how data is collected, coded, and analyzed, especially when multiple researchers are involved in a study.

Lack of Detailed Documentation

Qualitative studies often involve complex and iterative processes of data collection, analysis, and interpretation. Without a clear and comprehensive record of these processes, it becomes challenging for others to assess the dependability and consistency of the findings.

Insufficient documentation of data collection methods, coding schemes, analytical decisions, and researcher reflexivity can hinder the ability to establish reliability.

A detailed audit trail , which provides a transparent account of the research process, is crucial for demonstrating the trustworthiness and credibility of qualitative findings.

Lack of detailed documentation of the research process, including data collection methods, coding schemes, and analytical decisions, can hinder reliability.

Without such documentation, it becomes difficult for other researchers to replicate the study or assess the reliability of the conclusions drawn.

Reductionism in Data Representation

Reductionism, or oversimplifying complex data by relying on short quotes and superficial descriptions, can also compromise reliability.

Such reductive practices can distort the richness and nuance of the data, leading to potentially misleading interpretations.

Qualitative research often yields rich, nuanced, and context-specific data that cannot be easily reduced to simple categories or short quotes.

However, in an effort to present findings concisely, researchers may resort to reductive practices that distort the true nature of the data.

Relying on short quotes or superficial descriptions without providing sufficient context can lead to misinterpretations and oversimplification.

Such reductive practices fail to capture the complexity and depth of the participants’ experiences and perspectives.

As a result, the reliability of the findings may be questioned, as they may not accurately represent the full range of data collected.