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Progressive Focusing and Trustworthiness in Qualitative Research

The Enabling Role of Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

• Research Article
• Published: 12 September 2012
• Volume 52 , pages 817–845, ( 2012 )

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• Rudolf R. Sinkovics 1 &
• Eva A. Alfoldi 1  

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The business and management community increasingly recognises that qualitative research is a ‘messy’, non-linear and often unpredictable undertaking. Yet, a considerable proportion of the qualitative research published in top journals is still presented as the result of a linear, predictable research process, thus wrongly suggesting deductive reasoning.

In this paper, we focus on a particular type of ‘messiness’ where during fieldwork, the research context is revealed to be more complex than anticipated, forcing the researcher to gradually refine/shift their focus to reflect ‘what really matters’. We adopt Stake’s notion of progressive focusing for this gradual approach.

Progressive focusing is well-suited to qualitative research in international business requiring complex iteration between theory and data, and the truthful yet coherent presentation of the research process. We propose that this dual challenge of complexity and trustworthiness may be addressed by using computer-assisted qualitative data analysis software (CAQDAS).

We present conceptual considerations and guidelines and offer a view on a ‘messy’, non-linear doctoral research project conducted using a progressive focusing approach, to demonstrate how CAQDAS can help to develop and re-negotiate insights from theory and interview data, as well as enhance trustworthiness, transparency and publication potential.

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The role of analytic direction in qualitative research

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Attempting rigour and replicability in thematic analysis of qualitative research data; a case study of codebook development

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What Should Be Avoided During Qualitative Research?

Although content analysis has been used as a label for a variety of methods and analytical techniques, we regard it as occupying a very specific theoretical space within the more general domain of qualitative research. We view content analysis as ‘a class of methods at the intersection of the qualitative and quantitative traditions’ (Duriau et al. 2007 , p. 5), which places much emphasis on inter-rater reliability (Neuendorf 2002 ; Strijbos et al. 2006 ; see also Welch et al. 2011 ) or the idea that ‘different people should code the same text in the same way’ (Weber 1990 , p. 12). In contrast, the type of qualitative research that we focus on in this paper is more generalised. More specifically, our concept of progressive focusing is perhaps closest to the domain of qualitative research that Welch et al. ( 2011 ) term ‘interpretive sensemaking’. Nonetheless, parts of our discussion may be useful for researchers using other types of qualitative research such as content analysis and grounded theory, or mixed-methods research.

Sinkovics et al. ( 2008 ) point out that reliability and validity have a somewhat uncertain place in the repertoire of a qualitative methodologist (Armstrong et al. 1997 ), as these dimensions are grounded on a different paradigmatic view and therefore not directly applicable to qualitative research. This is why alternative terms and ways of assessing qualitative research have been proposed, such as credibility, transferability, dependability and confirmability (Denzin and Lincoln 1994 ; Guba and Lincoln 1989 ; Kirk and Miller 1986 ; LeCompte and Goetz 1982 ).

For similar concepts, see also cycles of deliberation (McGaughey 2004 , 2007 ), systematic combining/abductive approach (Dubois and Gadde 2002 ), zipping (Orton 1997 ) and evolution of perspective (Peshkin 1985 ).

A preferable method would be to import the entire document where possible, which allows the coding of content as well as the recording of key attributes. Whilst this can easily be done in the case of Word files, the majority of journal articles are accessed online as PDF documents, which in our experience often poses practical problems. In principle, newer versions of NVivo (8 and 9) can handle PDF files, however, many PDF documents (especially older ones) tend to be very large files or lack text recognition, and as yet, NVivo does not appear to have sufficient processing power to manage these efficiently. Nonetheless, given that many PDF texts can already be highlighted and annotated in freely available software such as Adobe Reader X, we believe that this limitation is likely to diminish in the future as more powerful versions of NVivo are developed.

In particular, the development of visual models based on coding templates is facilitated by the modelling function in software such as NVivo: the researcher can work on a dynamic version of their model in a continuous manner, whilst also saving static versions of the model at different points in time, thus tracking the evolution of the research model.

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Acknowledgements

We are grateful to Gillian Symon and Catherine Cassell for their helpful comments and constructive suggestions on earlier drafts. We also acknowledge insightful comments received from anonymous conference reviewers and in research presentations, especially Sara McGaughey, Rebecca Piekkari and Catherine Welch, whose suggestions helped in fleshing out the International Business perspective. We also appreciate the constructive comments from two anonymous MIR reviewers and conversations with Brandon Charleston, who triggered the idea of using NVivo for literature reviews.

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Sinkovics, R.R., Alfoldi, E.A. Progressive Focusing and Trustworthiness in Qualitative Research. Manag Int Rev 52 , 817–845 (2012). https://doi.org/10.1007/s11575-012-0140-5

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Received : 03 November 2010

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Accepted : 15 August 2011

Published : 12 September 2012

Issue Date : December 2012

DOI : https://doi.org/10.1007/s11575-012-0140-5

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How to Achieve Trustworthiness in Qualitative Research

Qualitative research is, by nature, more directional than quantitative research. There is a misguided assumption that qualitative data is somehow inferior, or at least more questionable, than quantitative data derived from market research. It all comes down to rigor in qualitative research, and whether your study meets certain criteria for credibility, dependability, transferability and confirmability.

Qualitative research is, by nature, more directional than quantitative research. Rather than producing facts and figures—like the hard lines of a drawing—it gives color to your customers’ experiences and provides context by exploring the how and why behind concepts or theories in question.

As a result, there is a misguided assumption that qualitative data is somehow inferior, or at least more questionable, than the quality of quantitative data derived from market research.

However, that’s not the case. Qualitative research plays an important role in understanding consumer attitudes and behaviors, measuring brand perceptions, finessing product development efforts, and achieving other goals as you strive to keep up with shifting demands from customers, new competitors and new technologies.

It all comes down to rigor in qualitative research , and whether your study meets certain criteria for credibility, dependability, transferability and confirmability.

How is Qualitative Research Trustworthiness Measured?

Led by Qualitative Research Director, Dawn McElfresh , The Farnsworth Group has been utilizing qualitative research for over 30 years to help clients make more informed strategic decisions.

When it comes to quantitative research, trustworthiness is measured in terms of validity and reliability. You can achieve trustworthiness in qualitative market research by demonstrating that your findings are dependable, credible, confirmable and transferable. Here are how those four concepts are to be understood:

1. Dependability in Qualitative Research

Dependability is used to measure or demonstrate the consistency and reliability of your study’s results. This starts by tracking the precise methods you use for data collection, analysis and interpretation and providing adequate contextual information about each piece, so that the study could theoretically be replicated by other researchers and generate consistent results. An inquiry audit—performed by an outside person—is one tool used to examine the dependability of a qualitative study. Alternatively, screening parameters can be used to solidify research dependability.

The Farnsworth Group demonstrates the dependability of research we conduct by using specific recruitment screener parameters that allow representation from a broad range of demographics, trade criteria, experience, geographic location and so on—so the insights represent the landscape desired by our clients plus aren't representing a slanted viewpoint from one concentrated demographic.

2. Credibility in Qualitative Research

Credibility is a measure of the truth value of qualitative research, or whether the study’s findings are correct and accurate. To some degree, it relies on the credibility of the researchers themselves, as well as their research methods. Triangulation, prolonged engagement with data, persistent observation, negative case analysis, member checks, and referential adequacy are all procedures that can be used to increase the credibility of qualitative studies. 

The Farnsworth Group demonstrates the measure of truth in research we conduct by conducting senior management analysis on the data collected during the in-depth interviews or focus groups. This involves summarizing each detail and finding the overlapping themes that are consistent—which drive the key insights found in the study.  

Our team’s unique combination of decades of industry experience within manufacturing and product development allow us to formulate strategic recommendations; these insights cannot be replicated by other research firms for this reason.

3. Confirmability in Qualitative Research

In terms of confirmability, you want to prove that your qualitative research is neutral and not influenced by the assumptions or biases of the researchers. Rather, trustworthy research should produce findings that objectively reflect information collected from participants. In other words, your data should speak for itself. Confirmability is often demonstrated by providing an audit trail that details each step of data analysis and shows that your findings aren’t colored by conscious or unconscious bias but accurately portray the participants’ responses. 

Confirmability of research conducted by The Farnsworth Group is achieved by our approach of summarizing the content of each question we ask during the in-depth interview or focus group.  This showcases the overlapping themes, without bias, plus all comments heard since they all can provide value to our clients. Qualitative reporting is about the details, and we provide all the color from the interview or focus group, so that the client can view everything without researcher bias.

4. Transferability in Qualitative Research

As the name implies, transferability measures whether, or to what extent, the study’s results are applicable within other contexts, circumstances and settings. It also can be thought of in terms of generalizability. In order to demonstrate transferability in qualitative research, you can utilize thick description, which involves providing adequate details on the site, participants and methods or procedures used to collect data during your study. 

This helps other researchers evaluate whether the results are applicable for other situations. While transferability cannot be proved with 100 percent certainty, you can demonstrate that it is highly likely in order to back up the trustworthiness of your qualitative market research.

What is Thematic Analysis in Qualitative Research?

Not only must the standard trustworthiness of the data collection be high, but the standard of trustworthiness of the interpretation of results must also remain high. Thematic analysis is a foundational and widely used qualitative research method .

Thematic analysis is a flexible and accessible approach to evaluating qualitative data—such as interview transcripts, field notes or other texts—that emphasizes identifying, analyzing and interpreting patterns, meanings and themes. The challenge is that there can be different ways to interpret data, and the researcher is often relied upon to make judgements and take action when it comes to theming, coding and contextualizing the data.

Here is a brief overview of how you can establish trustworthiness within each phase of the thematic analysis process:

1. Familiarize Yourself with the Data

The first step is to establish prolonged engagement with the data and triangulate different data collection modes. Document both reflective thoughts and thoughts about potential themes in the data. Keep records of all raw data and store it in organized archives.

2. Generate Initial Codes or Tally’s

Through peer debriefing, reflexive journaling and researcher triangulation, you have the option to generate initial codes using a reliable coding framework. Alternatively, you can use response tally’s to refer to when conducting qualitative theme analysis.

In any case, be diligent to leave behind an audit trail of this work, which means having documentation of all debriefings and meetings used in the generation process. 

3. Search for Themes and Patterns

During this phase, you will once again utilize triangulation to establish trustworthiness. You can also employ diagramming to track patterns and themes in the data. Maintain detailed notes about the development of certain concepts and themes.

4. Review Your Themes

Team members can help vet themes and subthemes during this phase. You also can return to the raw data to test for referential adequacy.

5. Define and Name Themes

At this point of the process, peer debriefing and researcher triangulation are tools used to establish trustworthiness in qualitative research. Maintaining rigorous documentation is also a key component.

6. Produce the Report

For this phase, you’ll want to provide thick descriptions of the context of your study and details on the process of coding and analyzing the data. This should include justifications for all analytical and methodological choices made throughout the entire study. Here is where you’ll also conduct member checks.

Establishing Trustworthiness in Qualitative Market Research

Qualitative studies are important within the realm of market research. However, in order for the results to be useful and meaningful, you have to take a rigorous and methodical approach to the collection of qualitative data and interpretation of its themes. This is crucial to ensuring that your findings are trustworthy and reliable. 

The Farnsworth Group has the right experience and tools to help you conduct qualitative market research for the building and construction, home improvement, or lawn and ranch industries. You end up with data-driven insights supported by industry expertise that provide you with actionable recommendations.

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Trustworthiness of Qualitative Data

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As noted in the dissertation template for qualitative studies, the section directly following the Chapter 4 introduction is to be labeled Trustworthiness of the Data, and in this section, qualitative researchers are required to articulate evidence of four primary criteria to ensure trustworthiness of the final study data set:

Credibility (e.g., triangulation, member checks)

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 (e.g., the extent to which the findings are generalizable to other situations)

Generalizability is not expected in qualitative research, so transferability of qualitative data assures the study findings are applicable to similar settings or individuals. Transferability can be demonstrated by clear assumptions and contextual inferences of the research setting and participants.

Dependability (e.g., an in-depth description of the methodology and design to allow the study to be repeated)

Dependability of the qualitative data is demonstrated through assurances that the findings were established despite any changes within the research setting or participants during data collection. Again, rigorous data collection techniques and procedures can assure dependability of the final data set.

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

Confirmability of qualitative data is assured when data are checked and rechecked throughout data collection and analysis to ensure results would likely be repeatable by others. This can be documented by a clear coding schema that identifies the codes and patterns identified in analyses. Finally, a data audit prior to analysis can also ensure dependability.

For more information on these criteria, visit the Sage Research Methods database in the NU Library: https://resources.nu.edu/sagerm

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Trustworthiness of Research

Prof. Dr. Eva Stumpfegger November 7, 2017

Trustworthiness is the highest premise of good research. But when is a paper considered trustworthy? Quantitative and qualitative research have different criteria for trustworthiness – we explain them to you .

„A man is more likely to believe something if he would like it to be true.“

Francis Bacon

Quantitative and qualitative research is based on different research paradigms that reflect the researcher’s scientific worldview. Due to this, methodology, methods and results naturally differ accordingly (Goertz & Mahoney, 2012). In order to appreciate the quality of academic research, it makes sense to apply different criteria for (1) quantitative and (2) qualitative methods.

1. Trustworthiness of quantitative research

Bryman and Bell (2005, p. 154) define quantitative research as „entailing the collection of numerical data and exhibiting the view of relationship between theory and research as deductive, a predilection for natural science approach, and as having an objectivist conception of social reality“. Data is usually generated by means of surveys and experiments and analyzed through statistical tests such as ANOVA (e.g. Black, 1999) and Cohen’s Kappa (Cohen, 1980).

Therefore, its quality can be assessed by its (a) internal and (b) external validity, (c) reliability and (d) objectivity (Lincoln & Guba, 1985), which are described below:

a. Internal validity

A study is internally valid if it is able to determine whether a causal relationship exists between one or more independent variables and one or more dependent variables (Heffner, 2017), i.e. it is explanative. A study is internally valid if there are as little confounding variables as possible. Confounding variables are variables that the researcher fails to control or eliminate, allowing the results to show false correlation (Shuttleworth, 2008).

Therefore, on the one hand, internal validity refers to how well the study is run, e.g. research design, operational definitions, how variables are measured, and what is (not) measured (Huitt, Hummel & Kaeck, 1999). On the other hand, internal validity determines how confidently it can be concluded that the change in the dependent variable was produced solely by the independent variable as opposed to extraneous ones (ibid.).

Campbell and Stanley‘s (1966) seminal work on experimental and quasi-experimental designs identifies and describes eight threats to internal validity:

• History – Studies that collect data over long time periods are likely to be affected by research subjects’ unique experiences over time that function like extra and unplanned, therefore independent variables.
• Maturation – Like the above, this effect also draws on the normal passage of time. Research subjects may become more or less motivated and thus affecting the internal validity of the study.
• Testing – Research design often use pre-tests. These may change, i.e. contaminate the outcome of the actual study.
• Instrumentation – Changing measurement methods or their administration may affect what is measured.
• Statistical Regression – Re-testing research subjects that were originally chosen because of extremely high or low scores can be expected to produce a distribution closer to the entire population’s distribution.
• Selection – The results of the study will be biased if the research subjects in the control and experimental groups are different from another at the beginning of the study.
• Experimental Mortality – If the comparison groups experience different or high levels of withdrawal (mortality) of research subjects, it becomes questionable whether the observed differences between the groups are due to different or high drop-out rates or are produced by the independent variable.
• Selection Interactions – If the selection method of research subjects interacts with one or more of the above threats, the study’s results will be biased.

b. External validity

External validity describes the ability to generalize a study, which is particularly threatened if people, places, or times are poorly chosen (Trochim, 2006).

As measuring an entire population is impossible, a sample, or a subset of the population is studied. The sample chosen needs to represent the whole population in order to allow inferences to be drawn (Landreneau, 2009).

A good sampling model firstly identifies the population that it should generalize, subsequently drawing a sample from that population, conducting research and finally generalizing the results back to the original population (Trochim, 2006). External validity will improve the more a study is replicated (ibid.).

The sufficient sample size depends on the minimum number of participants required to identify a statistically significant difference and increases the smaller the anticipated effect is (Burmeister & Aitken, 2012).

As quantitative methods are often used in natural science, the very fine tools and criteria developed for that realm may be adapted and used for business research.

As an example, a set of eight criteria was developed to identify high quality evidence in the public health sector (Effective Public Health Practice Project, 1998). Each of the criteria (selection bias, study design, confounders, blinding, data collection methods, withdrawals and dropouts, intervention integrity, analysis) is rated as strong, moderate, or weak, thus achieving an overall methodological rating (ibid.).

c. Reliability

In research, reliability means “repeatability” or “consistency”; it is achieved if a measure will always provide the same result (Trochim, 2006). Reliability issues often come up when researchers adopt a subjective approach to research (Wilson, 2010), which on the other hand is consciously allowed in qualitative research.

d. Objectivity

Objectivity demands that „researchers should remain distanced from what they study so findings depend on the nature of what was studied rather than on the personality, beliefs, and values of the researcher“ (Payne & Payne, 2004).

The four criteria for trustworthiness of quantitative research overlap at some points, for example validity and reliability: if a study’s results can be generalized, its repetition should offer the same results.

2. Trustworthiness of qualitative research

The above described criteria applying for quantitative research are not suitable for qualitative research as it accepts multiple and subjective realities and aims at deep insights. Therefore, these criteria are not sought to be complied with. In order to provide a different set for criteria that can be used for ascertaining the quality, Lincoln & Guba (1985) created a corresponding set of criteria for trustworthiness of qualitative research: (a) credibility (vs. internal validity), (b) transferability (vs. external validity), (c) dependability (vs. reliability) and (d) confirmability (vs. objectivity).

a. Credibility

Credibility depends on the richness of the data and analysis and can be enhanced by triangulation (Patton, 2002), rather than relying on sample size aiming at representing a population.

There are four types of triangulation as introduced by Denzin (1970), which can also be used in conjunction with each other:

• Data triangulation – using different sources of data, e.g. from existing research
• Methodological triangulation – using more than one method, e.g. mixed methods approach, however with focus on qualitative methods
• Investigator triangulation – using more than one researcher adds to the credibility of a study in order to mitigate the researcher’s influence
• Theoretical triangulation – using more than one theory as conceptual framework

b. Transferability

Transferability corresponds to external validity, i.e. generalizing a study’s results. Transferability can be achieved by thorough description of the research context and underlying assumptions (Trochim, 2006). With providing that information, the research results may be transferred from the original research situation to a similar situation.

c. Dependability

Dependability aims to replace reliability, which requires that when replicating experiments, the same results should be achieved. As this would not be expected to happen in a qualitative setting, alternative criteria are general understandability, flow of arguments, and logic. Both the process and the product of the research need to be consistent (Lincoln & Guba, 1985).

d. Confirmability

Instead of general objectivity in quantitative research, the researcher’s neutrality of research interpretations is required. This can be achieved by means of a confirmability audit that includes an audit trail of raw data, analysis notes, reconstruction, and synthesis products, process notes, personal notes, as well as preliminary developmental information (Lincoln & Guba, 1985).

The approach to sampling differs significantly in quantitative and qualitative research. Qualitative samples are usually small and should be selected purposefully in order to select information-rich cases for in-depth study (Patton, 2002). There may be as few as five (Creswell, 1998, p. 64) or six participants (Morse, 1994, p. 225).

As seen from the above criteria, qualitative research requires far more documentation than quantitative research in order to establish trustworthiness in. Quantitative research, on the other hand, requires more effort during the research design phase.

With qualitative and quantitative research serving different objectives and being designed in a different way, quality assessment criteria must be adapted and adhered to accordingly.

Literature :

• Bacon, F. (1620). The New Organon: or True Directions Concerning the Interpretation of Nature. Retrieved from: http://www.constitution.org/bacon/nov_org.htm
• Black, T. (1999). Doing Quantitative Research in the Social Sciences – An Integrated Approach to Research Design, Measurement and Statistics. London, Sage.
• Burmeister, E. & Aitken, L. M. (2012). Sample size: How many is enough? Australian Critical Care, 25(4), pp. 271-274. doi: 10.1016/j.aucc.2012.07.002
• Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Chicago: Rand McNally.
• Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage.
• Cohen, J.: Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. In: Psychological Bulletin. 1968, 213–220.
• Goertz, G. & Mahoney, J. (2012). A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton, NJ: Princeton University Press.
• Lincoln, Y. S. & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.
• Effective Public Health Practice Project (1998). Quality Assessment Tool For Quantitative Studies. Hamilton, O.N.: Effective Public Health Practice Project. Available from: http://www.ephpp.ca/index.html
• Heffner, C. (2017): Research Methods. Retrieved from: https://allpsych.com/researchmethods/experimentalvalidity/
• Huitt, W., Hummel, J. & Kaeck, D. (1999). Internal and External Validity. Retrieved from: http://www.edpsycinteractive.org/topics/intro/valdgn.html
• Landreneau, K. J. (2009). Sampling Strategies. Retrieved from: http://www.natco1.org/research/files/samplingstrategies.pdf
• Morse, J. M. (1994). Designing funded qualitative research. In N. K. Denzin & Y. S. Lincoln, Handbook of qualitative research (pp. 220-235). Thousand Oaks, CA: Sage.
• Payne, G. & Payne, J. (2004). Key Concepts in Social Research. SAGE Key Concepts.
• Patton, M. Q. (2002). Qualitative evaluation and Research Methods 3rd ed. Newbury Park: Sage.
• Shuttleworth, M. (2008): Research Methodology. Retrieved from: https://explorable.com/confounding-variables
• Trochim, W. M. (2006). Social Research Methods. Retrieved from Research Methods Knowledge Base: https://www.socialresearchmethods.net/
• Wilson, J. (2010). Essentials of Business Research: A Guide to Doing Your Research Project. London: Sage.
• Qualitative Research
• Quantitative Research
• Research Design
• Research Topics
• Trustworthiness

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