advanced research methods lecture 6

What are the three types of interviews and when are they used?

Unstructured

• Free-flowing conversation

• No fixed questions

• Follows interviewee’s lead

• Used when little/no prior knowledge

• Purpose: Explore topic, build theory

Semi-Structured

• Interview guide with key topics

• Flexible order + follow-up questions

• Theory/practice informed

• Used for in-depth insights with structure

• Purpose: Build or elaborate theory

Structured

• Fixed list of short questions

• Often closed-ended

• Same questions for all participants

• Easy comparison and analysis

• Purpose: Compare opinions; test/build theory

Types of questions for semi-structured interviews

Open Questions (MAIN type to use)

• Cannot be answered with yes/no

• Contain only one question

• Start with a question word (what, how, why, etc.)

Closed Questions (Minimize use)

• Short or yes/no answers

• Limited detail

• Useful for verifying facts

Leading Questions (Must be avoided)

• Push interviewee toward a specific answer

• Cause interviewer/researcher bias

• Undesirable in research interviews

equipementen interview

• Interview guide / questions list

• Recording / Device (if present)

• Interviewee (Informant / Respondent)

What can interviews be used as in research?

• A stand-alone research method

• A data collection method (e.g. in case studies)

Are interviews and case studies the same?

• Doing interviews ≠ doing a case study

• Doing a case study ≠ interviews required

• Case studies are broader; interviews are optional tools

What are the main limitations of interviews?

• Provide opinions, not fully accurate facts

• Less suitable for sensitive topics

• Depend on interviewee memory (not good for long ago events)

• Subject to researcher/interviewer bias

• Time-consuming (prepare, conduct, transcribe, analyze)

Why are interviews widely used, and what should researchers consider?

• Interviews are widely used because they are relatively easy

• However, consider non-obtrusive alternatives when possible:

  • Observations

  • Archival documents

  • Secondary data

Semi-structured interviews are guided by an interview guide

1. Start with a topic

2. Think about potential respondents

3. Divide the topic into subtopics

4. Develop questions for each subtopic

5. Review logic and flow

6. Revise after a few interviews

Who are my potential interviewees and how many do I need?

• Knowledgeable informants

• Diverse perspectives (stakeholders/depts)

• Make a preliminary list → update as you go

• Snowballing: ask for more contacts

• More interviews → better (time permitting)

• Stop at saturation

• Ask same questions → triangulation

To record or not?

Advantages:

• Captures complete info & easy access

• Focus on listening, not notes

• Improves transparency & quality

• Easier transcription (automation)

Disadvantages:

• May affect responses (esp. sensitive topics)

• Small risk of technical issues

What is a case study & when is it used?

• Studies a real-life phenomenon in context

• Uses at least 2 types of data (qualitative + quantitative)

• Good for

  • exploring theories

  • building theories

  • testing theories

Advantages & disadvantages of case studies

Advantages:

• Deep dive into real-life context

• Study new phenomena without prior theory

• Uncover hidden issues

• Well received by practitioners

• Very versatile

Disadvantages:

• Researcher discretion → bias risk

• Time & resource intensive

• Limited statistical generalizability

• Hard to study past events (recollection issues)

• Difficult to write concisely & convincingly

Typology of case studies

• Single case: unique/extreme, deep insights, low generalizability, shows gaps, not proof.

• Multiple cases: replicate/enrich data, higher generalizability, balance depth vs. number, case selection matters.

Types of case study timing

• Cross-sectional: one point in time <=> Longitudinal: over time (emergence/change)

• Real-time: happening now <=> Retrospective: study past events

• Mix possible: look back + current development

Tips:

• Single cases → often longitudinal

• Multiple cases → often cross-sectional / retrospective

How to sample cases?

• No random sampling → theoretical sampling

• Single case: unique/extreme or easy access

• Multiple cases: choose similar, different, or a mix (replicate & compare)

Key data principles in case studies?

• Use ≥2 sources for rich, convincing data

• Combine sources (data triangulation)

• Examples: interviews + documents

• Sources serve different purposes (context, perspectives)

• Can include quantitative data

Common data sources in case studies?

1. Interviews

• Unstructured, semi-structured, structured

• Multiple informants, but prone to bias

2. Observations

• Real-time study only

• Recorded in field notes, helps verify interviews

3. Archival documentation

• Policies, reports, emails, databases

• Useful to recreate story & verify info

4. Other sources

• Videos, photos, audio, questionnaires

• Expert panels, secondary data

Quality criteria for qualitative research?

Validity:

• Construct: Is the construct well-defined and measured?

• Internal: Do data support findings/conclusions?

• External: Can results be generalized beyond the context?

Reliability:

• Hard to replicate due to unique contexts

• Thorough documentation is key

• Can recreate research process, but not exact results

hat are the general threats to quality in qualitative research?

1. Informant bias – participants’ values, opinions, memory, social desirability, sensitive topics, or personal stakes.

2. Researcher bias – researcher’s beliefs, interests, or poor skills affecting sampling, data collection, and analysis.

3. Idiosyncratic findings – unique cases limit generalizability beyond the sample.

What are the steps in the logic of qualitative data analysis?

1. Collect data

2. Organize & prepare (e.g., transcribe)

3. Code the data

4. Analyze for insights – describe, compare, relate

5. Report insights – develop findings, support arguments with data, link results to existing knowledge

What is thematic analysis and what are its steps?

• Definition: Flexible method to interpret data; identifies themes—important patterns related to the research question.

• 6 Steps (Braun & Clarke, 2006):
  1. Read & get familiar with data
  2. Create initial codes
  3. Search for themes (group codes)
  4. Review themes (keep, group, discard)
  5. Define themes in relation to theory/RQ
  6. Write up the analysis

What is coding in qualitative research and its levels?

• Definition: Coding is making sense of data; a decision-making process; type depends on research purpose.

• Levels:
  1. First-level: Descriptive, summarize data
  2. Second-level: Analytical, find patterns/themes
  3. Third-level: Integrate themes into theory