Qualitative and Mixed-Methods Research

Introduction and context

Accounting and finance research is not limited to numerical data. Financial statements, audit opinions, ratios, market prices and survey responses can reveal what has happened and how frequently it occurs, but they may not fully explain why people behave in particular ways, how organisational practices develop, or how accounting information is interpreted in context. Qualitative research addresses these questions by studying meanings, experiences, practices, relationships and institutional settings.

For example, a quantitative study may show that small businesses adopt accounting software at a low rate. Interviews may reveal that owners regard the software as expensive, distrust external providers, lack confidence in interpreting reports, or rely on informal records because these fit their decision-making routines. The qualitative evidence explains the mechanisms behind the numerical pattern.

Mixed-methods research combines qualitative and quantitative approaches within a single project. It can be used to develop a questionnaire, explain statistical results, assess the implementation of a financial intervention, or produce a more complete account of a complex accounting or finance problem. The purpose is not simply to collect two types of data, but to integrate them so that the combined analysis answers the research question more effectively than either approach alone.

This chapter focuses on interviews, focus groups, observation, document analysis and case studies; credible coding and thematic analysis; and the integration of qualitative and quantitative evidence.

Learning objectives

By the end of this chapter, students should be able to:

1. Explain the philosophical and methodological foundations of qualitative and mixed-methods research.
2. Select an appropriate qualitative method for an accounting or finance research question.
3. Design and conduct interviews, focus groups, observations and document analysis ethically.
4. Construct a defensible case study design.
5. Code qualitative data and develop credible themes.
6. Evaluate qualitative quality using appropriate criteria.
7. Distinguish major mixed-methods designs and integrate evidence systematically.
8. Recognise ethical, practical and technological challenges in qualitative research.

1. Foundations of qualitative inquiry

1.1 What qualitative research examines

Qualitative research investigates how participants understand and experience social phenomena. Its data may include spoken accounts, written documents, field notes, images, emails, policies, meeting records and digital communications. The researcher is concerned with context and interpretation rather than only measurement.

Typical qualitative questions include:

• How do finance managers interpret climate-risk reporting requirements?
• Why do employees resist a new budgeting system?
• How do auditors negotiate disagreements with client management?
• How do small-business owners use informal financial information?
• How is professional identity constructed in an accounting firm?

These questions commonly begin with “how,” “why,” or “in what ways.” They are usually unsuitable for a purely experimental design because the relevant meanings and processes may not be known in advance.

1.2 Philosophical positions

A paradigm is a broad set of assumptions about reality, knowledge and appropriate research methods.

Positivism generally treats social phenomena as capable of objective observation and measurement. It is commonly associated with hypothesis testing, structured instruments and quantitative analysis.

Interpretivism holds that social reality is understood through the meanings people give to their actions and circumstances. Reality may be experienced differently by different groups. Interviews, observation and document analysis are therefore valuable because they reveal participants’ interpretations.

Critical approaches examine power, inequality, ideology and whose interests are represented in accounting and financial systems. A critical study might investigate how performance metrics disadvantage particular groups or how regulatory language privileges investor interests.

Pragmatism selects methods according to the research problem rather than loyalty to one philosophical tradition. It provides a particularly useful foundation for mixed-methods research, where qualitative and quantitative evidence are combined to support practical inquiry.

A student should not claim that qualitative research is automatically subjective or that quantitative research is automatically objective. Both involve decisions about concepts, measurement, sampling, interpretation and reporting. The standard is transparent and justified inquiry.

1.3 Inductive, deductive and abductive reasoning

In deductive reasoning, concepts or propositions are developed before data collection and examined using evidence. In inductive reasoning, concepts and explanations emerge from close engagement with data. Abductive reasoning moves iteratively between data and existing theory to identify the most plausible explanation.

Most high-quality qualitative studies combine these forms. A researcher may begin with sensitising concepts from the literature, remain open to unexpected findings, and revise the conceptual framework after comparing cases and participants.

2. Designing a qualitative study

2.1 Aligning question, method and evidence

Methodological alignment means that the research question, data source, sampling strategy, analysis and claims fit together. The following examples illustrate appropriate alignment.

A question about prevalence—such as the proportion of firms using a particular accounting standard—requires quantitative evidence. A question about how firms understand and implement that standard may require qualitative evidence. Some projects require both.

2.2 Sampling

Qualitative sampling is usually purposive rather than statistically representative. Participants are selected because they have relevant knowledge or experience. Common strategies include:

• Criterion sampling: selecting all participants who meet defined conditions.
• Maximum-variation sampling: seeking diverse cases to examine different perspectives.
• Typical-case sampling: selecting an ordinary or broadly representative case for its context.
• Critical-case sampling: selecting a case whose circumstances are especially informative.
• Snowball sampling: asking participants to recommend other eligible participants.
• Theoretical sampling: selecting further participants or cases as concepts develop during analysis.

Sample adequacy depends on the research purpose, richness of data, heterogeneity of the population and analytical strategy. The concept of information power is useful: a focused question, specific sample, strong dialogue and relevant theory may require fewer participants than a broad question involving diverse groups. Researchers should avoid treating a fixed number as a universal rule.

2.3 Access and the researcher’s role

Access may depend on professional networks, gatekeepers, organisational permission and participant trust. A gatekeeper can facilitate entry but may also influence who is available or willing to participate. The researcher should record access decisions and consider whether employees feel pressured to participate.

Reflexivity is the systematic examination of how the researcher’s identity, assumptions, professional position and relationship with participants affect the research. A reflexive journal can record decisions, emotional reactions, unexpected events and possible biases. Reflexivity does not eliminate interpretation; it makes interpretation more transparent.

3. Qualitative data-collection methods

3.1 Semi-structured interviews

A semi-structured interview uses a prepared guide while allowing follow-up questions. It balances consistency across participants with flexibility to explore important issues.

A good interview guide normally contains:

1. An opening explanation, consent procedure and confidentiality statement.
2. Warm-up questions about role and experience.
3. Core questions linked to the research objectives.
4. Probes such as “Can you give an example?” or “What happened next?”
5. Closing questions inviting additional information.

Questions should be open, neutral and understandable. “How did the new reporting system affect your work?” is generally better than “Did the inefficient new system create problems?” The latter contains an assumption and may lead the participant.

Interviews may be face-to-face, by telephone or online. With permission, audio recording improves accuracy, but the researcher should also make brief field notes about context and non-verbal observations. Transcripts should identify speakers consistently and remove unnecessary personal identifiers.

3.2 Focus groups

A focus group is a moderated discussion with several participants. Its distinctive value lies in interaction: participants may challenge, support or stimulate one another. This can reveal shared norms and disagreements that individual interviews might not produce.

Focus groups are useful for exploring views about budgeting practices, financial literacy materials or professional training. They are less suitable where participants fear disclosure, where there are strong power differences, or where the subject involves confidential performance information.

The moderator should establish ground rules, invite quieter participants, prevent domination by one person and avoid expressing approval or disapproval. Group confidentiality can be requested but cannot be guaranteed fully because participants hear one another’s contributions.

3.3 Observation

Observation studies behaviour and practices in their setting. It may be:

• Participant or non-participant, depending on the researcher’s involvement;
• Structured or unstructured, depending on the degree of predefinition; and
• Overt or covert, depending on whether participants know they are being observed.

Overt observation is normally preferable in university research because informed consent and respect for privacy are central ethical requirements. An observation schedule may record who acts, what occurs, when and where it occurs, how participants respond, and what materials or technologies are involved.

Field notes should distinguish description from interpretation. For example, “The finance manager closed the spreadsheet after the auditor entered the room” is descriptive; “The manager felt threatened” is an interpretation requiring further evidence.

3.4 Document analysis

Documents are not neutral containers of facts. They are produced for particular audiences and purposes. A management report may present a favourable account, while internal minutes may record disagreement more directly.

Relevant documents can include annual reports, audit reports, accounting policies, board minutes, regulatory consultations, budgets, emails, training manuals, websites and social-media posts. Analysis should consider authorship, date, audience, purpose, missing information, language and institutional context.

A document-analysis matrix can include document identifier, producer, intended audience, purpose, key claims, accounting concepts, evidence of power or conflict, and relevance to the research question. Researchers must verify authenticity and avoid presenting confidential documents without authorisation.

3.5 Case studies

A case study is an in-depth investigation of a bounded case in its real-life context. The case may be a company, accounting department, project, regulatory event, implementation process or financial crisis. A case is not simply an example selected for convenience; its boundaries and significance must be explained.

A strong case study specifies:

• The case and its spatial, organisational or temporal boundaries;
• The central phenomenon;
• The theoretical or practical reason for selection;
• The units of analysis;
• Multiple sources of evidence where feasible;
• A chain of evidence connecting data to conclusions; and
• Rival explanations or negative evidence.

Case studies may be single or multiple. A single case permits depth, while multiple cases enable comparison and replication of reasoning. The aim is usually analytical generalisation—relating findings to concepts or theory—not statistical generalisation to every organisation.

4. Coding and thematic analysis

4.1 From raw data to themes

Coding is the process of assigning concise labels to meaningful segments of data. A code may describe content, action, emotion, belief or process. Themes are broader patterns of meaning that answer the research question and are supported by coherent evidence.

A useful thematic-analysis process is iterative:

1. Familiarisation: read transcripts and documents repeatedly and record initial observations.
2. Initial coding: label relevant segments, retaining participants’ language where useful.
3. Code development: compare codes, merge overlaps and define boundaries.
4. Theme construction: group related codes into candidate themes.
5. Theme review: test whether each theme is internally coherent and distinct from others.
6. Theme definition: state the central organising concept and scope of each theme.
7. Writing: connect themes to the research question, literature and evidence.

A codebook improves consistency. It may include the code name, definition, inclusion criteria, exclusion criteria, example quotation and related codes.

4.2 Descriptive and interpretive coding

Descriptive codes summarise what is explicitly stated, such as “training shortage” or “late data.” Interpretive codes examine underlying meaning, such as “accountability anxiety” or “strategic compliance.” Both may be appropriate, but interpretive claims require careful support and should not be presented as participants’ direct words unless they said them.

Researchers should actively seek negative cases—evidence that does not fit an emerging interpretation. A theme becomes more credible when the researcher explains variation rather than silently excluding inconvenient data.

4.3 Thematic analysis and other approaches

Thematic analysis is flexible and suitable for many undergraduate projects. Other approaches may be more appropriate for particular questions:

• Grounded theory aims to develop an explanatory theory through systematic comparison and theoretical sampling.
• Content analysis may classify manifest or latent content, sometimes using frequencies alongside interpretation.
• Narrative analysis examines how people construct stories, identities and temporal sequences.
• Discourse analysis studies how language constructs social realities, categories and power relations.
• Phenomenology focuses on the meaning and structure of lived experience.

The researcher should name the approach used and explain its analytical logic rather than claiming to have used every approach simultaneously.

4.4 Credibility and quality

Qualitative quality is multidimensional. Common criteria include:

• Credibility: findings are plausible and well supported by the data.
• Dependability: procedures are documented and logically consistent.
• Confirmability: interpretations can be traced to evidence rather than unsupported personal preference.
• Transferability: sufficient contextual detail enables readers to judge relevance elsewhere.

Practical techniques include triangulation across sources or methods, participant feedback on factual accuracy, peer debriefing, negative-case analysis, a clear audit trail, reflexive journaling and thick description. No technique guarantees truth. Participant checking, for example, may correct factual errors but participants may disagree with an interpretation or later revise their views.

5. Mixed-methods research

5.1 Rationale

Mixed methods is justified when qualitative and quantitative evidence perform complementary roles. The common rationales are:

• Triangulation: compare evidence to assess convergence or explain divergence.
• Complementarity: use one method to elaborate or clarify the other.
• Development: use findings from one phase to design the next instrument or sample.
• Expansion: examine different dimensions of a broad problem.
• Initiation: use contrasting findings to generate a new perspective.

Mixed methods should not be used merely to make a dissertation appear more sophisticated. It increases workload and requires a clear integration plan.

5.2 Major designs

Convergent design collects qualitative and quantitative data during a broadly similar period, analyses them separately, and then compares or integrates the findings.

Explanatory sequential design begins with quantitative analysis and follows with qualitative research to explain surprising, important or representative results. For example, a survey may identify unexpectedly low trust in financial advice, followed by interviews with selected respondents.

Exploratory sequential design begins qualitatively and uses the findings to develop a survey, measurement instrument or intervention. Interviews with small-business owners might generate items for a financial-management questionnaire.

Embedded design places one method within a larger design. For example, interviews may be embedded in a quantitative evaluation of a budgeting intervention.

Multiphase design uses several linked qualitative and quantitative studies over an extended programme. This is usually more demanding than a bachelor’s dissertation.

5.3 Integration

Integration occurs when the researcher connects, builds, merges or embeds evidence. It should be visible in the research design, sampling, analysis and conclusions.

A joint display is a practical integration tool:

Researchers should report whether findings converge, complement, contradict or remain unrelated. Contradiction is not necessarily a failure. Different methods may measure different aspects of a phenomenon, or participants may describe formal policy differently from actual practice.

6. Illustrative applied case: implementing a sustainability-reporting system

The following is a fictional illustrative case designed for teaching purposes.

Background

Northbridge Components, a medium-sized manufacturing company, introduced a sustainability-reporting system alongside its existing financial reporting process. Senior management expected the system to improve decision-making and prepare the company for increased stakeholder scrutiny.

Problem

After six months, the company had produced the required indicators, but managers rarely used them in budgeting. Senior leaders suspected employee resistance; operational staff argued that the indicators were difficult to interpret and disconnected from production decisions.

Analysis and approach

A student researcher adopted an explanatory sequential mixed-methods design. First, an internal survey of 84 employees measured perceived usefulness, training adequacy and use of sustainability information. The survey showed that perceived usefulness was moderate, while training adequacy was low. These figures did not explain how the reporting process operated in practice.

The researcher then conducted semi-structured interviews with 12 participants from finance, operations, procurement and senior management. Two observations of monthly performance meetings and an analysis of reporting templates and training documents were added. Purposive sampling ensured representation from users and preparers.

Initial codes included “unclear ownership,” “duplicate data entry,” “indicator unfamiliarity,” “managerial sponsorship” and “link to capital decisions.” These were developed into three themes:

1. Technical production without interpretive capability: employees could enter data but did not understand how indicators should affect decisions.
2. Fragmented responsibility: finance checked data quality, while operations controlled much of the underlying information, creating delays and disputes.
3. Conditional managerial use: executives discussed indicators when they were linked to investment or cost consequences, but not when they appeared as an isolated compliance exercise.

The researcher used a joint display to compare the survey and qualitative findings. The low training score was explained by interviews showing that training focused on data entry rather than interpretation. Observation revealed that meetings prioritised short-term cost variances, even though the new system was intended to broaden decision criteria.

Solution and outcome

The researcher recommended role-specific training, a clear data-ownership matrix, integration of selected indicators into capital-investment templates and a quarterly cross-functional review. The company piloted these changes in one division. Three months later, managers reported more frequent discussion of sustainability information, although the study did not claim that the pilot caused long-term financial improvement.

Learning points

The case demonstrates that quantitative findings may identify the scale of a problem while qualitative evidence explains its organisational mechanisms. It also illustrates the importance of bounded case design, multiple sources, transparent coding and cautious causal claims. The researcher should not generalise the outcome to all manufacturing firms, but the concepts may be analytically relevant to organisations with similar reporting structures.

7. Ethics, governance and technology

Qualitative research can expose personal opinions, workplace conflict, fraud allegations or commercially sensitive information. Ethical approval should address informed consent, voluntary participation, withdrawal, confidentiality, data storage, retention and potential harm. Participants should know whether quotations will be anonymised and whether managers may access raw data.

In accounting and finance, researchers may encounter privileged information, insider information, personal financial data or evidence of misconduct. The researcher should not promise absolute secrecy where legal or institutional reporting duties apply. Confidential files must be stored securely, access-controlled and deleted according to the approved protocol.

Artificial-intelligence tools can assist with transcription, translation, search, preliminary classification and organisation of large textual datasets. However, automated outputs may contain errors, reproduce bias, remove context or expose confidential information to third-party systems. Researchers remain responsible for checking transcripts, protecting data, documenting tool use and making the final interpretive decisions. AI-generated codes should be treated as provisional suggestions rather than evidence of analysis.

8. Common challenges and best practices

Common problems include overly broad research questions, convenience samples justified poorly, leading interview questions, insufficient attention to context, treating quotations as self-evident proof, confusing codes with themes, and claiming saturation without explaining the basis for stopping data collection.

Best practice includes:

• Begin with a focused question and an explicit methodological rationale.
• Pilot interview and focus-group guides.
• Maintain a sampling and recruitment log.
• Separate description, participants’ accounts and researcher interpretation.
• Keep an auditable record of coding decisions and revisions.
• Search for disconfirming evidence and variation.
• Use quotations selectively and identify their analytical purpose.
• Integrate mixed-methods findings in a joint display or equivalent structure.
• State limitations and avoid causal or generalisable claims beyond the design.
• Protect confidential accounting, employee and financial information.

Chapter Summary

Qualitative research explores meanings, experiences, practices and processes in context. Interpretivist, critical and pragmatic perspectives offer different assumptions about knowledge, while inductive, deductive and abductive reasoning can be combined in a carefully designed project. Interviews, focus groups, observation, document analysis and case studies each generate distinctive evidence and must be selected to fit the research question.

Credible qualitative analysis requires systematic familiarisation, coding, theme development, negative-case analysis and an audit trail. Quality can be strengthened through credibility, dependability, confirmability, transferability, reflexivity and triangulation. Mixed-methods research combines qualitative and quantitative evidence through designs such as convergent, explanatory sequential, exploratory sequential and embedded approaches. Integration must be planned and made visible, especially when findings converge or conflict. Ethical governance, confidentiality and responsible use of digital and AI tools are essential in accounting and finance research.

Key Terms

Abductive reasoning: Iterative movement between data and theory to develop the most plausible explanation.

Analytical generalisation: Relating case findings to concepts or theory rather than estimating population prevalence.

Case study: In-depth investigation of a bounded phenomenon in its real-life context.

Code: A concise label assigned to a meaningful segment of qualitative data.

Codebook: A structured record of code definitions, boundaries and examples.

Convergent design: Mixed-methods design in which qualitative and quantitative evidence are collected in a similar period and integrated.

Credibility: The plausibility and evidential support of qualitative findings.

Document analysis: Systematic interpretation of written, visual or digital records.

Explanatory sequential design: Quantitative research followed by qualitative research to explain results.

Focus group: Moderated group discussion used to examine views and interaction.

Information power: The extent to which a sample and data provide sufficient information for the study purpose.

Interpretivism: Perspective that emphasises socially situated meanings and participants’ interpretations.

Mixed-methods research: Intentional integration of qualitative and quantitative approaches in one inquiry.

Negative-case analysis: Examination of evidence that challenges an emerging pattern or explanation.

Observation: Systematic study of behaviour and interaction in context.

Purposive sampling: Selection of participants or cases because they possess relevant characteristics or knowledge.

Reflexivity: Critical consideration of the researcher’s influence on the research process and interpretation.

Theme: A coherent, significant pattern of meaning relevant to the research question.

Triangulation: Comparison of evidence from different sources, methods, researchers or perspectives.

Review Questions

Short Answer Questions

1. What distinguishes qualitative research from quantitative research?
2. Define purposive sampling and give one example.
3. What is the difference between a code and a theme?
4. State two purposes of using a focus group.
5. What is meant by reflexivity?

Long Answer Questions

1. Compare interviews, focus groups, observation and document analysis for an accounting research project.
2. Explain the stages of thematic analysis and show how a codebook supports credibility.
3. Discuss the philosophical assumptions associated with interpretivism, critical research and pragmatism.
4. Explain the main mixed-methods designs and identify a suitable design for a study of accounting-software adoption.
5. Evaluate the ethical and governance issues involved in analysing confidential organisational documents.

Analytical / Application Questions

1. A survey shows that employees use a new budgeting system infrequently, but it does not identify why. Design an explanatory sequential mixed-methods study, including sampling, interview topics and integration strategy.
2. You observe that finance staff describe a reporting process as efficient, while field notes show repeated delays and workarounds. Explain how you would analyse this divergence without assuming that one source is automatically correct.
3. Develop three initial codes and two possible themes for a case study investigating why sustainability indicators are not used in investment decisions. State what additional evidence you would seek to test your interpretation.

Multiple Choice Questions with Answers

Question 1

Which research question is most suitable for a qualitative interview study?

A. What percentage of firms use cloud accounting software?

B. What is the average audit fee paid by listed companies?

C. How do small-business owners interpret the usefulness of cloud accounting software?

D. Is there a statistically significant relationship between firm size and software adoption?

Correct Answer: C. How do small-business owners interpret the usefulness of cloud accounting software?

Question 2

What is the main distinctive value of a focus group?

A. It guarantees anonymous responses from participants.

B. It reveals interaction, agreement and disagreement among participants.

C. It produces statistically representative population estimates.

D. It eliminates the influence of the researcher.

Correct Answer: B. It reveals interaction, agreement and disagreement among participants.

Question 3

Which sampling strategy selects participants because they meet specified conditions?

A. Criterion sampling

B. Random sampling

C. Cluster sampling

D. Systematic sampling

Correct Answer: A. Criterion sampling

Question 4

Which statement best describes a qualitative theme?

A. A single interesting quotation

B. A broad pattern of meaning relevant to the research question

C. A list of all words appearing in a transcript

D. A numerical average calculated from coded data

Correct Answer: B. A broad pattern of meaning relevant to the research question

Question 5

What is negative-case analysis?

A. Deleting data that contradicts the main finding

B. Searching for evidence that challenges an emerging interpretation

C. Assigning negative numerical values to interview responses

D. Rejecting all participant accounts as biased

Correct Answer: B. Searching for evidence that challenges an emerging interpretation

Question 6

Which mixed-methods design begins with quantitative research and then uses qualitative research to explain the results?

A. Convergent design

B. Exploratory sequential design

C. Explanatory sequential design

D. Embedded-only design

Correct Answer: C. Explanatory sequential design

Question 7

A researcher uses interviews to develop items for a later questionnaire. Which mixed-methods rationale is illustrated?

A. Development

B. Replication

C. Randomisation

D. Statistical control

Correct Answer: A. Development

Question 8

Which practice most directly strengthens confirmability?

A. Keeping an audit trail linking interpretations to evidence

B. Using the largest possible sample regardless of the question

C. Avoiding all interpretation of participant accounts

D. Allowing organisational managers to rewrite transcripts

Correct Answer: A. Keeping an audit trail linking interpretations to evidence

Question 9

Why should a case study define its boundaries?

A. To ensure that every organisation can be statistically represented

B. To clarify what is included, excluded and treated as the unit of analysis

C. To avoid collecting evidence from more than one source

D. To guarantee that the case has no limitations

Correct Answer: B. To clarify what is included, excluded and treated as the unit of analysis

Question 10

What is an important responsibility when using AI-assisted transcription or coding?

A. Accepting automated codes without review

B. Uploading confidential files to any available platform

C. Checking outputs, protecting data and documenting the tool’s use

D. Replacing the researcher’s interpretation with the software’s conclusion

Correct Answer: C. Checking outputs, protecting data and documenting the tool’s use