how to ensure reliability and validity in qualitative research

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Validity, reliability, and generalizability in qualitative research

Lawrence leung.

1 Department of Family Medicine, Queen's University, Kingston, Ontario, Canada

2 Centre of Studies in Primary Care, Queen's University, Kingston, Ontario, Canada

In general practice, qualitative research contributes as significantly as quantitative research, in particular regarding psycho-social aspects of patient-care, health services provision, policy setting, and health administrations. In contrast to quantitative research, qualitative research as a whole has been constantly critiqued, if not disparaged, by the lack of consensus for assessing its quality and robustness. This article illustrates with five published studies how qualitative research can impact and reshape the discipline of primary care, spiraling out from clinic-based health screening to community-based disease monitoring, evaluation of out-of-hours triage services to provincial psychiatric care pathways model and finally, national legislation of core measures for children's healthcare insurance. Fundamental concepts of validity, reliability, and generalizability as applicable to qualitative research are then addressed with an update on the current views and controversies.

Nature of Qualitative Research versus Quantitative Research

The essence of qualitative research is to make sense of and recognize patterns among words in order to build up a meaningful picture without compromising its richness and dimensionality. Like quantitative research, the qualitative research aims to seek answers for questions of “how, where, when who and why” with a perspective to build a theory or refute an existing theory. Unlike quantitative research which deals primarily with numerical data and their statistical interpretations under a reductionist, logical and strictly objective paradigm, qualitative research handles nonnumerical information and their phenomenological interpretation, which inextricably tie in with human senses and subjectivity. While human emotions and perspectives from both subjects and researchers are considered undesirable biases confounding results in quantitative research, the same elements are considered essential and inevitable, if not treasurable, in qualitative research as they invariable add extra dimensions and colors to enrich the corpus of findings. However, the issue of subjectivity and contextual ramifications has fueled incessant controversies regarding yardsticks for quality and trustworthiness of qualitative research results for healthcare.

Impact of Qualitative Research upon Primary Care

In many ways, qualitative research contributes significantly, if not more so than quantitative research, to the field of primary care at various levels. Five qualitative studies are chosen to illustrate how various methodologies of qualitative research helped in advancing primary healthcare, from novel monitoring of chronic obstructive pulmonary disease (COPD) via mobile-health technology,[ 1 ] informed decision for colorectal cancer screening,[ 2 ] triaging out-of-hours GP services,[ 3 ] evaluating care pathways for community psychiatry[ 4 ] and finally prioritization of healthcare initiatives for legislation purposes at national levels.[ 5 ] With the recent advances of information technology and mobile connecting device, self-monitoring and management of chronic diseases via tele-health technology may seem beneficial to both the patient and healthcare provider. Recruiting COPD patients who were given tele-health devices that monitored lung functions, Williams et al. [ 1 ] conducted phone interviews and analyzed their transcripts via a grounded theory approach, identified themes which enabled them to conclude that such mobile-health setup and application helped to engage patients with better adherence to treatment and overall improvement in mood. Such positive findings were in contrast to previous studies, which opined that elderly patients were often challenged by operating computer tablets,[ 6 ] or, conversing with the tele-health software.[ 7 ] To explore the content of recommendations for colorectal cancer screening given out by family physicians, Wackerbarth, et al. [ 2 ] conducted semi-structure interviews with subsequent content analysis and found that most physicians delivered information to enrich patient knowledge with little regard to patients’ true understanding, ideas, and preferences in the matter. These findings suggested room for improvement for family physicians to better engage their patients in recommending preventative care. Faced with various models of out-of-hours triage services for GP consultations, Egbunike et al. [ 3 ] conducted thematic analysis on semi-structured telephone interviews with patients and doctors in various urban, rural and mixed settings. They found that the efficiency of triage services remained a prime concern from both users and providers, among issues of access to doctors and unfulfilled/mismatched expectations from users, which could arouse dissatisfaction and legal implications. In UK, a care pathways model for community psychiatry had been introduced but its benefits were unclear. Khandaker et al. [ 4 ] hence conducted a qualitative study using semi-structure interviews with medical staff and other stakeholders; adopting a grounded-theory approach, major themes emerged which included improved equality of access, more focused logistics, increased work throughput and better accountability for community psychiatry provided under the care pathway model. Finally, at the US national level, Mangione-Smith et al. [ 5 ] employed a modified Delphi method to gather consensus from a panel of nominators which were recognized experts and stakeholders in their disciplines, and identified a core set of quality measures for children's healthcare under the Medicaid and Children's Health Insurance Program. These core measures were made transparent for public opinion and later passed on for full legislation, hence illustrating the impact of qualitative research upon social welfare and policy improvement.

Overall Criteria for Quality in Qualitative Research

Given the diverse genera and forms of qualitative research, there is no consensus for assessing any piece of qualitative research work. Various approaches have been suggested, the two leading schools of thoughts being the school of Dixon-Woods et al. [ 8 ] which emphasizes on methodology, and that of Lincoln et al. [ 9 ] which stresses the rigor of interpretation of results. By identifying commonalities of qualitative research, Dixon-Woods produced a checklist of questions for assessing clarity and appropriateness of the research question; the description and appropriateness for sampling, data collection and data analysis; levels of support and evidence for claims; coherence between data, interpretation and conclusions, and finally level of contribution of the paper. These criteria foster the 10 questions for the Critical Appraisal Skills Program checklist for qualitative studies.[ 10 ] However, these methodology-weighted criteria may not do justice to qualitative studies that differ in epistemological and philosophical paradigms,[ 11 , 12 ] one classic example will be positivistic versus interpretivistic.[ 13 ] Equally, without a robust methodological layout, rigorous interpretation of results advocated by Lincoln et al. [ 9 ] will not be good either. Meyrick[ 14 ] argued from a different angle and proposed fulfillment of the dual core criteria of “transparency” and “systematicity” for good quality qualitative research. In brief, every step of the research logistics (from theory formation, design of study, sampling, data acquisition and analysis to results and conclusions) has to be validated if it is transparent or systematic enough. In this manner, both the research process and results can be assured of high rigor and robustness.[ 14 ] Finally, Kitto et al. [ 15 ] epitomized six criteria for assessing overall quality of qualitative research: (i) Clarification and justification, (ii) procedural rigor, (iii) sample representativeness, (iv) interpretative rigor, (v) reflexive and evaluative rigor and (vi) transferability/generalizability, which also double as evaluative landmarks for manuscript review to the Medical Journal of Australia. Same for quantitative research, quality for qualitative research can be assessed in terms of validity, reliability, and generalizability.

Validity in qualitative research means “appropriateness” of the tools, processes, and data. Whether the research question is valid for the desired outcome, the choice of methodology is appropriate for answering the research question, the design is valid for the methodology, the sampling and data analysis is appropriate, and finally the results and conclusions are valid for the sample and context. In assessing validity of qualitative research, the challenge can start from the ontology and epistemology of the issue being studied, e.g. the concept of “individual” is seen differently between humanistic and positive psychologists due to differing philosophical perspectives:[ 16 ] Where humanistic psychologists believe “individual” is a product of existential awareness and social interaction, positive psychologists think the “individual” exists side-by-side with formation of any human being. Set off in different pathways, qualitative research regarding the individual's wellbeing will be concluded with varying validity. Choice of methodology must enable detection of findings/phenomena in the appropriate context for it to be valid, with due regard to culturally and contextually variable. For sampling, procedures and methods must be appropriate for the research paradigm and be distinctive between systematic,[ 17 ] purposeful[ 18 ] or theoretical (adaptive) sampling[ 19 , 20 ] where the systematic sampling has no a priori theory, purposeful sampling often has a certain aim or framework and theoretical sampling is molded by the ongoing process of data collection and theory in evolution. For data extraction and analysis, several methods were adopted to enhance validity, including 1 st tier triangulation (of researchers) and 2 nd tier triangulation (of resources and theories),[ 17 , 21 ] well-documented audit trail of materials and processes,[ 22 , 23 , 24 ] multidimensional analysis as concept- or case-orientated[ 25 , 26 ] and respondent verification.[ 21 , 27 ]

Reliability

In quantitative research, reliability refers to exact replicability of the processes and the results. In qualitative research with diverse paradigms, such definition of reliability is challenging and epistemologically counter-intuitive. Hence, the essence of reliability for qualitative research lies with consistency.[ 24 , 28 ] A margin of variability for results is tolerated in qualitative research provided the methodology and epistemological logistics consistently yield data that are ontologically similar but may differ in richness and ambience within similar dimensions. Silverman[ 29 ] proposed five approaches in enhancing the reliability of process and results: Refutational analysis, constant data comparison, comprehensive data use, inclusive of the deviant case and use of tables. As data were extracted from the original sources, researchers must verify their accuracy in terms of form and context with constant comparison,[ 27 ] either alone or with peers (a form of triangulation).[ 30 ] The scope and analysis of data included should be as comprehensive and inclusive with reference to quantitative aspects if possible.[ 30 ] Adopting the Popperian dictum of falsifiability as essence of truth and science, attempted to refute the qualitative data and analytes should be performed to assess reliability.[ 31 ]

Generalizability

Most qualitative research studies, if not all, are meant to study a specific issue or phenomenon in a certain population or ethnic group, of a focused locality in a particular context, hence generalizability of qualitative research findings is usually not an expected attribute. However, with rising trend of knowledge synthesis from qualitative research via meta-synthesis, meta-narrative or meta-ethnography, evaluation of generalizability becomes pertinent. A pragmatic approach to assessing generalizability for qualitative studies is to adopt same criteria for validity: That is, use of systematic sampling, triangulation and constant comparison, proper audit and documentation, and multi-dimensional theory.[ 17 ] However, some researchers espouse the approach of analytical generalization[ 32 ] where one judges the extent to which the findings in one study can be generalized to another under similar theoretical, and the proximal similarity model, where generalizability of one study to another is judged by similarities between the time, place, people and other social contexts.[ 33 ] Thus said, Zimmer[ 34 ] questioned the suitability of meta-synthesis in view of the basic tenets of grounded theory,[ 35 ] phenomenology[ 36 ] and ethnography.[ 37 ] He concluded that any valid meta-synthesis must retain the other two goals of theory development and higher-level abstraction while in search of generalizability, and must be executed as a third level interpretation using Gadamer's concepts of the hermeneutic circle,[ 38 , 39 ] dialogic process[ 38 ] and fusion of horizons.[ 39 ] Finally, Toye et al. [ 40 ] reported the practicality of using “conceptual clarity” and “interpretative rigor” as intuitive criteria for assessing quality in meta-ethnography, which somehow echoed Rolfe's controversial aesthetic theory of research reports.[ 41 ]

Food for Thought

Despite various measures to enhance or ensure quality of qualitative studies, some researchers opined from a purist ontological and epistemological angle that qualitative research is not a unified, but ipso facto diverse field,[ 8 ] hence any attempt to synthesize or appraise different studies under one system is impossible and conceptually wrong. Barbour argued from a philosophical angle that these special measures or “technical fixes” (like purposive sampling, multiple-coding, triangulation, and respondent validation) can never confer the rigor as conceived.[ 11 ] In extremis, Rolfe et al. opined from the field of nursing research, that any set of formal criteria used to judge the quality of qualitative research are futile and without validity, and suggested that any qualitative report should be judged by the form it is written (aesthetic) and not by the contents (epistemic).[ 41 ] Rolfe's novel view is rebutted by Porter,[ 42 ] who argued via logical premises that two of Rolfe's fundamental statements were flawed: (i) “The content of research report is determined by their forms” may not be a fact, and (ii) that research appraisal being “subject to individual judgment based on insight and experience” will mean those without sufficient experience of performing research will be unable to judge adequately – hence an elitist's principle. From a realism standpoint, Porter then proposes multiple and open approaches for validity in qualitative research that incorporate parallel perspectives[ 43 , 44 ] and diversification of meanings.[ 44 ] Any work of qualitative research, when read by the readers, is always a two-way interactive process, such that validity and quality has to be judged by the receiving end too and not by the researcher end alone.

In summary, the three gold criteria of validity, reliability and generalizability apply in principle to assess quality for both quantitative and qualitative research, what differs will be the nature and type of processes that ontologically and epistemologically distinguish between the two.

Source of Support: Nil.

Conflict of Interest: None declared.

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Methodology

Reliability vs. Validity in Research | Difference, Types and Examples

Published on July 3, 2019 by Fiona Middleton . Revised on June 22, 2023.

Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method , technique. or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.opt

It’s important to consider reliability and validity when you are creating your research design , planning your methods, and writing up your results, especially in quantitative research . Failing to do so can lead to several types of research bias and seriously affect your work.

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Understanding reliability vs validity, how are reliability and validity assessed, how to ensure validity and reliability in your research, where to write about reliability and validity in a thesis, other interesting articles.

Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.

What is reliability?

Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.

What is validity?

Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.

High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.

If the thermometer shows different temperatures each time, even though you have carefully controlled conditions to ensure the sample’s temperature stays the same, the thermometer is probably malfunctioning, and therefore its measurements are not valid.

However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.

Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.

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Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.

Types of reliability

Different types of reliability can be estimated through various statistical methods.

Types of validity

The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.

To assess the validity of a cause-and-effect relationship, you also need to consider internal validity (the design of the experiment ) and external validity (the generalizability of the results).

The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.

Ensuring validity

If you use scores or ratings to measure variations in something (such as psychological traits, levels of ability or physical properties), it’s important that your results reflect the real variations as accurately as possible. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data.

• Choose appropriate methods of measurement

Ensure that your method and measurement technique are high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge.

For example, to collect data on a personality trait, you could use a standardized questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or findings of previous studies, and the questions should be carefully and precisely worded.

• Use appropriate sampling methods to select your subjects

To produce valid and generalizable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession).  Ensure that you have enough participants and that they are representative of the population. Failing to do so can lead to sampling bias and selection bias .

Ensuring reliability

Reliability should be considered throughout the data collection process. When you use a tool or technique to collect data, it’s important that the results are precise, stable, and reproducible .

• Apply your methods consistently

Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved.

For example, if you are conducting interviews or observations , clearly define how specific behaviors or responses will be counted, and make sure questions are phrased the same way each time. Failing to do so can lead to errors such as omitted variable bias or information bias .

• Standardize the conditions of your research

When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.

For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions, preferably in a properly randomized setting. Failing to do so can lead to a placebo effect , Hawthorne effect , or other demand characteristics . If participants can guess the aims or objectives of a study, they may attempt to act in more socially desirable ways.

It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper . Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
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Minimizing Bias in Qualitative Research: Strategies for Ensuring Validity and Reliability

Qualitative research, with its emphasis on understanding human experiences and perspectives, plays a vital role in various fields. However, like any research methodology, it is susceptible to bias, which can threaten the validity and reliability of findings. Therefore, researchers must actively strive to minimize bias throughout the research process. This blog post delves into the importance of minimizing bias in qualitative research and explores various strategies to achieve this goal.

Recognizing and Understanding Bias 

The first step towards minimizing bias is acknowledging its existence. Bias can creep into research in various ways, often subconsciously, influencing how researchers design studies, collect data, analyze results, and interpret findings. Recognizing potential sources of bias is crucial for developing strategies to mitigate their impact.

Types of Bias in Qualitative Research

Researchers should be aware of different types of bias that can affect qualitative research. These include:

• Design bias: Occurs when the research design itself favors certain outcomes or perspectives. For instance, if a study is designed in such a way that it only includes participants from a specific demographic or uses a particular method that leans towards a certain result, it can lead to design bias. This can skew the results and make them less representative of the broader population or phenomenon being studied.
• Selection bias: Arises when the sample of participants is not representative of the population being studied. For example, if a study on dietary habits only includes participants from a health club, the results may not represent the dietary habits of the general population, as people who attend a health club may have different dietary habits compared to those who do not.
• Omission bias: Occurs when important data is excluded from the analysis. For example, if a researcher conducting a study on the effects of a new drug only includes positive results and excludes negative ones, this would be an example of omission bias. The results of the study would then be skewed, as they do not take into account all relevant data.
• Inclusive bias: Occurs when irrelevant data is included in the analysis. For instance, if a researcher is studying the impact of diet on heart disease and includes data about participants’ favorite colors, this would be an example of inclusive bias. The favorite color is likely irrelevant to the development of heart disease and its inclusion could confuse the analysis.
• Procedural bias: Occurs due to inconsistencies in data collection or analysis procedures. For example, if a researcher uses different methods to collect data from different participants, or if the criteria used to analyze data changes during the course of the study, this could introduce procedural bias. The results may then not accurately reflect the phenomenon being studied, but rather the inconsistencies in the procedures used.
• Measurement bias: Occurs when the research instruments or methods used to collect data are flawed. For example, if a researcher uses a faulty scale to measure weight in a study on obesity, the data collected would be inaccurate, leading to measurement bias. Similarly, if a survey question is poorly worded or ambiguous, it could lead to inconsistent responses, introducing measurement bias.
• Interviewer bias: Occurs when the interviewer’s own beliefs or expectations influence how they interact with participants and interpret their responses. For example, if an interviewer has strong opinions about a topic, they may unconsciously lead participants to respond in a certain way, or they may interpret responses in a way that aligns with their own beliefs. Similarly, characteristics such as the interviewer’s age, gender, or race may influence how participants respond.
• Response bias: Occurs when participants intentionally or unintentionally misrepresent their experiences or opinions. For example, participants might give socially desirable responses, or they might try to guess what the researcher is looking for and tailor their responses accordingly. They might also misunderstand the question, forget relevant information, or exaggerate their responses.
• Reporting bias: Occurs when researchers selectively report findings that support their hypotheses or preconceived notions. For example, if a researcher only reports positive outcomes of a clinical trial and neglects to mention negative or neutral outcomes, this would be an example of reporting bias. The results of the study would then not accurately reflect the true effects of the treatment being studied.

Strategies for Minimizing Bias

Several strategies can be employed to minimize bias in qualitative research. These include:

• Multiple Coders: Using multiple researchers to code the data can help identify and address individual biases. Consistency in interpretation across coders strengthens the validity of findings.
• Participant Review: Allowing participants to review and provide feedback on the research findings can help ensure that their perspectives are accurately represented.
• Reflexivity, Peer Debriefing, and Triangulation: Engaging in reflexivity, where researchers critically examine their own biases and assumptions, can help mitigate their influence on the research process. Additionally, peer debriefing with colleagues and using triangulation, where data is collected from multiple sources and methods, can further strengthen the objectivity of the research.
• Changing Experimental Design: When unavoidable omission bias is identified, researchers can consider modifying the research design to address it. This may involve expanding the scope of the study or including additional data sources.
• Respecting Participants: Ensuring that participants are treated with respect and are not coerced into participating is essential for protecting them from exploitation and minimizing response bias.

Minimizing bias in qualitative research is an ongoing process that requires researchers to be vigilant and proactive. By employing the strategies discussed above, researchers can enhance the validity and reliability of their findings, ensuring that they accurately reflect the lived experiences and perspectives of the participants they study. Remember, while complete elimination of bias may be challenging, these strategies can help ensure that research findings remain as unbiased and faithful representations of participants’ perspectives as possible.

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Validity and reliability in qualitative research.

What is Validity and Reliability in Qualitative research?

In Quantitative research, reliability refers to consistency of certain measurements, and validity – to whether these measurements “measure what they are supposed to measure”. Things are slightly different, however, in Qualitative research.

Reliability in qualitative studies is mostly a matter of “being thorough, careful and honest in carrying out the research” (Robson, 2002: 176). In qualitative interviews, this issue relates to a number of practical aspects of the process of interviewing, including the wording of interview questions, establishing rapport with the interviewees and considering ‘power relationship’ between the interviewer and the participant (e.g. Breakwell, 2000; Cohen et al., 2007; Silverman, 1993).

What seems more relevant when discussing qualitative studies is their validity , which very often is being addressed with regard to three common threats to validity in qualitative studies, namely researcher bias , reactivity and respondent bias (Lincoln and Guba, 1985).

Researcher bias refers to any kind of negative influence of the researcher’s knowledge, or assumptions, of the study, including the influence of his or her assumptions of the design, analysis or, even, sampling strategy. Reactivity , in turn, refers to a possible influence of the researcher himself/herself on the studied situation and people. Respondent bias refers to a situation where respondents do not provide honest responses for any reason, which may include them perceiving a given topic as a threat, or them being willing to ‘please’ the researcher with responses they believe are desirable.

Robson (2002) suggested a number of strategies aimed at addressing these threats to validity, being prolonged involvement , triangulation , peer debriefing , member checking ,  negative case analysis  and keeping an audit trail .

So, what exactly are these strategies and how can you apply them in your research?

Prolonged involvement refers to the length of time of the researcher’s involvement in the study, including involvement with the environment and the studied participants. It may be granted, for example, by the duration of the study, or by the researcher belonging to the studied community (e.g. a student investigating other students’ experiences). Being a member of this community, or even being a friend to your participants (see my blog post on the ethics of researching friends ), may be a great advantage and a factor that both increases the level of trust between you, the researcher, and the participants and the possible threats of reactivity and respondent bias. It may, however, pose a threat in the form of researcher bias that stems from your, and the participants’, possible assumptions of similarity and presuppositions about some shared experiences (thus, for example, they will not say something in the interview because they will assume that both of you know it anyway – this way, you may miss some valuable data for your study).

Triangulation may refer to triangulation of data through utilising different instruments of data collection, methodological triangulation through employing mixed methods approach and theory triangulation through comparing different theories and perspectives with your own developing “theory” or through drawing from a number of different fields of study.

Peer debriefing and support is really an element of your student experience at the university throughout the process of the study. Various opportunities to present and discuss your research at its different stages, either at internally organised events at your university (e.g. student presentations, workshops, etc.) or at external conferences (which I strongly suggest that you start attending) will provide you with valuable feedback, criticism and suggestions for improvement. These events are invaluable in helping you to asses the study from a more objective, and critical, perspective and to recognise and address its limitations. This input, thus, from other people helps to reduce the researcher bias.

Member checking , or testing the emerging findings with the research participants, in order to increase the validity of the findings, may take various forms in your study. It may involve, for example, regular contact with the participants throughout the period of the data collection and analysis and verifying certain interpretations and themes resulting from the analysis of the data (Curtin and Fossey, 2007). As a way of controlling the influence of your knowledge and assumptions on the emerging interpretations, if you are not clear about something a participant had said, or written, you may send him/her a request to verify either what he/she meant or the interpretation you made based on that. Secondly, it is common to have a follow-up, “validation interview” that is, in itself, a tool for validating your findings and verifying whether they could be applied to individual participants (Buchbinder, 2011), in order to determine outlying, or negative, cases and to re-evaluate your understanding of a given concept (see further below). Finally, member checking, in its most commonly adopted form, may be carried out by sending the interview transcripts to the participants and asking them to read them and provide any necessary comments or corrections (Carlson, 2010).

Negative case analysis is a process of analysing ‘cases’, or sets of data collected from a single participant, that do not match the patterns emerging from the rest of the data. Whenever an emerging explanation of a given phenomenon you are investigating does nto seem applicable to one, or a small number, of the participants, you should try to carry out a new line of analysis aimed at understanding the source of this discrepancy. Although you may be tempted to ignore these “cases” in fear of having to do extra work, it should become your habit to explore them in detail, as the strategy of negative case analysis, especially when combined with member checking, is a valuable way of reducing researcher bias.

Finally, the notion of keeping an audit trail refers to monitoring and keeping a record of all the research-related activities and data, including the raw interview and journal data, the audio-recordings, the researcher’s diary (see this post about recommended software for researcher’s diary ) and the coding book.

If you adopt the above strategies skilfully, you are likely to minimize threats to validity of your study. Don’t forget to look at the resources in the reference list, if you would like to read more on this topic!

Breakwell, G. M. (2000). Interviewing. In Breakwell, G.M., Hammond, S. & Fife-Shaw, C. (eds.) Research Methods in Psychology. 2nd Ed. London: Sage. Buchbinder, E. (2011). Beyond Checking: Experiences of the Validation Interview. Qualitative Social Work, 10 (1), 106-122. Carlson, J.A. (2010). Avoiding Traps in Member Checking. The Qualitative Report, 15 (5), 1102-1113. Cohen, L., Manion, L., & Morrison, K. (2007). Research Methods in Education. 6th Ed. London: Routledge. Curtin, M., & Fossey, E. (2007). Appraising the trustworthiness of qualitative studies: Guidelines for occupational therapists. Australian Occupational Therapy Journal, 54, 88-94. Lincoln, Y. S. & Guba, E. G. (1985). Naturalistic Inquiry. Newbury Park, CA: SAGE. Robson, C. (2002). Real world research: a resource for social scientists and practitioner-researchers. Oxford, UK: Blackwell Publishers.

Silverman, D. (1993) Interpreting Qualitative Data. London: Sage.

There is an argument for using your identity and biases to enrich the research (see my recent blog… researcheridentity.wordpress.com) providing that the researcher seeks to fully comprehend their place in the research and is fully open, honest and clear about that in the write up. I have come to see reliability and validity more as a defence of is the research rigorous, thorough and careful therefore is it morally, ethically and accurately defensible?

Hi Nathan, thank you for your comment. I agree that being explicit about your own status and everything that you bring into the study is important – it’s a very similar issue (although seemingly it’s a different topic) to what I discussed in the blog post about grounded theory where I talked about being explicit about the influence of our previous knowledge on the data. I have also experienced this dilemma of “what to do with” my status as simultaneously a “researcher” an “insider” a “friend” and a “fellow Polish migrant” when conducting my PhD study of Polish migrants’ English Language Identity, and came to similar conclusions as the ones you reach in your article – to acknowledge these “multiple identities” and make the best of them.

I have read your blog article and really liked it – would you mind if I shared it on my Facebook page, and linked to it from my blog section on this page?

Please do share my blog by all means; I’d be delighted. Are you on twitter? I’m @Nathan_AHT_EDD I strongly believe that we cannot escape our past, including our multiple/present habitus and identities when it comes to qualitative educational research. It is therefore, arguably, logical to ethically and sensibly embrace it/them to enrich the data. Identities cannot be taken on and off like a coat, they are, “lived as deeply committed personal projects” (Clegg, 2008: p.336) and so if we embrace them we bring a unique insight into the process and have a genuine investment to make the research meaningful and worthy of notice.

Hi Nathan, I don’t have twitter… I know – somehow I still haven’t had time to get to grips with it. I do have Facebook, feel free to find me there. I also started to follow your blog so that I am notified about your content. I agree with what you said here and in your posts, and I like the topic of your blog. This is definitely something that we should pay more attention to when doing research. It would be interesting to talk some time and exchange opinions, as our research interests seem very closely related. Have a good day !

How to establish the validity and reliability of qualitative research?

The validity and reliability of qualitative research represent the key aspects of the quality of research. When handled meticulously, the reliability and validity parameters help differentiate between good and bad research. They also assure readers that the findings of the study are credible and trustworthy. This aspect becomes particularly vital in case studies involving primary data analysis. Here the researcher’s subjectivity can highly influence the interpretation of the data. This article clarifies how to establish the validity and reliability of qualitative research using various tools and techniques.

Establishing the validity of qualitative data

Qualitative data is as important as quantitative data , as it also helps in establishing key research points. However, since it cannot be quantified, the question of its correctness is critical. Validity relates to the appropriateness of any research value, tools and techniques, and processes, including data collection and validation (Mohamad et al., 2015). Validity also establishes the soundness of the methodology, sampling process, data analysis process, and conclusion of the study (Golafshani, 2003).

The main aspect that needs to be ensured is that the research philosophies fall in line with the research. To maintain the validity of the research, there is a need to understand the underlying needs of the research, the overarching process guidelines and the societal rules of ethical research. While establishing validity, there needs to be a consensus among the individual and the society on how to establish the correctness and accuracy of the research. If this aspect is kept in mind, the tools and techniques used are bound to be accepted by wider audiences. The main point to remember, thus, is to choose the tools wisely, which will establish how to correct the data of the research.

One of the major techniques that can be used for establishing the validity of qualitative data includes choosing a skilled moderator. Employing a moderator will help overcome personal bias. Thus, the researcher or the organisation can employ moderators to ensure that the data is genuine and is not influenced by “what the researcher wants to see or hear”.

Another way to promote the validity of research is by employing the strategy of triangulation. This basically involves that the research will be conducted from different or multiple perspectives. For example, this can take the form of using several moderators, in different locations or it could be multiple individuals who are analysing the same data. Basically, any technique through which the researcher can analyse the data from different angles.

Furthermore, the validity of qualitative research can also be established using a technique known as respondent validation. This basically involves testing the initial results with the participants in order to see if the results still ring true.

Establishing the reliability of qualitative data

Quantitative research includes reliability measures where the researcher must prove that the process and the results have replicable outcomes. On the other hand, reliability in qualitative research includes very diverse paradigms, where the aspect itself is epistemologically counter-intuitive along with having a very difficult definition (Russell, 2014). Thus, what needs to be done to maintain and establish reliability in qualitative research is to be consistent.

Reliability tests for qualitative research can be established by techniques like:

• refutational analysis,
• use of comprehensive data,
• constant testing and comparison of data,
• use of tables to record data,
• as well as the use of inclusive deviant cases.

These techniques can help support the data sourcing, data validation and data presentation process of the research, as well as support the claim of reliability in terms of form and context.

Triangulation is another aspect which becomes very important in establishing reliability in the research. Also, as an additional note, it is very important for qualitative research to include a reference to a quantitative aspect. The use of a simple quantitative aspect in otherwise completely qualitative research creates a very positive attitude towards the overall concept of the research and helps to establish reliability in a much easier form. Also, the inclusion of at least two reliability tests, as per the type of research outcomes of a research, is a dependable way of establishing that the research process and results are reliable.

Dos of validity and reliability of qualitative data

• While establishing validity and reliability, it is very important to decide the tools and techniques to be used in the research, before conducting the actual research. This helps in establishing the parameters for obtaining reliable and valid results from the beginning and does not impair the results of the research at the end of the process.
• To conduct efficient reliability and validity measures, an effective assessment of the literature must be done to understand which processes will work. Irrelevant approaches that compromise the reliability and validity of research should not be pursued.
• Reliability and validity processes should be conducted in tandem with the research processes being carried out to confirm the research objective, which provides another additional layer of authenticity to the research work.

Don’ts’ of validity and reliability of qualitative data

• Reliability and validity should not be taken as an extra element of the research. If they do not add value to research, they result in insecurity regarding the accuracy of the results.
• To be able to establish reliability and validity, researchers should not include excessive measures to support their research claims. This may add to unauthenticated results, as adding too many measures will overcomplicate the research.

An example statement of validity

The validity of this research was established using two measures, the data blinding and the inclusion of different sampling groups in the plan. The research included an assessment of the knowledge of traditional cuisine among the present population of a city. Where the sample was divided into two groups- to reduce biases. The sample included young adults, who have been mostly raised in an urban environment, along with middle-aged and elderly population who have had a partial upbringing in the rural area of India. The inclusion of greater diversity and a large number of sample respondents led the research to reduce its biasness towards only one type of outcome, creating a base for valid results. The other technique used was to restrict the amount of information shared with the respondents to make sure that the research was not biased with preconceived notions of the respondents. These steps helped to establish the validity of the results gained, proving the accurateness of the qualitative research. Further, the validity of the questionnaire was established using a panel of experts that reviewed the questionnaire. And hence the statements that did not go well with the subject of the study were removed.

An example statement of reliability

In terms of establishing reliability, the researcher conducted two processes. The first included recording the data in a table to provide an overall assessment of the data collection process and the updated assessment of the results, as they come. The use of the table for recording data provided the researcher with a chance to quickly interpret the results as per the record of every individual respondent and realize the progress of the research.

The table also helped in the concise construction of the conclusion of the research parameters. The reliability was also assessed through data triangulation. Among the various model of data triangulation such as methodological triangulation, data triangulation, investigator triangulation and theoretical triangulation. The study adopted theoretical triangulation, wherein other research works in the same arena were analysed and presented as a literature review to support the results claims of the data collection and analysis process. Moreover, the reliability measures relating to the triangulation of data provided an extensive understanding of the research objectives, which provided an additional layer of reliable stamping to the research.

• Golafshani, N. (2003) ‘Understanding Reliability and Validity in Qualitative Research’, The Qualitative Report , 8(4), pp. 597–607.
• Mohamad, M. M. et al. (2015) ‘Measuring the Validity and Reliability of Research Instruments’, Procedia – Social and Behavioral Sciences . Elsevier, 204, pp. 164–171. doi: 10.1016/j.sbspro.2015.08.129.
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Reliability vs. Validity in Research: The Essence of Credible Research

Table of contents

• 1 Understanding Reliability in Research
• 2 Understanding Validity in Research
• 3 Key Differences Between Reliability and Validity
• 4 The Role of Reliability and Validity in Research Design
• 5.1 Ensuring Reliability
• 5.2 Ensuring Validity
• 5.3 Considerations for Specific Research Methods
• 6 Ensuring Excellence in Research Through Meticulous Methodology

The concepts of reliability and validity play pivotal roles in ensuring the integrity and credibility of research findings. These foundational principles are crucial for researchers aiming to produce work that contributes to their field and withstands scrutiny. Understanding the interplay between reliability vs validity in research is essential for any rigorous investigation.

The main points of our article include:

• A detailed exploration of the concept of reliability, including its types and how it is assessed.
• An in-depth look at validity, discussing its various forms and the methods used to evaluate it.
• The relationship between reliability and validity, and why both are essential for the credibility of research.
• Practical examples illustrating the application of reliability and validity in different research contexts.
• Strategies for enhancing both reliability and validity in research studies.

This understanding sets the stage for a more in-depth look at these important parts of the methodology. By explaining these ideas in more detail, we can have a deeper discussion about how to use and evaluate them successfully in different research settings. For better understanding and faster coming with your research, students can look at sites like PapersOwl to get more information and help with defining these two concepts.

Understanding Reliability in Research

So, at first, let’s start with the reliability definition.

It measures how stable and consistent the results of a research tool are across a number of tests and conditions. It tells us how reliable the data we collected is, which, in turn, is important for making sure that the study results are valid.

There are several types of reliability crucial for assessing the quality of research instruments:

• Test-retest reliability evaluates the consistency of results when the same test is administered to the same participants under similar conditions at two different points in time.
• Inter-rater reliability measures the extent to which different observers or raters agree in their assessments, ensuring that the data collection process is unbiased and consistent across individuals.
• Parallel-forms reliability involves comparing the results of two different but equivalent versions of a test to the same group of individuals, assessing the consistency of the scores.
• Internal consistency reliability assesses the homogeneity of items within a test, ensuring that all parts contribute equally to what is being measured. This is closely tied to the concept of criterion validity, which evaluates how well one measure predicts an outcome based on other measures.

Methods for measuring and improving reliability include statistical techniques such as Cronbach’s alpha for internal consistency reliability, as well as ensuring standardized testing conditions and thorough training for raters to enhance inter-rater reliability.

Examples of reliability in research can be seen in educational assessments (test-retest reliability), psychological evaluations (internal consistency reliability), and health studies (inter-rater reliability).

Each context underscores the importance of reliable measurement as a precursor to assessing content validity (the extent to which a test measures all aspects of the desired content) and construct validity (the degree to which a test accurately measures the theoretical construct it is intended to measure). Both content validity and construct validity are essential components of overall validity, which refers to the accuracy of the research findings.

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Understanding Validity in Research

In research, validity is a measure of accuracy that indicates how well a method or test measures what it is designed to assess. High validity is indicative of results that closely correspond to actual characteristics, behaviors, or phenomena in the physical or social world, making it a critical aspect of any credible research endeavor.

Types of validity include:

• Content validity, which ensures that a test comprehensively covers all aspects of the subject it aims to measure.
• Criterion-related validity, which is divided into predictive validity (how well a test predicts future outcomes) and concurrent validity (how well a test correlates with established measures at the same time).
• Construct validity, further broken down into convergent validity (how closely a new test aligns with existing tests of the same constructs) and discriminant validity (how well the test distinguishes between different constructs).
• Face validity, a more subjective measure of how relevant a test appears to be at face value, without delving into its technical merits.

Validity can be measured and ensured in a number of ways, such as through expert evaluations or by doing statistical calculations. They focus on how well the test or method matches up with theoretical expectations and set standards. Validity requires careful data collection, careful test design, and regular checks to see if the test is still relevant and accurately showing the intended constructs.

Examples of validity in research are abundant and varied. In educational testing, content validity is assessed to ensure that exams or assessments fully represent the curriculum they aim to measure. In psychology, convergent validity is demonstrated when different tests of the same psychological construct yield similar results, while predictive validity might be observed in employment settings where a cognitive test predicts job performance. Each of these examples showcases how validity is assessed and achieved, highlighting its role in producing meaningful and accurate research outcomes.

Key Differences Between Reliability and Validity

The key differences between reliability and validity lie in their focus and implication in research. Reliability concerns the consistency of a measurement tool, ensuring that the same measurement is obtained across different instances of its application. For instance, interrater reliability ensures consistency in observations made by different scholars. Validity, on the other hand, assesses whether the research tool accurately measures what it is intended to, aligning with established theories and meeting the research objectives. While reliability is about the repeatability of the same measurement, validity dives deeper into the accuracy and appropriateness of what is being measured, ensuring it reflects the intended constructs or realities.

The Role of Reliability and Validity in Research Design

The incorporation of reliability and validity assessments in the early stages of  research design is paramount for ensuring the credibility and applicability of research outcomes. By prioritizing these evaluations from the outset, research writers can design studies that accurately reflect and measure the phenomena of interest, leading to more trustworthy and meaningful findings.

Strategies for integrating reliability and validity checks throughout the research process include the use of established statistical methods and the continuous evaluation of research measures. For instance, employing factor analysis can help in identifying the underlying structure of data, thus aiding in the assessment of construct validity. Similarly, calculating Cronbach’s alpha can ensure the internal consistency of items within a survey, contributing to the overall reliability of the research measures.

Case studies across various disciplines underscore the critical role of reliability and validity in shaping research outcomes and influencing subsequent decisions. For example, in clinical psychology research, the use of validated instruments to assess patient symptoms ensures that the measures accurately capture the constructs of interest, such as depression or anxiety levels, which in turn supports the internal validity of the study. In the field of education, ensuring the interrater reliability of grading rubrics can lead to fairer and more consistent assessments of student performance.

Moreover, the application of rigorous statistical methods not only enhances the reliability and validity of the research but also strengthens the study’s foundation, making the findings more compelling and actionable. By systematically integrating these checks, researchers can avoid common pitfalls such as measurement errors or biases, thereby ensuring that their studies contribute valuable insights to the body of knowledge.

Challenges and Considerations in Ensuring Reliability and Validity

Ensuring reliability and validity in research is crucial for the credibility and applicability of research results. These principles guide how to design studies, collect data, and interpret findings to ensure that their work accurately reflects the underlying constructs they aim to explore.

Ensuring Reliability

To ensure reliability, researchers must focus on creating consistent, repeatable conditions and employing precise measurement tools. The test-retest correlation is a fundamental method where writers administer the same test to the same subjects under similar conditions at two different times. A high correlation between the two sets of results indicates strong reliability.

For example, in a study measuring the stress levels of first responders, using the same stress assessment tool at different intervals can validate the tool’s reliability through consistent scores.

Another strategy is ensuring reliable measurement through inter-rater reliability, where multiple observers assess the same concept to verify consistency in observations. In environmental science, when studying the impact of pollution on local ecosystems, different researchers might assess the same water samples for contaminants. The consistency of their measurements confirms the reliability of the methods used.

Ensuring Validity

Ensuring validity involves verifying that the research accurately measures the intended concept. This can be achieved through careful  questioning in research formulation, selecting valid measurement instruments, and employing appropriate statistical analyses.

For instance, when studying the effectiveness of a new educational curriculum, researchers might use standardized test scores to measure student learning outcomes. This approach ensures that the tests are a valid measurement of the educational objectives the curriculum aims to achieve.

Construct validity can be enhanced through factor analysis, which helps in identifying whether the collected data truly represent the underlying construct of interest. In health research, exploring the validity of a new diagnostic tool for a specific disease involves comparing its results with those from established diagnostic methods, ensuring that the new tool accurately identifies the disease it claims to measure.

Considerations for Specific Research Methods

Different research methods, such as qualitative vs. quantitative research, require distinct approaches to ensure validity and reliability. In qualitative research, ensuring external validity involves a detailed and transparent description of the research setting and context, allowing others to assess the applicability of the findings to similar contexts.

For instance, in-depth interviews exploring patients’ experiences with chronic pain provide rich, contextual insights that might not be generalizable without a clear articulation of the setting and participant characteristics.

In quantitative research, ensuring the validity and reliability of collecting data often involves statistical validation methods and reliability tests, such as Cronbach’s alpha for internal consistency.

Ensuring Excellence in Research Through Meticulous Methodology

In summary, the fundamental takeaways from this article highlight the paramount importance of ensuring high reliability and validity in conducting research. These principles are not merely academic considerations but are crucial for the integrity and applicability of research findings. The accuracy of research instruments, the consistency of test scores, and the thoughtful design of the methods section of a research paper are all critical to achieving these goals. For researchers aiming to enhance the credibility of their work, focusing on these aspects from the outset is key. Additionally, seeking help with research can provide valuable insights and support in navigating the complexities of research design, ensuring that studies not only adhere to the highest standards of reliability and validity but also contribute meaningful knowledge to their respective fields.

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