what is data verification in qualitative research

Home » Data Verification – Process, Types and Examples

Data Verification – Process, Types and Examples

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Data Verification

Definition:

Data verification is the process of checking and confirming that the data entered or stored in a system or database is accurate, complete, and consistent with the source data.

The goal of data verification is to ensure that the data being used is reliable and error-free. Data verification is often used in data entry and database management to prevent errors that can lead to incorrect results or decisions.

Data Verification Process

The data verification process involves several steps to ensure that the data is accurate, complete, and consistent. The following are the typical steps involved in data verification:

The first step in data verification is data entry, where data is entered into a system or database from a source document, such as a paper form, an electronic file, or a web form.

The next step is to compare the entered data with the source document or the original data source. This step helps to identify any errors, omissions, or inconsistencies that may have occurred during data entry.

Consistency Check

In this step, the data is checked for consistency with established rules and standards. For example, if the data pertains to customer addresses, it may be checked for consistency with the established postal codes, city names, and state or country codes.

In this step, the data is validated against established standards and criteria. For example, if the data pertains to customer information, it may be validated against established standards for accuracy, completeness, and timeliness.

In this step, the data is reviewed for quality, completeness, and accuracy. The review process may involve a review of the data quality metrics such as completeness, accuracy, timeliness, and relevance to ensure that the data meets the required standards.

The final step in data verification is the correction of any errors or inconsistencies that have been identified. This step involves making the necessary corrections to the data to ensure that it is accurate, complete, and consistent with the source data.

Types of Data Verification

Several types of data verification can be used depending on the type of data and the purpose of verification. Here are some of the common types of data verification:

Manual Verification

Manual verification involves human intervention in the data verification process, where a person reviews the data and compares it with the source data to ensure that it is accurate and complete. This method is time-consuming and prone to errors, but it can be effective for small datasets or when the data is complex and requires human judgment.

Automated Verification

Automated verification involves the use of software or algorithms to verify the data automatically. This method is fast, reliable, and can handle large volumes of data. Automated verification can be effective for data with well-defined rules and standards, such as credit card numbers, email addresses, or zip codes.

Double-Entry Verification

Double-entry verification involves entering the data twice and comparing the two entries to ensure that they are identical. This method is useful for minimizing errors during data entry and is commonly used in accounting and financial data entry.

Sampling Verification

Sampling verification involves selecting a subset of the data and verifying it to represent the entire dataset. This method is useful when verifying large datasets, where it is impractical to verify every record.

Cross-Field Verification

Cross-field verification involves verifying the data in one field against the data in another related field. For example, verifying the city and state fields against the postal code field in an address record. This method can help to detect inconsistencies in the data.

Peer Review Verification

Peer review verification involves having another person review the data to ensure that it is accurate and complete. This method is useful for ensuring that the data meets established standards and is suitable for its intended purpose.

Applications of Data Verification

Here are some common applications of data verification:

Research : Data verification is necessary in research to ensure that research data, such as survey responses, interview transcripts, and experimental data, are accurate, complete, and reliable. This helps to ensure the validity and reliability of research findings.
Banking and Finance: Data verification is essential in banking and finance to ensure that customer information, account details, and transaction records are accurate and consistent with the source data. This helps to prevent fraud, errors, and incorrect financial reporting.
Healthcare: Data verification is critical in healthcare to ensure that patient records, treatment plans, and medical histories are accurate, complete, and up-to-date. This helps to ensure the quality of care, patient safety, and compliance with regulatory requirements.
Marketing and Sales: Data verification is necessary in marketing and sales to ensure that customer data, such as email addresses, phone numbers, and mailing addresses, is accurate and up-to-date. This helps to prevent wasted resources on ineffective marketing campaigns and improves the accuracy of customer segmentation and targeting.
Education : Data verification is essential in education to ensure that student records, such as enrollment data, grades, and transcripts, are accurate, complete, and up-to-date. This helps to ensure the integrity of academic records and the accuracy of student reporting.
Government : Data verification is critical in government to ensure that public records, such as census data, voting records, and tax records, are accurate, complete, and consistent with the source data. This helps to ensure the accuracy of government reporting and decision-making.

Examples of Data Verification

Here are some examples of data verification:

Email Address Verification: When a user signs up for a website or service, the email address they provide is verified to ensure that it is valid and belongs to the user. This is done by sending a verification email to the user’s email address with a link to confirm their account.
Credit Card Verification: When a user enters their credit card information to make a purchase, the credit card number is verified to ensure that it is valid and belongs to the user. This is done by comparing the entered credit card number against the card issuer’s database.
Mailing Address Verification: When a company sends a mailing or shipment to a customer, the customer’s mailing address is verified to ensure that it is correct and complete. This is done by comparing the entered mailing address against a database of valid mailing addresses.
Academic Record Verification: When an employer or educational institution needs to verify a candidate’s academic record, the record is verified to ensure that it is accurate and complete. This is done by contacting the educational institution and verifying the candidate’s enrollment, grades, and transcripts.
Voter Registration Verification : When a citizen registers to vote, their registration information is verified to ensure that they are eligible to vote and that their information is accurate and complete. This is done by comparing the registration information against government databases, such as driver’s license records.
Financial Transaction Verification : When a financial transaction is made, the transaction details are verified to ensure that they are accurate and complete. This is done by comparing the transaction details against the financial institution’s database and verifying that the transaction is authorized.

When to Use Data Verification

Data verification should be used whenever data is being collected, entered, processed, analyzed, or reported. Here are some common situations in which data verification should be used:

Data Collection : Data verification should be used when collecting data to ensure that the data is accurate and complete. This can be done by double-checking the data as it is being collected and verifying that it matches the intended data.
Data Entry : Data verification should be used when entering data into a database or system to ensure that the data is accurately entered. This can be done by double-checking the data as it is being entered and verifying that it matches the intended data.
Data Processing: Data verification should be used when processing data to ensure that the data is accurate, complete, and consistent with the source data. This can be done by comparing the processed data against the source data and verifying that there are no errors or inconsistencies.
Data Analysis : Data verification should be used when analyzing data to ensure that the analysis is based on accurate and complete data. This can be done by verifying that the data used for analysis is accurate, complete, and consistent with the source data.
Data Reporting: Data verification should be used when reporting data to ensure that the reported data is accurate, complete, and consistent with the source data. This can be done by verifying the accuracy of the data and ensuring that it is properly formatted and presented.

Data Verification Tools

Data verification tools are software applications or programs designed to validate the accuracy and integrity of data. These tools are used to check the consistency, completeness, and quality of data, and to identify any errors, inconsistencies, or discrepancies that may exist in the data. Some common data verification tools include:

Data Validation : This tool is used to ensure that the data entered into a system or database meets certain criteria or requirements. It checks the data against a set of predefined rules or validation criteria and highlights any errors or issues that need to be addressed.
Data Cleansing: This tool is used to identify and remove any duplicate, incomplete, or inaccurate data from a database or dataset. It helps to ensure that the data is consistent and accurate.
Data Profiling: This tool is used to analyze and understand the structure and quality of the data. It helps to identify any patterns or trends in the data and provides insights into the quality of the data.
Data Matching: This tool is used to match and identify similar or duplicate records in a database or dataset. It helps to ensure that the data is consistent and accurate.
Data Integrity: This tool is used to maintain the integrity and security of the data. It ensures that the data is protected from unauthorized access, modification, or deletion.

Purpose of Data Verification

The purpose of data verification is to ensure that the data being used is accurate, complete, and consistent with the source data. This helps to prevent errors, fraud, and incorrect reporting, which can lead to serious consequences for individuals and organizations.

Data verification serves several purposes, including:

Ensuring Data Accuracy : Data verification helps to ensure that the data being used is accurate by comparing the data against the source data and verifying that there are no errors or inconsistencies.
Ensuring Data Completeness : Data verification helps to ensure that the data being used is complete by verifying that all the necessary data has been collected and entered into the system.
Ensuring Data Consistency: Data verification helps to ensure that the data being used is consistent by verifying that the data is consistent with the source data and other data in the system.
Preventing Errors and Fraud: Data verification helps to prevent errors and fraud by identifying and correcting any errors or inconsistencies in the data.
Improving Data Quality : Data verification helps to improve data quality by identifying any issues with the data and correcting them, which in turn leads to better decision making and more accurate reporting.

Characteristics of Data Verification

Some of the characteristics of data verification include:

Accuracy : Data verification ensures that the data is accurate and free from errors. It checks for inconsistencies, typographical errors, and other mistakes that could affect the quality of the data.
Completeness : Data verification ensures that all required data fields are filled in and that no data is missing. It checks for incomplete records or missing values, which could compromise the quality of the data.
Consistency : Data verification ensures that the data is consistent with other data sources and that there are no conflicting data points. It checks for duplicate records or conflicting information, which could result in inaccurate conclusions.
Timeliness : Data verification ensures that the data is up-to-date and reflects the current state of affairs. It checks for outdated or obsolete data, which could lead to inaccurate conclusions or decisions.
Validity : Data verification ensures that the data is valid and relevant to the task at hand. It checks for data that is not relevant or that does not fit the purpose of the analysis.

Advantages of Data Verification

There are several advantages of data verification, including:

Improved Data Quality : Data verification helps to improve the quality of data by identifying errors, inconsistencies, and other issues that can impact data accuracy. By verifying data, organizations can ensure that their data is reliable, consistent, and up-to-date.
Better Decision Making : Accurate and reliable data is essential for making informed decisions. Data verification ensures that the data being used for analysis and decision-making is accurate and reliable, which can help organizations to make better decisions.
Increased Efficiency : By identifying and resolving errors and inconsistencies in data, data verification can help to increase efficiency in data processing and analysis. This can save time and resources, and help organizations to achieve their objectives more effectively.
Compliance with Regulations: Many industries are subject to regulations that require accurate and reliable data. Data verification can help organizations to comply with these regulations by ensuring that their data is accurate and up-to-date.
Enhanced Customer Satisfaction : By ensuring that data is accurate and reliable, organizations can provide better service to their customers. This can help to enhance customer satisfaction and loyalty.

Limitations of Data Verification

There are some limitations to data verification, including:

Cost : Data verification can be time-consuming and resource-intensive. Depending on the size of the dataset, it can be costly to manually review and verify all the data.
Human Error: Data verification requires human input, which can introduce errors and inconsistencies. Even with careful review, humans may miss errors or make mistakes, which can impact the accuracy of the data.
Limited Scope : Data verification may only verify a subset of the data or may only verify certain types of data. This can limit the effectiveness of data verification in identifying all errors or inconsistencies in the dataset.
Data Volume: As the volume of data increases, it can become increasingly difficult and time-consuming to verify all the data. This can result in incomplete or inaccurate verification, which can impact the accuracy of the data.
Real-Time Data : Real-time data may be difficult to verify in real-time, which can limit the effectiveness of data verification for applications that require real-time data analysis.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Design Review

A discussion of qualitative & quantitative research design, verification: looking beyond the data in qualitative data analysis.

Yet looking outside the data we gather in in-depth interviews (IDIs), group discussions, or observations is important to the integrity of our qualitative research designs. The consideration of alternative sources of information serves to verify the study data while giving the researcher a different, more enriched perspective on study outcomes. It is not important whether this additional input supports the researcher’s conclusions from the primary data; and, indeed, contradictions in the verification process do not necessarily invalidate the study’s findings. What is important, however, is that the researcher recognizes how other points of view can contribute to a more balanced as well as more robust and meaningful analysis rather than relying on study data alone.

There are many proposed approaches to the verification of qualitative research data. Three of the most useful are:

Triangulation : The use of multiple sources to contrast and compare study data to establish supporting and/or contradictory information. A few common forms of triangulation are those that compare study data with data obtained from other sources (e.g., comparing the IDI transcripts from interviews with environmental activists with those from conservationists), a different method (e.g., comparing results from an IDI study to focus group results on the same subject matter), and another researcher (e.g., using multiple researchers in the analysis phase to compare interpretations of the data).
Negative case (or “deviant”) analysis : The researcher actively seeks instances in the study data that contradict or otherwise conflict with the prevailing evidence in the data, i.e., looks for outliers. This analysis compels the researcher to develop an understanding about why outliers exist, leading to a greater comprehension as to the strengths and limits of the research data.
Reflexive journal : A diary kept by the researcher to provide personal thoughts and insights on what happened during the study. It is an invaluable resource that the researcher can use to review and judge the quality of data collection as well as the soundness of the researcher’s interpretations during the analysis phase. This blog has discussed reflexive journals in many posts, including “Reflections from the Field: Questions to Stimulate Reflexivity Among Qualitative Researchers.”

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Trustworthiness of the Data

Thematic Data Analysis in Qualitative Design
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Qualitative researchers are required to articulate evidence of four primary criteria to ensure the trustworthiness of the study’s findings: credibility, transferability, dependability, and confirmability.

Credibility (i.e., data collected is accurate/representative of the phenomenon under study)

Credibility corresponds to the notion of validity in quantitative work but is more about internal validity. The credibility of qualitative data can be assured through multiple perspectives throughout data collection to ensure data are appropriate. This may be done through data, investigator, or theoretical triangulation; participant validation or member checks; or the rigorous techniques used to gather the data.

Transferability (I.e., the extent to which the findings are transferable to other situations)

Transferability is like generalizability in quantitative; however, it is not generalizability. Transferability addresses the applicability of the findings to similar contexts or individuals not to broader contexts. Transferability can be achieved by a “thick description” of the findings from multiple data collection methods.

Dependability (i.e., an in-depth description of the study procedures and analysis to allow the study to be replicated)

Dependability is like reliability in quantitative studies. Dependability can be ensured through rigorous data collection techniques and procedures and analysis that are well documented. Typically, an inquiry audit using an outside reviewer assures dependability. For students, this would be your committee.

Confirmability (i.e., the steps to ensure that the data and findings are not due to the participant and/or researcher bias)

Confirmability is like objectivity in quantitative studies; however, objectivity is not necessarily critical for qualitative studies as long as personal biases are unpacked in the write-up. Unpacking personal bias can be accomplished by a bracketing interview or reflexivity. Confirmability of qualitative data is assured when data are checked and rechecked throughout data collection and analysis to ensure findings would likely be repeatable by others. Confirmability can be documented by a clear coding schema that identifies the codes and patterns identified in analyses. This technique is called an audit trail. It can also be ensured through triangulation and member checking of the data as well as conducting a bracketing interview or practicing reflexivity to confront potential personal bias.

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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.

what is data verification in qualitative research

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Data analysis in qualitative research, theertha raj, august 30, 2024.

While numbers tell us "what" and "how much," qualitative data reveals the crucial "why" and "how." But let's face it - turning mountains of text, images, and observations into meaningful insights can be daunting.

This guide dives deep into the art and science of how to analyze qualitative data. We'll explore cutting-edge techniques, free qualitative data analysis software, and strategies to make your analysis more rigorous and insightful. Expect practical, actionable advice on qualitative data analysis methods, whether you're a seasoned researcher looking to refine your skills or a team leader aiming to extract more value from your qualitative data.

What is qualitative data?

Qualitative data is non-numerical information that describes qualities or characteristics. It includes text, images, audio, and video.

This data type captures complex human experiences, behaviors, and opinions that numbers alone can't express.

A qualitative data example can include interview transcripts, open-ended survey responses, field notes from observations, social media posts and customer reviews

Importance of qualitative data

Qualitative data is vital for several reasons:

It provides a deep, nuanced understanding of complex phenomena.
It captures the 'why' behind behaviors and opinions.
It allows for unexpected discoveries and new research directions.
It puts people's experiences and perspectives at the forefront.
It enhances quantitative findings with depth and detail.

What is data analysis in qualitative research?

Data analysis in qualitative research is the process of examining and interpreting non-numerical data to uncover patterns, themes, and insights. It aims to make sense of rich, detailed information gathered through methods like interviews, focus groups, or observations.

This analysis moves beyond simple description. It seeks to understand the underlying meanings, contexts, and relationships within the data. The goal is to create a coherent narrative that answers research questions and generates new knowledge.

How is qualitative data analysis different from quantitative data analysis?

Qualitative and quantitative data analyses differ in several key ways:

Data type: Qualitative analysis uses non-numerical data (text, images), while quantitative analysis uses numerical data.
Approach: Qualitative analysis is inductive and exploratory. Quantitative analysis is deductive and confirmatory.
Sample size: Qualitative studies often use smaller samples. Quantitative studies typically need larger samples for statistical validity.
Depth vs. breadth: Qualitative analysis provides in-depth insights about a few cases. Quantitative analysis offers broader insights across many cases.
Subjectivity: Qualitative analysis involves more subjective interpretation. Quantitative analysis aims for objective, statistical measures.

What are the 3 main components of qualitative data analysis?

The three main components of qualitative data analysis are:

Data reduction: Simplifying and focusing the raw data through coding and categorization.
Data display: Organizing the reduced data into visual formats like matrices, charts, or networks.
Conclusion drawing/verification: Interpreting the displayed data and verifying the conclusions.

These components aren't linear steps. Instead, they form an iterative process where researchers move back and forth between them throughout the analysis.

How do you write a qualitative analysis?

Step 1: organize your data.

Start with bringing all your qualitative research data in one place. A repository can be of immense help here. Transcribe interviews , compile field notes, and gather all relevant materials.

Immerse yourself in the data. Read through everything multiple times.

Step 2: Code & identify themes

Identify and label key concepts, themes, or patterns. Group related codes into broader themes or categories. Try to connect themes to tell a coherent story that answers your research questions.

Pick out direct quotes from your data to illustrate key points.

Step 3: Interpret and reflect

Explain what your results mean in the context of your research and existing literature.

Als discuss, identify and try to eliminate potential biases or limitations in your analysis.

Summarize main insights and their implications.

What are the 5 qualitative data analysis methods?

Thematic Analysis Identifying, analyzing, and reporting patterns (themes) within data.

Content Analysis Systematically categorizing and counting the occurrence of specific elements in text.

Grounded Theory Developing theory from data through iterative coding and analysis.

Discourse Analysis Examining language use and meaning in social contexts.

Narrative Analysis Interpreting stories and personal accounts to understand experiences and meanings.

Each method suits different research goals and data types. Researchers often combine methods for comprehensive analysis.

What are the 4 data collection methods in qualitative research?

When it comes to collecting qualitative data, researchers primarily rely on four methods.

Interviews : One-on-one conversations to gather in-depth information.
Focus Groups : Group discussions to explore collective opinions and experiences.
Observations : Watching and recording behaviors in natural settings.
Document Analysis : Examining existing texts, images, or artifacts.

Researchers often use multiple methods to gain a comprehensive understanding of their topic.

How is qualitative data analysis measured?

Unlike quantitative data, qualitative data analysis isn't measured in traditional numerical terms. Instead, its quality is evaluated based on several criteria.

Trustworthiness is key, encompassing the credibility, transferability, dependability, and confirmability of the findings. The rigor of the analysis - the thoroughness and care taken in data collection and analysis - is another crucial factor.

Transparency in documenting the analysis process and decision-making is essential, as is reflexivity - acknowledging and examining the researcher's own biases and influences.

Employing techniques like member checking and triangulation all contribute to the strength of qualitative analysis.

Benefits of qualitative data analysis

The benefits of qualitative data analysis are numerous. It uncovers rich, nuanced understanding of complex phenomena and allows for unexpected discoveries and new research directions.

By capturing the 'why' behind behaviors and opinions, qualitative data analysis methods provide crucial context.

Qualitative analysis can also lead to new theoretical frameworks or hypotheses and enhances quantitative findings with depth and detail. It's particularly adept at capturing cultural nuances that might be missed in quantitative studies.

Challenges of Qualitative Data Analysis

Researchers face several challenges when conducting qualitative data analysis.

Managing and making sense of large volumes of rich, complex data can lead to data overload. Maintaining consistent coding across large datasets or between multiple coders can be difficult.

There's a delicate balance to strike between providing enough context and maintaining focus on analysis. Recognizing and mitigating researcher biases in data interpretation is an ongoing challenge.

The learning curve for qualitative data analysis software can be steep and time-consuming. Ethical considerations, particularly around protecting participant anonymity while presenting rich, detailed data, require careful navigation. Integrating different types of data from various sources can be complex. Time management is crucial, as researchers must balance the depth of analysis with project timelines and resources. Finally, communicating complex qualitative insights in clear, compelling ways can be challenging.

Best Software to Analyze Qualitative Data

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Here’s how Looppanel’s features can help with qualitative data analysis:

Automatic Transcription: Quickly turn speech into accurate text; it works across 8 languages and even heavy accents, with over 90% accuracy.
AI Note-Taking: The research assistant can join you on calls and take notes, as well as automatically sort your notes based on your interview questions.
Automatic Tagging: Easily tag and organize your data with free AI tools.
Insight Generation: Create shareable insights that fit right into your other tools.
Repository Search: Run Google-like searches within your projects and calls to find a data snippet/quote in seconds
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Looppanel’s focus on automating research tasks makes it perfect for researchers who want to save time and work smarter.

G2 rating: 4.7/5

Pricing: Free version available, with the Plus version costing $20 monthly.

ChatGPT, developed by OpenAI, offers a range of capabilities for qualitative data analysis including:

Document analysis : It can easily extract and analyze text from various file formats.
Summarization : GPT can condense lengthy documents into concise summaries.
Advanced Data Analysis (ADA) : For paid users, Chat-GPT offers quantitative analysis of data documents.
Sentiment analysis: Although not Chat-GPT’s specialty, it can still perform basic sentiment analysis on text data.

ChatGPT's versatility makes it valuable for researchers who need quick insights from diverse text sources.

How to use ChatGPT for qualitative data analysis

ChatGPT can be a handy sidekick in your qualitative analysis, if you do the following:

Use it to summarize long documents or transcripts
Ask it to identify key themes in your data
Use it for basic sentiment analysis
Have it generate potential codes based on your research questions
Use it to brainstorm interpretations of your findings

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Atlas.ti is a powerful platform built for detailed qualitative and mixed-methods research, offering a lot of capabilities for running both quantitative and qualitative research.

It’s key data analysis features include:

Multi-format Support: Analyze text, PDFs, images, audio, video, and geo data all within one platform.
AI-Powered Coding: Uses AI to suggest codes and summarize documents.
Collaboration Tools: Ideal for teams working on complex research projects.
Data Visualization: Create network views and other visualizations to showcase relationships in your data.

G2 rating: 4.1/5 Pricing: Custom

NVivo is another powerful platform for qualitative and mixed-methods research. It’s analysis features include:

Data Import and Organization: Easily manage different data types, including text, audio, and video.
AI-Powered Coding: Speeds up the coding process with machine learning.
Visualization Tools: Create charts, graphs, and diagrams to represent your findings.
Collaboration Features: Suitable for team-based research projects.

NVivo combines AI capabilities with traditional qualitative analysis tools, making it versatile for various research needs.

Can Excel do qualitative data analysis?

Excel can be a handy tool for qualitative data analysis, especially if you're just starting out or working on a smaller project. While it's not specialized qualitative data analysis software, you can use it to organize your data, maybe putting different themes in different columns. It's good for basic coding, where you label bits of text with keywords. You can use its filter feature to focus on specific themes. Excel can also create simple charts to visualize your findings. But for bigger or more complex projects, you might want to look into software designed specifically for qualitative data analysis. These tools often have more advanced features that can save you time and help you dig deeper into your data.

How do you show qualitative analysis?

Showing qualitative data analysis is about telling the story of your data. In qualitative data analysis methods, we use quotes from interviews or documents to back up our points. Create charts or mind maps to show how different ideas connect, which is a common practice in data analysis in qualitative research. Group your findings into themes that make sense. Then, write it all up in a way that flows, explaining what you found and why it matters.

What is the best way to analyze qualitative data?

There's no one-size-fits-all approach to how to analyze qualitative data, but there are some tried-and-true steps.

Start by getting your data in order. Then, read through it a few times to get familiar with it. As you go, start marking important bits with codes - this is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes - how do they connect?

Finally, think about what it all means in the bigger picture of your research. Remember, it's okay to go back and forth between these steps as you dig deeper into your data. Qualitative data analysis software can be a big help in this process, especially for managing large amounts of data.

In qualitative methods of test analysis, what do test developers do to generate data?

Test developers in qualitative research might sit down with people for in-depth chats or run group discussions, which are key qualitative data analysis methods. They often use surveys with open-ended questions that let people express themselves freely. Sometimes, they'll observe people in their natural environment, taking notes on what they see. They might also dig into existing documents or artifacts that relate to their topic. The goal is to gather rich, detailed information that helps them understand the full picture, which is crucial in data analysis in qualitative research.

Which is not a purpose of reflexivity during qualitative data analysis?

Reflexivity in qualitative data analysis isn't about proving you're completely objective. That's not the goal. Instead, it's about being honest about who you are as a researcher. It's recognizing that your own experiences and views might influence how you see the data. By being upfront about this, you actually make your research more trustworthy. It's also a way to dig deeper into your data, seeing things you might have missed at first glance. This self-awareness is a crucial part of qualitative data analysis methods.

What is a qualitative data analysis example?

A simple example is analyzing customer feedback for a new product. You might collect feedback, read through responses, create codes like "ease of use" or "design," and group similar codes into themes. You'd then identify patterns and support findings with specific quotes. This process helps transform raw feedback into actionable insights.

How to analyze qualitative data from a survey?

First, gather all your responses in one place. Read through them to get a feel for what people are saying. Then, start labeling responses with codes - short descriptions of what each bit is about. This coding process is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes. Are certain ideas coming up a lot? Do different groups of people have different views? Use actual quotes from your survey to back up what you're seeing. Think about how your findings relate to your original research questions.

Which one is better, NVivo or Atlas.ti?

NVivo is known for being user-friendly and great for team projects. Atlas.ti shines when it comes to visual mapping of concepts and handling geographic data. Both can handle a variety of data types and have powerful tools for qualitative data analysis. The best way to decide is to try out both if you can.

While these are powerful tools, the core of qualitative data analysis still relies on your analytical skills and understanding of qualitative data analysis methods.

Do I need to use NVivo for qualitative data analysis?

You don't necessarily need NVivo for qualitative data analysis, but it can definitely make your life easier, especially for bigger projects. Think of it like using a power tool versus a hand tool - you can get the job done either way, but the power tool might save you time and effort. For smaller projects or if you're just starting out, you might be fine with simpler tools or even free qualitative data analysis software. But if you're dealing with lots of data, or if you need to collaborate with a team, or if you want to do more complex analysis, then specialized qualitative data analysis software like NVivo can be a big help. It's all about finding the right tool for your specific research needs and the qualitative data analysis methods you're using.

Here’s a guide that can help you decide.

How to use NVivo for qualitative data analysis

First, you import all your data - interviews, documents, videos, whatever you've got. Then you start creating "nodes," which are like folders for different themes or ideas in your data. As you read through your material, you highlight bits that relate to these themes and file them under the right nodes. NVivo lets you easily search through all this organized data, find connections between different themes, and even create visual maps of how everything relates.

How much does NVivo cost?

NVivo's pricing isn't one-size-fits-all. They offer different plans for individuals, teams, and large organizations, but they don't publish their prices openly. Contact the team here for a custom quote.

What are the four steps of qualitative data analysis?

While qualitative data analysis is often iterative, it generally follows these four main steps:

1. Data Collection: Gathering raw data through interviews, observations, or documents.

2. Data Preparation: Organizing and transcribing the collected data.

3. Data Coding: Identifying and labeling important concepts or themes in the data.

4. Interpretation: Drawing meaning from the coded data and developing insights.

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Participant Validation: A Strategy to Strengthen the Trustworthiness of Your Study and Address Ethical Concerns

Open Access
First Online: 15 February 2022

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Tone Lindheim 2

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How can you as a researcher ensure the trustworthiness of your data and results? This chapter presents participant validation as a strategy for doing so and discusses the ethical challenges that come with it. Participant validation implies that the researcher in one way or another presents the data material or the preliminary analysis to the informants to validate and assess interpretations. In this chapter, previous literature and studies of participant validation are reviewed, and a case study of cultural diversity and inclusion in the workplace is used as an example of how participant validation can be incorporated in the research process. The chapter shows how participant validation addresses as well as raises ethical concerns. The examples used in the chapter demonstrate how participant validation can contribute to qualitative research by generating new data that can be incorporated into a study. As an integrated part of the research process, participant validation represents a site and an opportunity for values work.

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The Trustworthiness of Content Analysis

The Business of Validity, Reliability and Authentic Need

Ethics in Qualitative Research

Participant validation
Member checking

Introduction

After gathering and analysing the empiricaldata from your study of values or values work, how can you ensure the trustworthiness of your study? Trustworthiness is important for you as a researcher, for the informants who have contributed to your study and for the reader. The technical terms often used to describe this are validity, reliability and generalisability (Denzin & Lincoln, 2018 ). In qualitative research, where the boundaries between the researcher and the researched are unclear, Denzin and Lincoln ( 2018 ; see also Krefting, 1991 ) recommend using credibility, dependability and transferability as equivalent terms. Different measures, like extended periods of fieldwork and triangulation of methods and sources of data, can be used to strengthen the credibility of a study. Participant validation, or member checking (the terms are here used interchangeably), is another strategy to strengthen the credibility of data and results (Lincoln & Guba, 1985 ; Merriam & Tisdell, 2015 ). Participant validation implies that you as a researcher in one way or another present the data material or the preliminary analysis to the informants to validate and assess interpretations. The purpose is to ensure the trustworthiness of your study from the perspective of the researcher, the informant and the reader (Carlson, 2010 ). With participant validation you are transparent about how your informants are represented, and it allows you to correct misunderstandings and document the research process.

This chapter describes how participant validation can be incorporated in the research design of valueswork studies. It is a strategy to address ethical concerns in a study, for example, related to transparency and power, but it also raises new ethical concerns. To decide how to incorporate participant validation in your study, it is useful to explore and develop a broad understanding of the ethical dilemmas involved. This chapter thus addresses the following questions: how can participant validation be incorporated into a study of values or values work, and how does participant validation respond to and generate ethical concerns? The chapter first reviews existing literature on participant validation and then uses a case study of cultural diversity and inclusion as an example of how participant validation can be incorporated into the research process. For researchers studying valueswork, the example demonstrates how participant validation may be an opportunity for valueswork in and of itself, generating valuable data that can be incorporated into a study.

Former Studies on Participant Validation

The most referenced text on participant validation, or member checking, is Lincoln and Guba’s ( 1985 ) book on naturalistic inquiry. Naturalistic inquiry is the study of a social phenomenon or people’s actions in their specific context or natural environment. In this type of research, the boundaries between you as a researcher and the subjects being researched are fuzzy (See Chap. 12 ). The ontological and epistemological foundation of naturalistic inquiry is that the realities you study are socially constructed. In the research process, the researcher and the researched interact and cocreate understandings and interpretations. Participant validation is one strategy for cocreation in research, and Lincoln and Guba ( 1985 ) suggest how it can be incorporated at different stages of the research process. Most studies claiming to have used participant validation refer to sharing interviewtranscripts or quotations with the informants. While that may be a way to correct misunderstandings and errors, it does not involve informants in the analysis of the data, and it does not reap the full benefits of member checking as an approach. Other researchers have demonstrated how participant validation can be incorporated in the research design and have applied it in a more extensive way. Three studies are presented here: Buchbinder’s ( 2011 ) review of experiences with validation interviews, Birt et al.’s ( 2016 ) elaboration of a synthesised member checking method and Slettebø’s ( 2020 ) use of participant validation in an action research project. The three studies highlight different aspects of the use of participant validation and illustrate different ways of applying it in studies of values or values work.

The first study analyses experiences with validation interviews. Buchbinder ( 2011 ) interviewed social work students who had used individual validation interviews in their study of more experienced social workers. The students first interviewed the social workers, transcribed the interviews and identified core themes. In the validation interview, the preliminary analysis was presented to the social worker, offering him or her an opportunity to confirm, modify or reject the analysis. Buchbinder’s study surfaced various ethical concerns: the legitimacy of offering interpretations going beyond the interviewees’ own understanding of their narratives, handling relationships and roles and the use and abuse of power in the validation process. The validation interviews challenged the students’ handling of the boundaries between interviewer and interviewee. As social work students, they were younger and less experienced than the social workers they interviewed. The interviews generated feelings of uneasiness when the students presented their interpretations of what had been said in the first interview. The feelings of uneasiness varied with how close or distant the interviewer and interviewee were prior to the interview. During the research process, the students experienced several shifts of power. In the initial interview, the experienced social workers had more power in determining what was said but were simultaneously vulnerable when sharing personal information. In the validation interview, the students assumed a more powerful position, offering interpretations of the first interview. At the same time, they felt vulnerable as their interpretations were being assessed by a senior person. In summary, Buchbinder presents validation interviews as one way of incorporating participant validation into a study. Buchbinder demonstrates how validation interviews address the ethical concerns of interpretations and power differences by offering informants an opportunity to correct the researcher’s interpretation. On the other hand, the validation interviews generated new ethical concerns related to roles, boundaries and power.

The second study offers an example and a model for how participant validation can be incorporated in studies with larger samples of informants, using written communication between the researcher and the informants instead of face-to-face validation interviews. Birt et al. ( 2016 ) developed a five-step ‘synthesized member checking’ (SMC) process and tested it out in a health research study. The first step of the model is to prepare a synthesised summary of emerging themes from the total sample of interviews using illustrative, anonymised quotes from the different interviews. In the second step, the informants’ eligibility for participating in the member checking process is considered to ensure that the research process will not inflict unnecessary harm on the informants. In the third step, the synthesised report is sent to the selected informants with an invitation to make corrections and add comments. The responses are collected and added to the data material in the fourth step. Finally, the new data are integrated and coded. In addition to developing a model for member checking, Birt et al.’s study addresses two central ethical concerns. First, by offering the informants an analysis of the total sample, the information from the interview is placed in a broader context, which gives the informants a better understanding of how their responses have been interpreted in relation to others. This relates to the ethical responsibility of ensuring that informants understand how the information they have provided is used. Even if the informants have received information about the purpose of the study before the interview, this form of member checking enhances a more comprehensive understanding of the research process. Second, an ethical concern in social research is that the study should be as little harmful to the researched subjects as possible. The second step in the SMC model addresses the ethical issue of the harmful effects of the research on the informants. For research on sensitive issues, participant validation may represent an additional burden and harm to the informants and thus generate an ethical concern. By evaluating whom to include in the member checking process, the possible negative effect is reduced. The fourth and fifth steps of the model illustrate how participant validation is used to generate new data for the study.

The third study highlights the empowering effect of participant validation and demonstrates how the process may modify and generate new and relevant data. The study presents an action research project involving parents who had involuntarily had a child placed in care (Slettebø, 2020 ). Throughout the research project, the parents participated in focus groups with the aim of developing new types of services for parents in their situation. At the end of the project, a preliminary report was elaborated and shared with the parents. This use of participant validation was aligned with the empowering purpose of the action research project. About a quarter of the participants received a 70-page hard copy version of the report, and after three weeks, comments from the parents were collected through telephone interviews. The comments were incorporated into the text and analysed as additional data. Participant validation contributed to the final report by complementing the researcher’s first draft, adjusting the analysis, and refining the use of theoretical concepts. Beyond generating additional data for the study, the process encouraged revisions of the use of concepts and methods for future studies. In this study, participant validation helped maintaining the proactive role of the parents throughout the process—a central ethical concern in action research. Slettebø discusses how the academic jargon of the research report represented a barrier as well as an empowering conceptual tool for the parents to handle their experiences, thus demonstrating how participant validation in this study both addressed and generated new ethical concerns.

In the three studies reviewed here, informants were not only invited to review the transcripts of the interviews they gave but were provided with an opportunity to respond the researchers’ interpretations of the data material at different stages of the process. In Buchbinder’s ( 2011 ) study, informants were presented with thematic analyses of their own interviews, whereas Birt et al. offered participants a synthesised preliminary analysis of the whole sample. In Slettebø’s ( 2020 ) study, the participants received copies of a preliminary report on the whole research project. The three studies illustrate how comments from participants may be collected through face-to-face interviews, in writing, or through telephone interviews. Inviting the informants to respond to and engage with the researcher’s interpretation of the data material disrupts the inherent power relations of the research process, but it also generates additional ethical concerns.

Participant Validation in a Study of Cultural Diversity and Inclusion

A case study of cultural diversity and inclusion in three nursing homes will here be used as an example of how participant validation can be incorporated at different stages of the research process. The case study combined different methods and sources of information to generate empirical data. In the nursing home units, I observed the interaction between employees and residents and participated in their different meetings and activities. Six unit managers were shadowed for a full shift each. During the shadowing, the unit managers’ activities were recorded in a format indicating how much time was spent on the activity, the location, the participants and who initiated the activity (see Chap. 8 ). After observation and shadowing, 27 interviews with managers and employees were conducted.

In the following, three different uses of participant validation are described. The examples illustrate how to incorporate participant validation in a study and how this strategy both addresses and raises ethical concerns. In addition, the examples demonstrate how participant validation provides opportunities for valueswork when the informants assess their own work and the management of their units.

Validation of Shadowing Reports

The unit managers received transcripts of the shadowing report before the interview, and in the interview, they validated my understanding of their working day. The unit managers could then correct mistakes in the shadowing report and comment on how representative this day was in comparison to other working days. In the interview, they further explained and interpreted what happened during the day I shadowed them. In general, the managers found it interesting to get this report of their day. Some of them had felt it awkward to be shadowed, and they were uncertain and curious about what information had been recorded about them. When they read the shadowing report, I sensed a sigh of relief, and one of them expressed that it was not as bad as she had thought it would be. Sharing the shadowing report with the unit managers thus responded to an ethical concern for transparency with informants in the research process.

Validating the shadowing report was an opportunity for the unit mangers to assess their own role and work. The following two quotes demonstrate the unit managers’ responses to the report:

It was very exciting to read. I was really happy when I read it, so shared it with my partner at home and said: “See! I have never written down what I have done at work, but now you can see what I do when I go to work!” (laughing). But I am encouraged by what I see. From this I see that I am not sitting so much by the computer to cover shifts, and that is good, because that is what I prioritise the least. (…) I spend more time on my employees, in conversations, listening to what they want, what we can change, having time for employees and procedures in the unit. (Dragan, unit manager)

First and foremost, I thought about how much and how varied [my day was], and how much could actually have been done without me—I think. I thought that right away. I am going to share this with Hege [the CEO]. It is a supervising tool for us. (…) [My] lack of structure is quite evident in the report. (Jonathan, unit manager)

These two quotes highlight the unit managers’ priorities at work and what they consider to be important. Dragan was proud of how the shadowing report confirmed his priorities, showing that he spent more time engaging with employees than doing administrative tasks. Jonathan was less satisfied. The report showed that he spent time on things he should not have done, and he suggested discussing the report with his supervisor. As such, participant validation generated reflections on priorities and subsequent initiatives to make changes. The unit managers’ evaluations and adjustments represent valueswork as a result of the validation process.

Validation of Observation in Interviews

Participant validation was also applied in the other interviews with employees in the units. The interviews took place after observation in the units, so incidents from these days were presented and discussed in the interviews, giving the informants an opportunity to offer their points of view or to explain further what had happened. As such, validation in the interviews adjusted my interpretation of the observational data.

Validation of observations that involved other informants generated new understanding and dilemmas. In one of the units, I had followed the unit manager closely and was in many ways impressed with what I saw. When I interviewed one of the employees about the unit manager’s leadership, more critical observations surfaced:

She is a bit direct. And it is not everybody who likes that. You feel that you are treated very hard sometimes. Nobody likes to be treated badly. Everybody does their best, and still, they get “pepper”. (…) And then we have heard she is the best to save money. So, it means that she doesn’t spend money on calling in substitutes. (Zahra, nurse)

At first, these comments were surprising, but in the following interviews with other employees in the unit, Zahra’s comments were confirmed. When employees talked about the unit manager’s leadership in the interview, they also engaged in reflections around the issue. Milan, another nurse in the unit expressed it this way:

She can be experienced as strict, and maybe unfair. But I think she is a good leader. I know that in our unit there is a general discontent with her. And I understand that the others can get upset or feel that she is condescending in the way she talks to them. (…) If it had been a male manager who had behaved the same way, there would have been fewer employees reacting. Because if a man is very direct and strict and so on, he’s ambitious, he wants things done. If it is a woman, then, well, well, she’s a bitch, she’s strict, you know. That’s how people think.

When the unit manager was interviewed at the end, the questions were revised based on the information from the employees. The unit manager then shared about the ongoing conflict in the unit and how she was handling the situation (Lindheim, 2020 ). In this example, participant validation elicited discussions of central leadershipvalues and generated further valueswork. On the other hand, participant validation generated ethical concerns related to how information should be shared and used with other informants (see Røthing, 2002 for further discussion).

Validation of Preliminary Analysis in Focus Groups

After a preliminary analysis of the data material from observation and interviews, validation meetings were held with a selected group of managers in two of the nursing homes. Footnote 1 A central finding of the study concerned the employment situation of immigrant employees without formal healthcare credentials (Lindheim, 2021 ). Tables that displayed the numbers and percentages of employees in different categories of healthcare positions and the size of their employment contracts were presented in the validation meetings. The participants could then compare the information from their nursing home with the information from the other two nursing homes. They were informed that the three nursing homes had different operating structures (one run by the municipality, one run by a non-profit entity and one run by a for-profit entity), but the identities of the nursing homes were kept anonymous. The comparison of the three nursing homes revealed that employment policies were applied differently, and informants from a nursing home with one operating structure justified their way of doing it and criticised the others:

We, too, follow the Working Environment Act in that you are entitled to a permanent position [when you have worked for three years]. It is exploitation of the staff not to give them extra shifts to avoid [them claiming] a permanent position. (Excerpt from validation meeting)

The validation meetings stirred up discussions among the participants about the identity and values of the nursing homes and evolved into what is here understood as valueswork. The validation meetings thus generated new data material that was incorporated into the study. The arguments and interpretations that emerged would not have been accessed without participant validation of the analysis of the data material. The validation meeting also generated concerns related to how informants’ reactions should be handled. How should I balance ethical responsibility and analytical freedom (Røthing, 2002 )? Should I accept their responses at face value and incorporate their feedback directly as new data, or could I further interpret their reactions as potential justifications and defence mechanisms?

Participant Validation—Ethical Concerns and Values Work

Participant validation is a strategy to strengthen the trustworthiness of a study. The review of the literature and the examples from the case study highlight three further contributions of participant validation when it is incorporated in the research process: it addresses and raises ethical concerns; it generates new data that can be incorporated into the study and it functions as a site and instantiation of values work.

Addressing and Generating Ethical Concerns

Participant validation addresses ethical concerns in the research process. Core issues in this regard are transparency and trust in the research process and the unequal power relation between the researcher and the researched (Buchbinder, 2011 ; Fangen, 2010 ; Slettebø, 2020 ). In the case study described above, by sharing the shadowing reports with the unit managers, the informants trusted that their work situation and everyday challenges were understood. A side effect of trust in the research process was that it improved the quality of the interviews that followed. When trust and rapport were established, the unit managers shared information more openly in the interviews. The case study also illustrates that transparency and power are interrelated. Sharing instead of withholding data, like the shadowing reports, modified the experience of power imbalance between researcher and informants, which in turn increased trust.

In the validation meetings in the nursing homes, the informants were invited to respond and react to the analysis of the data material from all three nursing homes, addressing again the ethical concern of transparency in the research process. The opportunity to compare findings from their own nursing home with other nursing homes also modified the power relation between the researcher and the researched (Birt et al., 2016 ). The interpretation and outside perspective offered in the validation meetings had an empowering potential (Slettebø, 2020 ), which could further reduce the power imbalance in the research process.

However, participant validation also generated a new set of ethical concerns. Of the examples presented above, the situation with the manager who had conflictful relationships with her employees elicited the most ethical concerns and feelings of uneasiness (Buchbinder, 2011 ). The discrepancy between the manager’s perspective and the employees’ perspective in the interviews surfaced questions around handling the issue of anonymity, protecting both managers and employees from harmful effects of the research process. In the information provided prior to the study, informants were ensured anonymity. In publications from the study, informants and nursing homes are anonymised. However, the informants in the study had knowledge of the other persons involved from their nursing home, in particular the other interviewees from their units. Røthing ( 2002 ) discusses the dilemma of external versus internal anonymity. In her study of couples, the partners were interviewed individually, while the data material from both parties was analysed together. If the couples read the analyses, the partners’ perspectives would be revealed. My solution to the challenge in the case study presented here was to examine even more carefully which quotes from the informants to use. I wanted to shed light on the tension between the manager and the employees’ perspective without causing further conflicts and placing the informants in a vulnerable position. By choosing quotes that contained information that was already known to both parties, I sought to safeguard both concerns.

The validation process also raised questions of representation of informants in the articles published from the study. How should the information and feedback received from one informant or from one validation meeting be balanced with information from other informants and my own interpretation. (Birt et al., 2016 )? Would they feel betrayed if they read the publication afterwards (Røthing, 2002 ; Slettebø, 2020 )? In the writing process, this question was troubling, and the papers written for publication were revised yet again to ensure that the presentation stayed true to the data material. These questions reflect the challenge of balancing the impetus to conduct research that sheds light on injustice in organisations with concerns for avoiding bias and partiality.

The validation meeting with the managers surfaced yet another ethical concern. Who should participate in the validation meeting? Was it right to have this meeting only with managers? What about the informants in subordinate positions? In hindsight I would have preferred a more representative validation meeting. The selection of participants was a pragmatic solution, which is often the case in research. It was easier to gather a smaller group of managers who had more flexibility in their work schedules than to organise a larger gathering for which employees had to leave their daily duties in the units at the nursing homes.

Generating New Data

In line with Slettebø’s ( 2020 ) findings, the experience from the case study discussed here was that participant validation generated new data that were incorporated into the study. The clearest example was the discussions generated during the validation meetings. When the informants examined the statistics on employee categories and employment contracts, they offered new information of how the system regulated these issues in the nursing homes, and they argued for their positions and priorities with reference to the other nursing homes. The tendency for employees without formal healthcare credentials in the nursing homes to remain in precarious employment (Lindheim, 2021 ) was an issue that stood out more clearly after the analysis of the data material. The validation meetings thus offered an opportunity to probe further into this issue, which had not been as evident during observations and interviews.

Focus groups are not frequently used in participant validation (Birt et al., 2016 ). However, the use of focus groups or validation meetings with multiple informants has the potential to generate discussions at a different level than what individual validation interviews or written feedback can do.

Participant Validation as a Site and Opportunity for Values Work

The examples from the case study presented above illustrate how participant validation may represent a site and an opportunity for values work. Beyond researching values work as a topic, incorporating participant validation into the research process may generate processes of values work, which offers an opportunity to study values work in situ and in vivo (Zilber, 2020 ). This was evident when the unit mangers assessed and evaluated their management practices in light of the shadowing format. Another example was the validation meetings, which generated opportunities to discuss the identity and values of the nursing home when the managers compared their nursing home with the others included in the study. This finding resonates with Slettebø’s ( 2020 ) experience with validation interviews in his study.

Concluding Remarks

Why should you incorporate participant validation in the research process when you study values work in an organisation? A first answer to that question is that it is a strategy to ensure the trustworthiness of the data and results of your study, and, second, it is a way to address ethical concerns of transparency and power imbalance in the research process. In addition, the validation process may itself result in values work. You may use participant validation when you collect different sources of data and data from different informants early in the process. To reap the benefits of this strategy I would encourage you to also include participant validation at a later stage in the research process, inviting the informants to validate and discuss your analysis and interpretation of data. This way, participant validation have a further empowering potential and may add valuable data to your study of values and values work.

The third nursing home was also offered the same opportunity but did not respond to the invitation, nor to a subsequent reminder.

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Lindheim, T. (2022). Participant Validation: A Strategy to Strengthen the Trustworthiness of Your Study and Address Ethical Concerns. In: Espedal, G., Jelstad Løvaas, B., Sirris, S., Wæraas, A. (eds) Researching Values. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-90769-3_13

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What is credibility in qualitative research and how do we establish it?

Since we consistently get questions about issues of trustworthiness in qualitative research, we decided to do a four-part series that really goes in-depth about each aspect of trustworthiness and how it can be established. There are four aspects of trustworthiness that qualitative researchers must establish: credibility, dependability, transferability, and confirmability. We begin the series here with a discussion of credibility in qualitative research.

Credibility is the first aspect, or criterion, that must be established. It is seen as the most important aspect or criterion in establishing trustworthiness. This is because credibility essentially asks the researcher to clearly link the research study’s findings with reality in order to demonstrate the truth of the research study’s findings. Credibility in qualitative research also has the most techniques available to establish it, compared to the other three aspects of trustworthiness. Here we focus on the two most important techniques (triangulation and member checking), since these will be the ones you find most often in qualitative research.

Triangulation: This is something that every qualitative researcher should be familiar with. Triangulation involves using multiple methods, data sources, observers, or theories in order to gain a more complete understanding of the phenomenon being studied. It is used to make sure that the research findings are robust, rich, comprehensive, and well-developed. There are four types of triangulation that researchers can employ.

Methods triangulation : This involves utilizing different data collection methods in order to check the consistency of the findings.
Triangulation of sources : This involves utilizing different data sources within the same method. This could be if you are using two different populations, interviewing people at different points in time, in private vs. public settings, or comparing people with different perspectives.
Analyst triangulation : This involves utilizing another analyst to review the findings or using multiple observers and analysts. This is helpful to illuminate blind spots in the analysis process.
Theoretical triangulation : This involves using multiple theoretical perspectives to analyze the data.

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Member-checking: This is the second important technique that qualitative researchers use to establish credibility. This is a technique in which the data, interpretations, and conclusions are shared with the participants. It allows participants to clarify what their intentions were, correct errors, and provide additional information if necessary.

When to Use the 4 Qualitative Data Collection Methods

Qualitative data collection methods are the different ways to gather descriptive, non-numerical data for your research.

Popular examples of qualitative data collection methods include surveys, observations, interviews, and focus groups.

But it’s not enough to know what these methods are. Even more important is knowing when to use them.

In an article published in Neurological Research and Practice titled, “How to use and assess qualitative research methods,” authors Busetto, Wick, and Gambinger assert that qualitative research is all about “flexibility, openness and responsivity to context . ”

Because of this, “the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research,” according to the authors.

This makes sense to me, too. And it means you have to use intuition and a pinch of guidance to know when—and how often—to use a specific qualitative data collection method.

In this post, you’ll learn when to use the most common methods: interviews, focus groups, observations, and open-ended surveys.

#1. Interviews

An interview is a qualitative data collection method where a researcher has a one-on-one conversation with a participant.

The goal of an interview is to explore how the participant feels about a specific topic. You’re mining for their unique experiences, perceptions, and thoughts.

There’s usually an element of structure here, with the researcher asking specific questions. But there’s room for organic discussion, too. The interviewer might take notes or record the session—or both—to capture the qualitative data collected.

Interviews are slower, in some ways, than other qualitative data collection methods. Since you can only talk to one person at a time, you might not get as much data as you would from a survey sent out to 100 people at once.

But interviews are a great way to go deep into a subject and collect details you wouldn’t get from a static survey response.

Interviews are ideal to use when:

You need to know the “why”: A one-on-one conversation can help participants open up about the reasons they feel the way they do about a certain topic.
You’re dealing with a sensitive topic: With an interview, you can create a safe space for a person to share their feelings without fear of judgment from other people.
You want to know someone’s personal, lived experience: In a group setting, no one likes the person who takes over and tells their life story rather than participate in a larger conversation. But if you want that life story—if it’s relevant to your research—an interview is ideal.

There are times when interviews aren’t such a great choice, though.

Choose another qualitative data collection method when:

You need information from lots of people, and quickly. Interviews are slow. If you need less depth and more breadth, go with a survey or questionnaire.
You don’t have a lot of resources to spare. It takes a significant amount of time and money to plan and carry out interviews. Most of the time, people don’t jump at the opportunity to participate in your research unless there’s an incentive—usually cash or a gift card. It ends up adding up to quite a bit.

#2. Focus Groups

A focus group is a qualitative data collection method where a small group of people discuss a topic together. A moderator is there to help guide the conversation. The goal here is to get everyone talking about their unique perspectives—and their shared experiences on a topic.

There’s one giant difference between focus groups and interviews, according to the authors of a 2018 article, “The use of focus groups discussion methodology: Insights from two decades of application in conservation,” published in the journal Methods in Ecology and Evolution . The article argues that in a one-on-one interview, the interviewer takes on the role of “investigator” and plays a central role in how the dynamics of the discussion play out.

But in a focus group, the researcher “takes a peripheral, rather than a centre-stage role in a focus group discussion.”

AKA, researchers don’t have as much control over focus groups as they do interviews.

And that can be a good thing.

Focus groups are ideal to use when:

You’re in the early stages of research. If you haven’t been able to articulate the deeper questions you want to explore about a topic, a focus group can help you identify compelling areas to dig into.
You want to study a wide range of perspectives. A focus group can bring together a very diverse group of people if you want it to—and the conversation that results from this gathering of viewpoints can be incredibly insightful.

So when should you steer clear of focus groups?

Another research method might be better if:

You need raw, real honesty—from as many people as possible. Some participants might share valuable, sensitive information (like their honest opinions!) in a focus group. But many won’t feel comfortable doing so. The social dynamics in a group of people can greatly influence who shares what. If you want to build rapport with people and create a trusting environment, an interview might be a better choice.

#3. Observation

Do you remember those strange, slightly special-feeling days in school when a random person, maybe the principal, would sit in on your class? Watching everyone, but especially your teacher? Jotting down mysterious notes from time to time?

If you were anything like me, you behaved extra-good for a few minutes…and then promptly forgot about the person’s presence as you went about your normal school day.

That’s observation in a nutshell, and it’s a useful way to gather objective qualitative data. You don’t interfere or intrude when you’re observing.

You just watch.

Observation is a useful tool when:

You need to study natural behavior. Observation is ideal when you want to understand how people behave in a natural (aka non-conference-room) environment without interference. It allows you to see genuine interactions, routines, and practices as they happen. Think of observing kids on a playground or shoppers in a grocery store.
Participants may not be likely to accurately self-report behaviors. Sometimes participants might not be fully aware of their behaviors, or they might alter their responses to seem more “normal” or desirable to others. Observation allows you to capture what people do, rather than what they say they do.

But observation isn’t always the best choice.

Consider using another qualitative research method when:

The topic and/or behaviors studied are private or sensitive. Publicly observable behavior is one thing. Stuff that happens behind closed doors is another. If your research topic requires more of the latter and less of the former, go with interviews or surveys instead.
You need to know the reasons behind specific behaviors. Observation gets you the what , but not the why . For detailed, in-depth insights, run an interview or open-ended survey.

#4. Open-Ended Surveys/Questionnaires

A survey is a series of questions sent out to a group of people in your target audience.

In a qualitative survey, the questions are open-ended. This is different from quantitative questions, which are closed, yes-or-no queries.

There’s a lot more room for spontaneity, opinion, and subjectivity with an open-ended survey question, which is why it’s considered a pillar of qualitative data collection.

Of course, you can send out a survey that asks closed and open-ended questions. But our focus here is on the value of open-ended surveys.

Consider using an open-ended survey when:

You need detailed information from a diverse audience. The beauty of an open-ended questionnaire is you can send it to a lot of people. If you’re lucky, you’ll get plenty of details from each respondent. Not as much detail as you would in an interview, but still a super valuable amount.
You’re just exploring a topic. If you’re in the early stages of research, an open-ended survey can help you discover angles you hadn’t considered before. You can move from a survey to a different data collection method, like interviews, to follow the threads you find intriguing.
You want to give respondents anonymity. Surveys can easily be made anonymous in a way other methods, like focus groups, simply can’t. (And you can still collect important quantitative data from anonymous surveys, too, like age range, income level, and years of education completed.)

Useful though they are, open-ended surveys aren’t foolproof.

Choose another method when:

You want to ask more than a few questions about a topic. It takes time and energy to compose an answer to an open-ended question. If you include more than three or four questions, you can expect the answers to get skimpier with each one. Or even completely absent by Question #4.
You want consistently high-quality answers. Researchers at Pew Research Center know a thing or two about surveys. According to authors Amina Dunn and Vianney Gómez in a piece for Decoded , Pew Research Center’s behind-the-scenes blog about research methods, “open-ended survey questions can be prone to high rates of nonresponse and wide variation in the quality of responses that are given.” If you need consistent, high-quality answers, consider hosting interviews instead.

How to Decide Which Qualitative Data Collection Method to Use

Choosing the right qualitative data collection method can feel overwhelming. That’s why I’m breaking it down into a logical, step-by-step guide to help you choose the best method for your needs.

(Psst: you’ll probably end up using more than one of these methods throughout your qualitative research journey. That’s totally normal.)

Okay. Here goes.

1. Start with your research goal

If your goal is to understand deep, personal experiences or the reasons behind specific behaviors, then interviews are probably your best choice. There’s just no substitute for the data you’ll get during a one-on-one conversation with a research participant. And then another, and another.
If you’re not sure what your research goals are, begin by sending out a survey with general, open-ended questions asking for your respondents’ opinions about a topic. You can dig deeper from there.

2. Consider how sensitive your topic is

If you’re dealing with a sensitive or private topic, where participants might not feel comfortable sharing in a group setting, interviews are ideal. They create a safe, confidential environment for open discussion between you and the respondent.
If the topic is less sensitive and you want to see how social dynamics influence opinions, consider using focus groups instead.

3. Evaluate whether you need broad vs. deep data

If you need broad data from a large number of people quickly, go with open-ended surveys or questionnaires . You don’t have to ask your respondents to write you an essay for each question. A few insightful lines will do just fine.
If you need deep data, run interviews or focus groups. These allow for more in-depth responses and discussions you won’t get with a survey or observation.

4. Think about the context of your research

If you want to study behavior in a natural setting without interference, observation is the way to go. More than any other, this method helps you capture genuine behaviors as they happen in real life.
But if you need to understand the reasons behind those behaviors, remember that observation only provides the what, not the why. In these cases, follow up with interviews or open-ended surveys for deeper insights.

5. Assess your resources If time and budget are limited, consider how many resources each qualitative data collection method will require. Open-ended surveys are less expensive—and faster to send out and analyze —than interviews or focus groups. The latter options require more time and effort from participants—and probably incentives, too.

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Qualitative Research

What is qualitative research.

Qualitative research is a methodology focused on collecting and analyzing descriptive, non-numerical data to understand complex human behavior, experiences, and social phenomena. This approach utilizes techniques such as interviews, focus groups, and observations to explore the underlying reasons, motivations, and meanings behind actions and decisions. Unlike quantitative research, which focuses on measuring and quantifying data, qualitative research delves into the 'why' and 'how' of human behavior, providing rich, contextual insights that reveal deeper patterns and relationships.

The Basic Idea

Ever heard of the saying “quality over quantity”? Well, some researchers feel the same way!

Imagine you are conducting a study looking at consumer behavior for buying potato chips. You’re interested in seeing which factors influence a customer’s choice between purchasing Doritos and Pringles. While you could conduct quantitative research and measure the number of bags purchased, this data alone wouldn’t explain why consumers choose one chip brand over the other; it would just tell you what they are purchasing. To gather more meaningful data, you may conduct interviews or surveys, asking people about their chip preferences and what draws them to one brand over another. Is it the taste of the chips? The font or color of the bag? This qualitative approach dives deeper to uncover why one potato chip is more popular than the other and can help companies make the adjustments that count.

Qualitative research, as seen in the example above, can provide greater insight into behavior, going beyond numbers to understand people’s experiences, attitudes, and perceptions. It helps us to grasp the meaning behind decisions, rather than just describing them. As human behavior is often difficult to qualify, qualitative research is a useful tool for solving complex problems or as a starting point to generate new ideas for research. Qualitative methods are used across all types of research—from consumer behavior to education, healthcare, behavioral science, and everywhere in between!

At its core, qualitative research is exploratory—rather than coming up with a hypothesis and gathering numerical data to support it, qualitative research begins with open-ended questions. Instead of asking “Which chip brand do consumers buy more frequently?”, qualitative research asks “Why do consumers choose one chip brand over another?”. Common methods to obtain qualitative data include focus groups, unstructured interviews, and surveys. From the data gathered, researchers then can make hypotheses and move on to investigating them.

It’s important to note that qualitative and quantitative research are not two opposing methods, but rather two halves of a whole. Most of the best studies leverage both kinds of research by collecting objective, quantitative data, and using qualitative research to gain greater insight into what the numbers reveal.

You may have heard the world is made up of atoms and molecules, but it’s really made up of stories. When you sit with an individual that’s been here, you can give quantitative data a qualitative overlay. – William Turner, 16th century British scientist 1

Quantitative Research: A research method that involves collecting and analyzing numerical data to test hypotheses, identify patterns, and predict outcomes.

Exploratory Research: An initial study used to investigate a problem that is not clearly defined, helping to clarify concepts and improve research design.

Positivism: A scientific approach that emphasizes empirical evidence and objectivity, often involving the testing of hypotheses based on observable data. 2

Phenomenology: A research approach that emphasizes the first-person point of view, placing importance on how people perceive, experience, and interpret the world around them. 3

Social Interaction Theory: A theoretical perspective that people make sense of their social worlds by the exchange of meaning through language and symbols. 4

Critical Theory: A worldview that there is no unitary or objective “truth” about people that can be discovered, as human experience is shaped by social, cultural, and historical contexts that influences reality and society. 5

Empirical research: A method of gaining knowledge through direct observation and experimentation, relying on real-world data to test theories.

Paradigm shift: A fundamental change in the basic assumptions and methodologies of a scientific discipline, leading to the adoption of a new framework. 2

Interpretive/descriptive approach: A methodology that focuses on understanding the meanings people assign to their experiences, often using qualitative methods.

Unstructured interviews: A free-flowing conversation between researcher and participant without predetermined questions that must be asked to all participants. Instead, the researcher poses questions depending on the flow of the interview. 6

Focus Group: Group interviews where a researcher asks questions to guide a conversation between participants who are encouraged to share their ideas and information, leading to detailed insights and diverse perspectives on a specific topic.

Grounded theory : A qualitative methodology that generates a theory directly from data collected through iterative analysis.

When social sciences started to emerge in the 17th and 18th centuries, researchers wanted to apply the same quantitative approach that was used in the natural sciences. At this time, there was a predominant belief that human behavior could be numerically analyzed to find objective patterns and would be generalizable to similar people and situations. Using scientific means to understand society is known as a positivist approach. However, in the early 20th century, both natural and social scientists started to criticize this traditional view of research as being too reductive. 2

In his book, The Structure of Scientific Revolutions, American philosopher Thomas Kuhn identified that a major paradigm shift was starting to occur. Earlier methods of science were being questioned and replaced with new ways of approaching research which suggested that true objectivity was not possible when studying human behavior. Rather, the importance of context meant research on one group could not be generalized to all groups. 2 Numbers alone were deemed insufficient for understanding the environment surrounding human behavior which was now seen as a crucial piece of the puzzle. Along with this paradigm shift, Western scholars began to take an interest in ethnography , wanting to understand the customs, practices, and behaviors of other cultures.

Qualitative research became more prominent throughout the 20th century, expanding beyond anthropology and ethnography to being applied across all forms of research; in science, psychology, marketing—the list goes on. Paul Felix Lazarsfield, Austrian-American sociologist and mathematician often known as the father of qualitative research, popularized new methods such as unstructured interviews and group discussions. 7 During the 1940s, Lazarfield brought attention to the fact that humans are not always rational decision-makers, making them difficult to understand through numerical data alone.

The 1920s saw the invention of symbolic interaction theory, developed by George Herbert Mead. Symbolic interaction theory posits society as the product of shared symbols such as language. People attach meanings to these symbols which impacts the way they understand and communicate with the world around them, helping to create and maintain a society. 4 Critical theory was also developed in the 1920s at the University of Frankfurt Institute for Social Research. Following the challenge of positivism, critical theory is a worldview that there is no unitary or objective “truth” about people that can be discovered, as human experience is shaped by social, cultural, and historical contexts. By shedding light on the human experience, it hopes to highlight the role of power, ideology, and social structures in shaping humans, and using this knowledge to create change. 5

Other formalized theories were proposed during the 20th century, such as grounded theory , where researchers started gathering data to form a hypothesis, rather than the other way around. This represented a stark contrast to positivist approaches that had dominated the 17th and 18th centuries.

The 1950s marked a shift toward a more interpretive and descriptive approach which factored in how people make sense of their subjective reality and attach meaning to it. 2 Researchers began to recognize that the why of human behavior was just as important as the what . Max Weber, a German sociologist, laid the foundation of the interpretive approach through the concept of Verstehen (which in English translates to understanding), emphasizing the importance of interpreting the significance people attach to their behavior. 8 With the shift to an interpretive and descriptive approach came the rise of phenomenology, which emphasizes first-person experiences by studying how individuals perceive, experience, and interpret the world around them.

Today, in the age of big data, qualitative research has boomed, as advancements in digital tools allow researchers to gather vast amounts of data (both qualitative and quantitative), helping us better understand complex social phenomena. Social media patterns can be analyzed to understand public sentiment, consumer behavior, and cultural trends to grasp how people attach subjective meaning to their reality. There is even an emerging field of digital ethnography which is entirely focused on how humans interact and communicate in virtual environments!

Thomas Kuhn

American philosopher who suggested that science does not evolve through merely an addition of knowledge by compiling new learnings onto existing theories, but instead undergoes paradigm shifts where new theories and methodologies replace old ones. In this way, Kuhn suggested that science is a reflection of a community at a particular point in time. 9

Paul Felix Lazarsfeld

Often referred to as the father of qualitative research, Austrian-American sociologist and mathematician Paul Lazarsfield helped to develop modern empirical methods of conducting research in the social sciences such as surveys, opinion polling, and panel studies. Lazarsfeld was best known for combining qualitative and quantitative research to explore America's voting habits and behaviors related to mass communication, such as newspapers, magazines, and radios. 10

German sociologist and political economist known for his sociological approach of “Verstehen” which emphasized the need to understand individuals or groups by exploring the meanings that people attach to their decisions. While previously, qualitative researchers in ethnography acted like an outside observer to explain behavior from their point of view, Weber believed that an empathetic understanding of behavior, that explored both intent and context, was crucial to truly understanding behavior. 11

George Herbert Mead

Widely recognized as the father of symbolic interaction theory, Mead was an American philosopher and sociologist who took an interest in how spoken language and symbols contribute to one’s idea of self, and to society at large. 4

Consequences

Humans are incredibly complex beings, whose behaviors cannot always be reduced to mere numbers and statistics. Qualitative research acknowledges this inherent complexity and can be used to better capture the diversity of human and social realities.

Qualitative research is also more flexible—it allows researchers to pivot as they uncover new insights. Instead of approaching the study with predetermined hypotheses, oftentimes, researchers let the data speak for itself and are not limited by a set of predefined questions. It can highlight new areas that a researcher hadn’t even thought of exploring.

By providing a deeper explanation of not only what we do, but why we do it, qualitative research can be used to inform policy-making, educational practices, healthcare approaches, and marketing tactics. For instance, while quantitative research tells us how many people are smokers, qualitative research explores what, exactly, is driving them to smoke in the first place. If the research reveals that it is because they are unaware of the gravity of the consequences, efforts can be made to emphasize the risks, such as by placing warnings on cigarette cartons.

Finally, qualitative research helps to amplify the voices of marginalized or underrepresented groups. Researchers who embrace a true “Verstehen” mentality resist applying their own worldview to the subjects they study, but instead seek to understand the meaning people attach to their own behaviors. In bringing forward other worldviews, qualitative research can help to shift perceptions and increase awareness of social issues. For example, while quantitative research may show that mental health conditions are more prevalent for a certain group, along with the access they have to mental health resources, qualitative research is able to explain the lived experiences of these individuals and uncover what barriers they are facing to getting help. This qualitative approach can support governments and health organizations to better design mental health services tailored to the communities they exist in.

Controversies

Qualitative research aims to understand an individual’s lived experience, which although provides deeper insights, can make it hard to generalize to a larger population. While someone in a focus group could say they pick Doritos over Pringles because they prefer the packaging, it’s difficult for a researcher to know if this is universally applicable, or just one person’s preference. 12 This challenge makes it difficult to replicate qualitative research because it involves context-specific findings and subjective interpretation.

Moreover, there can be bias in sample selection when conducting qualitative research. Individuals who put themselves forward to be part of a focus group or interview may hold strong opinions they want to share, making the insights gathered from their answers not necessarily reflective of the general population. 13 People may also give answers that they think researchers are looking for leading to skewed results, which is a common example of the observer expectancy effect .

However, the bias in this interaction can go both ways. While researchers are encouraged to embrace “Verstehen,” there is a possibility that they project their own views onto their participants. For example, if an American researcher is studying eating habits in China and observes someone burping, they may attribute this behavior to rudeness—when in fact, burping can be a sign that you have enjoyed your meal and it is a compliment to the chef. One way to mitigate this risk is through thick description , noting a great amount of contextual detail in their observations. Another way to minimize the researcher’s bias on their observations is through member checking , returning results to participants to check if they feel they accurately capture their experience.

Another drawback of qualitative research is that it is time-consuming. Focus groups and unstructured interviews take longer and are more difficult to logistically arrange, and the data gathered is harder to analyze as it goes beyond numerical data. While advances in technology alleviate some of these labor-intensive processes, they still require more resources.

Many of these drawbacks can be mitigated through a mixed-method approach, combining both qualitative and quantitative research. Qualitative research can be a good starting point, giving depth and contextual understanding to a behavior, before turning to quantitative data to see if the results are generalizable. Or, the opposite direction can be used—quantitative research can show us the “what,” identifying patterns and correlations, and researchers can then better understand the “why” behind behavior by leveraging qualitative methods. Triangulation —using multiple datasets, methods, or theories—is another way to help researchers avoid bias.

Linking Adult Behaviors to Childhood Experiences

In the mid-1980s, an obesity program at the KP San Diego Department of Preventive Medicine had a high dropout rate. What was interesting is that a majority of the dropouts were successfully losing weight, posing the question of why they were leaving the program in the first place. In this instance, greater investigation was required to understand the why behind their behaviors.

Researchers conducted in-depth interviews with almost 200 dropouts, finding that many of them had experienced childhood abuse that had led to obesity. In this unfortunate scenario, obesity was a consequence of another problem, rather than the root problem itself. This led Dr. Vincent J. Felitti, who was working for the department, to launch the Adverse Childhood Experiences (ACE) Study, aimed at exploring how childhood experiences impact adult health status.

Felitti and the Department of Preventive Medicine studied over 17,000 adults with health plans that revealed a strong relationship between emotional experiences as children and negative health behaviors as adults, such as obesity, smoking, and intravenous drug use. This study demonstrates the importance of qualitative research to uncover correlations that would not be discovered by merely looking at numerical data. 14

Understanding Voter Turnout

Voting is usually considered an important part of political participation in a democracy. However, voter turnout is an issue in many countries, including the US. While quantitative research can tell us how many people vote, it does not provide insights into why people choose to vote or not.

With this in mind, Dawn Merdelin Johnson, a PhD student in philosophy at Walden University, explored how public corruption has impacted voter turnout in Cook County, Illinois. Johnson conducted semi-structured telephone interviews to understand factors that contribute to low voter turnout and the impact of public corruption on voting behaviors. Johnson found that public corruption leads to voters believing public officials prioritize their own well-being over the good of the people, leading to distrust in candidates and the overall political system, and thus making people less likely to vote. Other themes revealed that to increase voter turnout, voting should be more convenient and supply more information about the candidates to help people make more informed decisions.

From these findings, Johnson suggested that the County could experience greater voter turnout through the development of an anti-corruption agency, improved voter registration and maintenance, and enhanced voting accessibility. These initiatives would boost voting engagement and positively impact democratic participation. 15

Increasing HPV Vaccination in Rural Kenya

While HPV vaccines are an effective method of preventing cervical cancer, there is low intake in low and middle-income countries worldwide. Qualitative research can uncover the social and behavioral barriers to increasing HPV vaccination, revealing that misinformation, skepticism, and fear prevent people from getting the vaccine. In this article, our writer Annika Steele explores how qualitative insights can inform a two-part intervention strategy to increase HPV vaccination rates.

Versta Research. (n.d.). Bridging the quantitative-qualitative gap . Versta Research. Retrieved August 17, 2024, from https://verstaresearch.com/newsletters/bridging-the-quantitative-qualitative-gap/
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.
Smith, D. W. (2018). Phenomenology. In E. N. Zalta (Ed.), Stanford Encyclopedia of Philosophy . Retrieved from https://plato.stanford.edu/entries/phenomenology/#HistVariPhen
Nickerson, C. (2023, October 16). Symbolic interaction theory . Simply Psychology. https://www.simplypsychology.org/symbolic-interaction-theory.html
DePoy, E., & Gitlin, L. N. (2016). Introduction to research (5th ed.). Elsevier.
ATLAS.ti. (n.d.). Unstructured interviews . ATLAS.ti. Retrieved August 17, 2024, from https://atlasti.com/research-hub/unstructured-interviews
O'Connor, O. (2020, August 14). The history of qualitative research . Medium. https://oliconner.medium.com/the-history-of-qualitative-research-f6e07c58e439
Sociology Institute. (n.d.). Max Weber: Interpretive sociology & legacy . Sociology Institute. Retrieved August 18, 2024, from https://sociology.institute/introduction-to-sociology/max-weber-interpretive-sociology-legacy
Kuhn, T. S. (2012). The structure of scientific revolutions (4th ed.). University of Chicago Press.
Encyclopaedia Britannica. (n.d.). Paul Felix Lazarsfeld . Encyclopaedia Britannica. Retrieved August 17, 2024, from https://www.britannica.com/biography/Paul-Felix-Lazarsfeld
Nickerson, C. (2019). Verstehen in Sociology: Empathetic Understanding . Simply Psychology. Retrieved August 18, 2024, from: https://www.simplypsychology.org/verstehen.html
Omniconvert. (2021, October 4). Qualitative research: Definition, methodology, limitations, and examples . Omniconvert. https://www.omniconvert.com/blog/qualitative-research-definition-methodology-limitation-examples/
Vaughan, T. (2021, August 5). 10 advantages and disadvantages of qualitative research . Poppulo. https://www.poppulo.com/blog/10-advantages-and-disadvantages-of-qualitative-research
Felitti, V. J. (2002). The relation between adverse childhood experiences and adult health: Turning gold into lead. The Permanente Journal, 6 (1), 44–47. https://www.thepermanentejournal.org/doi/10.7812/TPP/02.994
Johnson, D. M. (2024). Voters' perception of public corruption and low voter turnout: A qualitative case study of Cook County (Doctoral dissertation). Walden University.

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Speaker 1: Validity and reliability are probably among the most confusing and frustrating terms when it comes to qualitative research. There are so many definitions and so many discussions and so many alternative terms have been put forward, so it doesn't really help to understand what validity is and how we can ensure that our findings are valid or how we can increase these findings' validity. So in this video, I'll take you through six steps to increase the validity of your qualitative findings. In quantitative research, validity and reliability are quite straightforward terms. So reliability refers to replicability and consistency of certain measurements and validity to whether this measurement is measuring what it's supposed to measure. So it's quite straightforward. But think about qualitative research. Can we really talk about consistency of our instruments? Imagine that you're interviewing the same person twice and asking the same questions. Even though you're asking the same questions, this person is not likely to give you exactly the same answers. So for this reason, reliability doesn't really refer to qualitative research. It's not that relevant. And usually, people discuss validity rather than reliability of qualitative studies. And validity of qualitative research is usually discussed in terms of three common threads to validity, which are three different types of bias. Respondent bias, researcher bias, and reactivity. So respondent bias refers to a situation where your participants are not giving you honest responses for any reason. They may feel that the topic is threatening to their self-esteem, for example, or they may simply try to please you and give you the answers they think you are looking for. Researcher bias refers to the influence of your previous knowledge and assumptions on your study, which may be a very dangerous and a very risky factor in your study. I've talked about the role of assumptions quite a lot in my other videos and in my blog. And finally, reactivity refers to the role of you as a researcher and your influence, your physical presence in the research situation, and its possible influence on the data, on what the participants say, and so on and so forth. And in order to minimize the potential influence of these three types of bias on your study, Robson suggests the following six strategies to deal with threats to validity. Prolonged involvement refers to you as a researcher being involved in the research situation in your participants' environment, which is likely to result in the increase in the level of trust between you and your participants. This in turn is likely to reduce the risk of respondent bias and reactivity as you generate this common trust. However, it is likely to increase the risk of researcher bias because you and your participants are likely to generate some set of common assumptions. And as I said, assumptions may be a very dangerous thing for your research. Triangulation is such a broad topic and I'm sure that you've at least heard about it before, if not read about it. Triangulation may refer to many things, including triangulation of data, so when you collect different kinds of data, triangulation of methodology, when you have, for example, mixed methods research, or triangulation of theory, where you're comparing what's emerging from your data to previous existing theories. In any case, triangulation is likely to reduce all kinds of threats to validity, so just remember that it's always good to consider triangulating these different aspects of your study. Peer debriefing refers to any input or feedback from other people. This may happen during internal events, such as seminars or workshops in your university, or external, such as conferences. In any case, the feedback and quite likely criticism that you'll receive from other people helps you become more objective and helps you see and become aware of certain limitations of your study. And this is likely to reduce researcher's bias, so again, researcher's bias which was about your previous assumptions and your previous knowledge. So you're becoming more objective and more aware of how your study may be improved. Member checking may mean a couple of things, but in essence it refers to the practice of seeking clarification with your participants. So asking them to clarify certain things before you actually jump into conclusions and describe your interpretation of that data. So it may be simply keeping in touch with your participants, sending them a text message or an email, and asking them whether what you think they meant when they said something in the interview is actually what they meant. Another practice is to send them interview transcripts. So to send them the whole transcript and ask them to delete or change things or add things to that transcript. And finally, you have a method called validation interview, which is all about member checking. So it's basically a whole interview which serves the purpose of this clarification that I discussed. So after you've conducted the first run of analysis after the interview, you conduct another interview and you just ask your participants about your interpretations and about anything that was not clear to you. Negative case analysis is one of my favorite things to do. And I talk extensively about it in my self-study course on how to analyze qualitative data. But basically what it involves is analyzing these cases or data sets that do not match the rest of the data, do not match the trends or patterns that emerge in the rest of the data. And although you may feel tempted to ignore these cases, you may fear that they will ruin your data or your findings, quite often they tell you more about the rest of the data than these actual other cases themselves. So negative cases highlight not just how this one case is different from the rest of the data, but they actually highlight the similarities between the rest of the data. So this is a very, very valuable and important thing to do. And finally, keeping an audit trail means that you keep a record of all the activities involved in your research. So all the audio recordings, your methodological decisions, your researcher diary, your coding book, just having all of this available so you can, for example, demonstrate it to somebody. So again, this way you become really transparent and the validity of your findings cannot really be argued. Importantly, don't worry about having to apply all these strategies in your study. Firstly, some of them are almost natural, like peer debriefing. So as a student, it's very likely that you will receive feedback, you will talk to other people about your study, you will receive feedback and criticism. So you don't really have to worry about consciously applying it as a strategy. And secondly, you can choose some of these strategies, a combination of these strategies. You don't really have to apply every single one on the list. However, it is important to think about validity and it's very important to talk about it in your study. So if you demonstrate that you are thinking about validity and you demonstrate what exactly you did to increase this validity, it will be a major, major advantage to you and to your study.

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Verification Strategies for Qualitative Methods

Related Papers

Deepak P Kafle

In general practice, qualitative research contributes as significantly as quantitative research and both try to find the same result; the truth. Qualitative research, also known as naturalistic inquiry, evolved inside the social and human sciences refers to theories on interpretation and human experience. The use of validity and reliability are common in quantitative research and currently, there are ongoing debates regarding whether the terms are appropriate to evaluate qualitative studies. Although there is no universally typical terminology and standards used to measure qualitative studies, all qualitative researchers comprise strategies to enhance the credibility of a study throughout the research design and implementation. The main aim of this article is to provide the concepts of validity and reliability and to ascertain that it is possible for qualitative research to be properly valid or reliable.

Evidence-based nursing

Helen Noble

Julia Crook

Academia Letters

Anjali Yadav

The idea of reliability in research refers to the repetition or reinforcements of the degree of findings given under the same experiment condition performed by the other researchers and thus leading a wider research community to accept the proposed generalizations. Reliability being the more often quoted part of quantitative research ensures and plays a vital part in guaranteeing the credibility of the qualitative research because not only it tests the integrity of the researcher but also has a wide and direct implication when incorporated in practice. This research paper attempts to highlight the problems associated with the understatement of reliability in qualitative research, its appropriateness, and ways through which it can attain more credible status at par with quantitative research. KEYWORDS: Reliability, Qualitative Research, Construct, Quantitative

Mohammed Ali Bapir

With reference to definitions of validity and reliability, and drawing extensively on conceptualisations of qualitative research, this essay examines the correlation between the reliability of effort to find answers to questions about the social world, and the validity of conclusions drawn from such attempts. This is to point out the fundamental position to the role of theory in relation to research; as an inductivist strategy qualitative research tries to confer the correspondence between reality and representation. The problem of validity and reliability in qualitative research is entwined with the definition of qualitative research and the possibility to mirror this in practice to make a qualitative research properly valid and reliable. That presents both challenges and chances to qualitative researchers; yet, with taking into consideration qualitative criteria in social research, achieving validity and as well as reliability in qualitative research is not impossible.

Wawan Yulianto

Despite a growing interest in quaLitative research in occupationaL therapy, littLe attention has been pLaced on establishing its rigor. This article presents one modeL that can be used for the assessment of trustworthi-ness or merit of qualitative inquiry. Cuba's (1981) modeL describes four generaL criteria for evaLuation of research and then defines each from both a quantitative and a qualitative perspective. SeveraL strategies for the achievement of rigor in qualitative research usefuL for both researchers and consumers of research are described.

The idea of reliability in research refers to the repetition or reinforcements of the degree of findings given under the same experiment condition performed by the other researchers and thus leading a wider research community to accept the proposed generalizations. Reliability being the more often quoted part of quantitative research ensures and plays a vital part in guaranteeing the credibility of the qualitative research because not only it tests the integrity of the researcher but also has a wide and direct implication when incorporated in practice. This research paper attempts to highlight the problems associated with the understatement of reliability in qualitative research, its appropriateness and ways through which it can attain more credible status at par with quantitative research.

Quality & Quantity

Anthony Onwuegbuzie

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Qualitative VS Quantitative Definition – Research Methods and Data

When undertaking any type of research study, the data collected will fall into one of two categories: qualitative or quantitative. But what exactly is the difference between these two data types and research methodologies?

Put simply, quantitative data deals with numbers, objective facts and measurable statistics. For example, quantitative data provides specifics on values like website traffic metrics, sales figures, survey response rates, operational costs, etc.

Qualitative data , on the other hand, reveals deeper insights into people‘s subjective perspectives, experiences, beliefs and behaviors. Instead of numbers, qualitative findings are expressed through detailed observations, interviews, focus groups and more.

Now let‘s explore both types of research to understand how and when to apply these methodologies.

Qualitative Research: An In-Depth Perspective

The purpose of qualitative research is to comprehend human behaviors, opinions, motivations and tendencies through an in-depth exploratory approach. Qualitative studies generally seek to answer "why" and "how" questions to uncover deeper meaning and patterns.

Key Features of Qualitative Research

Exploratory and open-ended data collection
Subjective, experiential and perception-based findings
Textual, audio and visual data representation
Smaller purposeful sample sizes with participants studied in-depth
Findings provide understanding and context around human behaviors

Some examples of popular qualitative methods include:

In-depth interviews – Open discussions exploring perspectives
Focus groups – Facilitated group discussions
Ethnographic research – Observing behaviors in natural environments
Content analysis – Studying documents, images, videos, etc.
Open-ended surveys or questionnaires – Subjective questions

The benefit of these techniques is collecting elaborate and descriptive qualitative data based on personal experiences rather than just objective facts and figures. This reveals not just what research participants are doing but more importantly, why they think, feel and act in certain ways.

For example, an open-ended survey may find that 52% of respondents felt "happy" about using a particular smartphone brand. But in-depth interviews would help uncover exactly why they feel this way by collecting descriptive details on their user experience.

In essence, qualitative techniques like interviews and ethnographic studies add crucial context . This allows us to delve deeper into research problems to gain meaningful insights.

Quantitative Research: A Data-Driven Approach

Unlike qualitative methods, quantitative research relies primarily on the collection and analysis of objective, measurable numerical data. This structured empirical evidence is then manipulated using statistical, graphical and mathematical techniques to derive patterns, trends and conclusions.

Key Aspects of Quantitative Research

Numerical, measurable and quantifiable data
Objective facts and empirical evidence
Statistical, mathematical or computational analysis
Larger randomized sample sizes to generalize findings
Research aims to prove, disprove or lend support to existing theories

Some examples of quantitative methods include:

Closed-ended surveys with numeric rating scales
Multiple choice/dichotomous questionnaires
Counting behaviors, events or attributes as frequencies
Scientific experiments generating stats and figures
Economic and marketing modeling based on historical data

For instance, an online survey may find that 74% of respondents rate a particular laptop 4 or higher on a 5-point scale for quality. Or an experiment might determine that a revised checkout process increases e-commerce conversion rates by 14.5%.

The benefit of quantitative data is that it generates hard numbers and statistics that allow objective measurement and comparison between groups or changes over time. But the limitation is it lacks detailed insights into the subjective reasons and context behind the data.

Qualitative vs. Quantitative: A Comparison

Qualitative	Quantitative
Textual data	Numerical data
In-depth insights	Hard facts/stats
Subjective	Objective
Detailed contexts	Generalizable data
Explores "why/how"	Tests "what/when"
Interviews, focus groups	Surveys, analytics

Is Qualitative or Quantitative Research Better?

Qualitative and quantitative methodologies have differing strengths and limitations. Expert researchers argue both approaches play an invaluable role when combined effectively .

Qualitative research allows rich exploration of perceptions, motivations and ideas through open-ended inquiry. This generates impactful insights but typically with smaller sample sizes focused on depth over breadth.

Quantitative statistically analyzes empirical evidence to uncover patterns and test hypotheses. This lends generalizable support to relationships between variables but risks losing contextual qualitative detail.

In short, qualitative informs the human perspectives while quantitative informs the overarching trends. Together this approaches a problem from both a granular and big-picture level for robust conclusions.

Integrating Mixed Research Methods

Mixing qualitative and quantitative techniques leverages the strengths while minimizing the weaknesses of both approaches. This integration can happen sequentially in phases or concurrently in parallel strands:

Sequential Mixed Methods

Initial exploratory qualitative data collection via interviews, ethnography etc.
Develop hypotheses and theories based on qualitative findings
Follow up with quantitative research to test hypotheses
Interpret how quantitative results explain qualitative discoveries

Concurrent Mixed Methods

Simultaneously collect both qualitative and quantitative data
Merge findings to provide a comprehensive analysis
Compare results between sources to cross-validate conclusions

This intermixing provides corroboration between subjective qualitative themes and hard quantitative figures to produce actionable insights.

Let‘s look at two examples of effective mixed methods research approaches.

Applied Examples of Mixed Methods

Hospital patient experience analysis.

A hospital administrator seeks to improve patient satisfaction rates.

Quantitative Data

Statistical survey ratings for aspects like room cleanliness, wait times, staff courtesy etc.
Rankings benchmarked over time and against other hospitals

Qualitative Data

Patient interviews detailing frustrations, likes/dislikes and emotional journey
Expert focus groups discussing challenges and brainstorming solutions

Combined Analysis

Statistical survey analysis coupled with patient interview narratives provides a robust perspective into precisely which issues most critically impact patient experience and what solutions may have the greatest impact.

Product Development Research

A technology company designs a new smartphone app prototype.

App metric tracking showing feature usage frequencies, conversions, churn rates
In-app surveys measuring ease-of-use ratings on numeric scales
Moderated focus groups discussing reactions to prototype
Diary studies capturing user challenges and delights

Metrics prove what features customers interact with most while qualitative findings explain why they choose to use or abandon certain app functions. This drives effective product refinement.

As demonstrated, thoughtfully blending quantitative and qualitative techniques can provide powerful multifaceted insights.

Tying It All Together: A Nuanced Perspective

Qualitative and quantitative research encompass differing but complementary methodological paradigms for understanding our world through data.

Qualitative research allows inquiry into the depths of human complexities – perceptions, stories, symbols and meanings. Meanwhile, quantitative methods enable us to zoom out and systematically analyze empirical patterns.

Leveraging both modes of discovery provides a nuanced perspective for unlocking insights. As analyst John Tukey noted, "The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data."

Rather than blindly following statistics alone, factoring in qualitative details allows us to carefully interpret the context and meaning behind the numbers.

In closing, elegantly integrating quantitative precision with qualitative awareness offers a multilayered lens for conducting research and driving data-savvy decisions.

Dr. Alex Mitchell is a dedicated coding instructor with a deep passion for teaching and a wealth of experience in computer science education. As a university professor, Dr. Mitchell has played a pivotal role in shaping the coding skills of countless students, helping them navigate the intricate world of programming languages and software development.

Beyond the classroom, Dr. Mitchell is an active contributor to the freeCodeCamp community, where he regularly shares his expertise through tutorials, code examples, and practical insights. His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next.js, and React, which he presents in an accessible and engaging manner.

Dr. Mitchell’s approach to teaching blends academic rigor with real-world applications, ensuring that his students not only understand the theory but also how to apply it effectively. His commitment to education and his ability to simplify complex topics have made him a respected figure in both the university and online learning communities.

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Verification Strategies for Establishing Reliability and Validity in
The purpose of this article is to reestablish reliability and validity as appropriate to qualitative inquiry; to identify the problems created by post hoc assessments of qualitative research; to review general verification strategies in relation to qualitative research, and to discuss the implications of returning the responsibility for the ...
Data Verification
Data verification is the process of checking and confirming that the data entered or stored in a system or database is accurate, complete, and consistent with the source data. The goal of data verification is to ensure that the data being used is reliable and error-free. Data verification is often used in data entry and database management to ...
Verification: Looking Beyond the Data in Qualitative Data Analysis
Verification: Looking Beyond the Data in Qualitative Data Analysis. It is a common misperception among researchers that the analysis of research data is a process that is confined to the data itself. This is probably truer among qualitative researchers than survey researchers given that the latter frequently publish their work in the literature ...
Verification Strategies for Establishing Reliability and Validity in
Verification is the process of checking, confirming, making sure, and being certain. In qualitative research, verification refers to the mechanisms used during the process of research to incrementally contribute to ensuring reliability and validity and, thus, the rigor of a study.
PDF Principles of verification in qualitative research
In. tive research,qualityISreflectedIn narrowness,conCIseness, and objectivity and leads to rigid adherence to research designs and pr. cise statistical analyses (Burns and Grove, 1993). Research quality in qualitative research is associated with openness, thoroughness in collecting data, and. l of the data in the su.
(PDF) Qualitative Data Analysis and Interpretation: Systematic Search
Qualitative data analysis is. concerned with transforming raw data by searching, evaluating, recogni sing, cod ing, mapping, exploring and describing patterns, trends, themes an d categories in ...
Planning Qualitative Research: Design and Decision Making for New
While many books and articles guide various qualitative research methods and analyses, there is currently no concise resource that explains and differentiates among the most common qualitative approaches. We believe novice qualitative researchers, students planning the design of a qualitative study or taking an introductory qualitative research course, and faculty teaching such courses can ...
A Review of the Quality Indicators of Rigor in Qualitative Research
Abstract. Attributes of rigor and quality and suggested best practices for qualitative research design as they relate to the steps of designing, conducting, and reporting qualitative research in health professions educational scholarship are presented. A research question must be clear and focused and supported by a strong conceptual framework ...
Verification strategies for establishing reliability and validity in
Verification is the process of checking, confirming, making sure, and being certain. In qualitative. research, verification refers to the mechani sms used during the process of research to incr ...
Verification Strategies for Establishing Reliability and Validity in
The policy applies to both quantitative and qualitative research (QR) data such as data from interviews or focus groups. QR data are often sensitive and difficult to deidentify, and thus have ...
LibGuides: Section 3: Trustworthiness of Qualitative Data
Confirmability of qualitative data is assured when data are checked and rechecked throughout data collection and analysis to ensure findings would likely be repeatable by others. Confirmability can be documented by a clear coding schema that identifies the codes and patterns identified in analyses. This technique is called an audit trail.
Qualitative Research: Data Collection, Analysis, and Management
Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management.
Validity, reliability, and generalizability in qualitative research
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. ... [25,26] and respondent verification. ... attempted to refute the qualitative data and analytes should be ...
Validation in Qualitative Research: General Aspects and Specificities
Abstract. The criteria for the validation of qualitative research are still open to discussion. This article has two aims: first, to present a summary of concepts, emerging from the field of qualitative research that present answers regarding issues of validation, reliability, and generalization; and second, to propose six concepts that allow the monitoring of the validation of ...
How to Do Qualitative Data Analysis
Data analysis in qualitative research is the process of examining and interpreting non-numerical data to uncover patterns, themes, and insights. It aims to make sense of rich, detailed information gathered through methods like interviews, focus groups, or observations. ... Conclusion drawing/verification: Interpreting the displayed data and ...
Member Check and Respondent Validation in Qualitative Research
Member checking, also called respondent validation, is a qualitative research technique where researchers and study respondents collaborate to ensure data accuracy. This technique is often used to verify qualitative data gathered from interviews, , or focus groups. Essentially, member checks are when a researcher gives data back to respondents ...
Participant Validation: A Strategy to Strengthen the ...
The examples used in the chapter demonstrate how participant validation can contribute to qualitative research by generating new data that can be incorporated into a study. As an integrated part of the research process, participant validation represents a site and an opportunity for values work.
The pillars of trustworthiness in qualitative research
Dear Editor, The global community of medical and nursing researchers has increasingly embraced qualitative research approaches. This surge is seen in their autonomous utilization or incorporation as essential elements within mixed-method research attempts [1].The growing trend is driven by the recognized additional benefits that qualitative approaches provide to the investigation process [2], [3].
Credibility in Qualitative Research: Best Practices and Strategies
Credibility is the first aspect, or criterion, that must be established. It is seen as the most important aspect or criterion in establishing trustworthiness. This is because credibility essentially asks the researcher to clearly link the research study's findings with reality in order to demonstrate the truth of the research study's ...
Qualitative Data Analysis: An Overview of Data Reduction, Data Display
Publication date: November 30th 2020. 1. Introduction. Collecting information, which researchers call data, is only the beginning of the research process. Once collected, the information has to be organized and thought about. Material collected through qualitative methods is invariably unstructured and bulky.
Validity in Qualitative Evaluation: Linking Purposes, Paradigms, and
Peer debriefing is a form of external evaluation of the qualitative research process. Lincoln and Guba (1985, p. 308) describe the role of the peer reviewer as the "devil's advocate.". It is a person who asks difficult questions about the procedures, meanings, interpretations, and conclusions of the investigation.
Data Analysis and Verification of Qualitative Research
The use of qualitative research methodology is well established for data generation within healthcare research generally and clinical pharmacy research specifically. In the past, qualitative research methodology has been criticized for lacking rigour, transparency, justification of data collection and analysis methods being used, and hence the ...
When to Use the 4 Qualitative Data Collection Methods
Qualitative data collection methods are the different ways to gather descriptive, non-numerical data for your research. Popular examples of qualitative data collection methods include surveys, observations, interviews, and focus groups. But it's not enough to know what these methods are. Even more important is knowing when to use them.
Qualitative Research
Quantitative Research: A research method that involves collecting and analyzing numerical data to test hypotheses, identify patterns, and predict outcomes. Exploratory Research: An initial study used to investigate a problem that is not clearly defined, helping to clarify concepts and improve research design. Positivism: A scientific approach that emphasizes empirical evidence and objectivity ...
The pillars of trustworthiness in qualitative research
The trustworthiness of qualitative data has been debatable, yet it has strong support from its supporters. ... A core aspect of the scientific process is the verification of the credibility of ...
6 Strategies to Enhance Validity in Qualitative Research
And usually, people discuss validity rather than reliability of qualitative studies. And validity of qualitative research is usually discussed in terms of three common threads to validity, which are three different types of bias. Respondent bias, researcher bias, and reactivity. ... and its possible influence on the data, on what the ...
What is a codebook?
The purpose of this first blog is to commence with an overview of the defining features of a codebook within qualitative research and analysis. The aim of qualitative research is to gather in-depth data from participants regarding their thoughts, ideas, opinions, and lived experiences in relation to certain issues or social phenomena ...
Verification Strategies for Qualitative Methods
The Nature of Verification in Qualitative Research Verification is the process of checking, confirming, making sure, and being certain. In qualitative research, verification refers to the mechanisms used during the process of research to incrementally contribute to ensuring reliability and validity and, thus, the rigor of a study.
Creating a Codebook
A codebook is typically defined as a guide for coding data on a particular qualitative research project. Yet, it can be so much more: it can be a tool to increase consistency in coding by a team of researchers, or a strategy to showcase rigour and process in a PhD project, or even a developmental tool for learning about coding (Oliveira, 2022). ...
Qualitative VS Quantitative Definition
Qualitative data, on the other hand, reveals deeper insights into people's subjective perspectives, experiences, beliefs and behaviors.Instead of numbers, qualitative findings are expressed through detailed observations, interviews, focus groups and more. Now let's explore both types of research to understand how and when to apply these methodologies.