A beginner’s guide to qualitative analysis in pharmacy practice research

Eyob Alemayehu Gebreyohannes,1,2 Kenneth Lee,2 and Amy T. Page2

  1. Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical & Health Services, University of South Australia
  2. School of Allied Health, University of Western Australia

SHPA proudly supports the Research Toolkit series, which aims which aims to support members in conducting and publishing their research. This series is coordinated by the SHPA Research Leadership Committee, and hence shares the insights and experience of our most research-passionate members. If you’re keen to make a difference to patient care, not just in your daily practice, but in improving practice itself, then this series is for you.

Contents:

Qualitative research is a branch of research that encompasses the collection and analysis of non-numerical data. This type of research focuses on peoples’ experiences.1 There are several research contexts that call for qualitative research. These include exploratory research, when there is limited knowledge about important variables that can explain an outcome. This information can help generate hypotheses that can be confirmed by subsequent studies (e.g., quantitatively). Furthermore, it is used when the goal is to obtain a more detailed understanding of a problem rather than generalising the findings to a larger population. Additionally, qualitative research is valuable for exploring complex and subjective outcomes, such as patient experience and beliefs.2 Qualitative research is an essential component of pharmacy practice research, providing valuable insights into the experiences, perceptions, and behaviours of patients, healthcare professionals, and other stakeholders. It involves developing a research question, designing the research methods, data collection, data analysis, and reporting.3

Conducting qualitative analysis can be challenging for pharmacists with minimal to no prior research experience. This article aims to provide a simple guide on how to approach qualitative data analysis in pharmacy practice research, offering practical tips and considerations to enhance the quality and rigor of your study. Unlike quantitative analysis (aka ‘statistics’), which focuses on numerical data, qualitative analysis involves systematically organising, interpreting, and making sense of participant perspectives, and ideas expressed primarily as text. Common sources of such data include observations, interviews (usually semi-structured), focus groups, or written materials (including government policy documents).3,4

Remember to ensure appropriate ethical and governance requirements are fulfilled prior to commencing your data collection and analysis. Participants will need to give informed consent prior to participation.

Common approaches to qualitative data analysis

There are various ways to analyse qualitative data. However, based on personal experience, for researchers with limited experience in qualitative research, thematic analysis and content analysis are the more user-friendly methods. Content and thematic analyses are typically used to analyse text documents and data collected through interviews and focus groups, respectively. Thematic analysis focuses on the recognition and reporting of patterns and themes as described by study participants.4,5 Content analysis, on the other hand, is interested in the frequency of mentions of core concepts.4 The key steps involved in qualitative data analysis are described below and focus on thematic analysis.

Step 1: Organise and prepare the data for analysis

Data collected through interviews and focus groups needs to be transcribed before it can be analysed.2 Tools such as Microsoft Word (Microsoft Corporation, Redmond, Washington, USA), Zoom (Zoom Video Communications, Inc., San Jose, California, USA), and Otter (https://otter.ai/, [Otter AI, Mountain View, California, USA]) may be used to transcribe the data, but it is always important to manually check the accuracy of the transcription as errors in transcription are common with such tools. It is important to note that the use of such tools may raise issues of confidentiality and privacy regarding the collected data. Therefore, researchers should ensure approval to use these tools from an ethics committee.

Step 2: Familiarise yourself with the data

Before commencing the analysis, it is important to get a general sense of the data. Read and listen to the collected interviews, transcriptions, or textual documents multiple times to gain a comprehensive understanding of the content. It is also good practice to take notes of concepts emerging from the data.2

Step 3: Generate initial codes for the data

Coding is the process of systematically assigning labels or codes to segments of data (e.g., sentences, paragraphs, etc.) to identify meaningful patterns to determine the themes.2,4 Qualitative analysis software tools, such as NVivo (Lumivero, Burlington, Massachusetts, USA), help manage and organise the coding process, and are commonly used.

There are three approaches to qualitative data coding, inductive (bottom up), deductive (top down), and hybrid.5 Inductive coding involves generating codes directly from the data. On the other hand, in deductive coding, pre-determined codes that capture the main themes or concepts relevant to your research objectives are applied as a way of approaching the data analysis.6 The decision to use either inductive or deductive coding depends on the research question and whether our analysis is underpinned by previous literature or theories.7 For example, the question “can older patients take an active role in managing their medications?” may require a deductive approach. On the other hand, a research question that aims to answer, “what are the reasons for medication non-adherence among patients with chronic diseases?” may follow an inductive approach for data coding. However, the same research question may adopt a deductive approach if a particular theoretical framework is used to help explain medication non-adherence. A hybrid approach involves a combination of both inductive and deductive coding. In this approach, new codes are generated from the data in addition to the pre-determined codes.5

Step 4: Categorising and organising codes into themes

Once you have coded your data, categorise and organise the codes into broader themes or categories by identifying areas of similarity and overlap between the codes. Look for connections and relationships between codes, grouping similar codes under overarching themes. This process helps to identify the major concepts and patterns within the data and provides a structure for the subsequent analysis. Each theme needs to be defined and given informative and concise name.4

Step 5: Refining themes

Qualitative data analysis is an iterative process. While some of the initial themes may remain unchanged in the final report, others may undergo modifications. After organising the codes into themes, take a closer look at each theme and explore the subtle differences and variations within the data. It is important to refine the initial themes, as appropriate, by splitting a single theme into two or more themes, collapsing two or more themes into one, or relocating codes into other themes.4 It is possible to have one or more sub-themes within a theme.

Step 6: Synthesising and writing-up findings

Finally, synthesise your findings by summarising the main themes, key insights, and notable patterns that have emerged from the analysis. It may be helpful to consider creating a thematic map, which helps to visualise the connections among the various themes.4 Select appropriate quotes from the transcript as illustrative examples to support your findings. Present your results in a clear and concise manner in the context of the broader literature. Last but not least, interesting findings may emerge from your data that may not necessarily be related to your research aims. Hence, it is always important to check that the findings are in line with the aims of your study.

Strategies to improve rigour in qualitative data analysis

Rigour can be defined as “the quality of being thorough and accurate”.8 There are various strategies to establish rigour in your qualitative research, but the following three strategies provide a good starting point to help you improve the quality of your qualitative data analysis:

  • Analyst triangulation: Multiple coders coding the same data independently and comparing the codes is known as analyst triangulation. It helps you to increase the consistency of the coding process.9,10
  • Audit trail: An audit trail is a detailed and transparent record of the steps taken in conducting qualitative research. It encompasses all stages of qualitative research, not just the data analysis. Audit trail helps you improve the confirmability and trustworthiness of your qualitative research.10,11
  • Reflexive notes: Reflexivity refers to continuous and active involvement of a researcher in the research process where the researcher critically examines their research experience, beliefs, decisions, and interpretations. Providing reflective notes allows readers to evaluate the impact of the researcher’s interests, perspectives, and assumptions on the research findings.1

Finally, it is always beneficial to seek guidance and support from an experienced qualitative researcher, starting from the initial planning stages all the way through to publishing and reporting the findings. Also, the use of a checklist is often recommended when reporting the findings of qualitative research. One commonly used tool that should be considered is the Consolidated criteria for reporting qualitative studies (COREQ), which is a 32-item check list, and should be considered.12

Further reading

  1. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006; 3: 77–101.
  2. Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 2013; 13: 117.

References

  1. Rolfe DE, Ramsden VR, Banner D, Graham ID. Using qualitative Health Research methods to improve patient and public involvement and engagement in research. Res Involv Engagem 2018; 4: 49.
  2. Creswell JW, Creswell JD. Research design: qualitative, quantitative, and mixed methods approaches. 5th edition. Thousand Oaks, CA: SAGE Publications; 2018.
  3. Busetto L, Wick W, Gumbinger C. How to use and assess qualitative research methods. Neurol Res Pract 2020; 2: 14.
  4. Palmer V, Coe A. A best practice guide to qualitative analysis of research to inform healthcare improvement, re-design, implementation and translation. NSW Government; 2020. Available from: https://aci.health.nsw.gov.au/__data/assets/pdf_file/0006/660867/ACI-qualitative-analysis-of-research.pdf.
  5. Proudfoot K. Inductive/deductive hybrid thematic analysis in mixed methods research. J Mix Methods Res 2022; 17: 308–26.
  6. Pope C, Ziebland S, Mays N. Qualitative research in health care: analysing qualitative data. BMJ 2000; 320(7227): 114–6.
  7. Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 2013; 13: 117.
  8. Cypress BS. Rigor or reliability and validity in qualitative research: perspectives, strategies, reconceptualization, and recommendations. Dimens Crit Care Nurs 2017; 36: 253–63.
  9. Castleberry A, Nolen A. Thematic analysis of qualitative research data: is it as easy as it sounds? Curr Pharm Teach Learn 2018; 10: 807–15.
  10. Korstjens I, Moser A. Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. Eur J Gen Pract 2018; 24: 120–4.
  11. Bradshaw C, Atkinson S, Doody O. Employing a qualitative description approach in health care research. Glob Qual Nurs Res 2017; 4: 2333393617742282.
  12. Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care 2007; 19: 349–57.
  13. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006; 3: 77–101.