**Effective Interviewing Techniques**: Enhance Your Research Skills with Qualitative Methods

1. IS6000 – Seminar 7 Research Methods – Qualitative – Interviews – Data – c

2. Research Methodology • A strategy of inquiry to answer a research question • Qualitative • Quantitative

3. Conceptual Distinctions Qualitative Quantitative Discover facts or beliefs about social phenomena Fixed and measurable reality • Understand human behavior from the research subject’s perspective • Dynamic and negotiated reality

4. Methodological distinctions Qualitative Quantitative Collection: measuring things Analysis: numerical comparisons and statistical inferences Reporting: statistical analyses • Collection: participant observation and interviews • Analysis: analyze themes from descriptions • Reporting: in the language of the informants

5. Other distinctions Qualitative Quantitative Focus on numbers Experiment, survey Note: Questionnaire is not a method. It is one of the parts of a survey method. • Focus on words • Case study, ethnography, action research • Note, Interview is not a method. It is a data collection technique

6. Methodologies and methods • Research Methodology • Strategy of inquiry to answer research question • Research method • Instantiation of a methodology • Method • A way of doing something • Prescribed set of rules about how it should be applied (rigour) • There is no one ‘best’ method • The most appropriate method depends on the situation • Methods can be combined

7. Choice of methodology • Depends on • Research question • Current state of knowledge • Relates to the skills of the researcher • Qualitative or quantitative analysis • Logical or interpretive thinking • Relates to the role of the researcher • Observing, Measuring, Changing

8. Qualitative methods

9. Qualitative methods • Case Study • Ethnography • Action Research • …

10. Qualitative methods • People live, operate, and behave in contexts • Decisions and actions are highly contextual • Study people and the social and cultural contexts • Explore context and offer explanations for why

11. Qualitative methods: characteristics • Natural setting • Researcher as key instrument • Multiple sources of data • Inductive analysis

12. Qualitative methods: characteristics • Focus on emergent meaning • Evolutionary design • Holistic and contextual

13. Qualitative methods Data

14. Qualitative data • Text (words) • Records of what people have said, done, believed, or experienced

15. Data types/sources (collection techniques) • Interviews • Observations • Documentation • Documents • Diaries • User-generated data • Documented conversations

16. Interviews • Descriptive • Description of phenomenon as perceived by individuals • Develop multiple individual perspectives • Arrive at comprehensive description • Exploratory • Define questions, propose new constructs, or build new theories • Explanatory • Determine if suggestedrelationships really occur & are perceived

17. Interviews: advantages & challenges • Advantages • Targeted • Insightful • Challenges • Reflexivity • Inaccuracy • Artificiality • Response bias • Time

18. Interviews: modes & types • Mediated • Telephone, IM, Email, Social Media • Un-mediated • Face-to-face • Individual • Focus Group • Structured; Semi-structured; Unstructured • Protocols

19. Interview protocols • A protocol is a plan for the interview • Important for more valid/reliable research • Which topics, themes, questions are important? • How to start? Open and closed questions? • Interview or conversation? • Free-style or very structured? • You may not get a second chance to ask, so… • How will you record the interview? • Handwritten notes? Digital? • It is best to transcribe notes as soon as possible, or they get forgotten

20. Interview: example • Interview CEO of a hotel chain about knowledge management • Start off by asking CEO to talk about what s/he does • Make CEO more relaxed • Later, can ask the important questions • Not everything will be useful – that doesn’t matter • Not every question I ask is KM related • May talk about business, politics, literature, music, hotel guests  more like a conversation between friends

21. Observations • Often, we often encounter interesting situations • Conversations with data subjects (people) • Watching and reflecting how people behave in different situations • Our own internal thoughts and interpretations • Important to be aware of the ‘data’ around us • Interesting facts, happenings, people, behaviors

22. Observations • Unplanned, unscripted events • Document carefully • Important to record these • Be prepared for the unexpected • Refer back to them later • Paper notebook vs dictating to an iPhone • Drawings, sketches, text

23. Observations: example • Visit to company site • Company office is open style – no doors, rooms, privacy • Sketch of office layout, location of people, teams, equipment • Can then ask CEO about design (refer to notes) • Could give unexpected insights into office/company culture

24. Documentation: documents • Corporate documentation • Can help build up picture of organizational values • Can help to ask right questions • Strategic plans, business processes, training manuals, etc. • Paper or online • Internal or external (for employees, customers, clients,…) • Structured, semi-structured, unstructured

25. Documentation: diaries • Personal and subjective accounts written by individuals • Can be focused on specific situations or events • Example: • Study on data privacy violations • Ask employees to document each time they are asked to release private data, together with some details (nature of request, rank of requester, decision taken, etc.)

26. Documentation: user-generated data • Many potentially-useful forms • E.g., a user’s IM data can show the extent of IM communication for work purposes (i.e. not just social chatting) • Often private and confidential • Possible to access if you ask for permission

27. Documentation: documented conversations • Emails, IMs, SMS, etc. • Generated between the researcher and the informant • Useful to be able to look back at what was said a year or two ago • Can’t trust your memory • Need to organize all emails/messages that are sent to and received from research partners

28. Qualitative methods Data analysis

29. Overarching goal • Discover the meaning of statements • Not only specific content of concrete individual statements • General abstract meaning underlying a group of related statements • What are the person’s attitudes, values, feelings, wishes, judgements that underlie a group of statements?

30. Triangulation • Relate multiple sources of evidence • E.g., interview data vs. company documentation • Confirming & conflicting evidence • Increase robustness • Data collection and analysis highly interwoven

31. Coding • Reduce qualitative data to meaningful information • Categorize and organize data • Words, phrases, paragraphs, entire documents • Concepts, key ideas, or themes • Coding: Assign tags or labels • Code: word or short phrase that represents the primary content and essence of a piece of data • Book title • Coding is already analysis & interpretation

32. Coding: possible outcomes • Ground a model or theory out of the data • Think of a new way to organize work, people, processes • Identify new constructs • Identify a new mode of metaphorical communication

33. Coding: what to look for? • Themes • Key ideas that dominate data • Metaphors • Innovative ways of communicating ideas • Hidden meanings • Semiotics, e.g. red = hot; blue = cold • Similarities or differences across documents

34. Cites and Themes • “I rely on other people to remember things for me” • “If my friends can’t help me, they may ask their friends” • “I have experts within my in-groups – for gifts, media, printing” • “I had to ask my friends in the media industry for help”

35. Themes • You can use themes to structure a research report • Each theme is a major section • Previous literature on the theme? • Theory relating to the theme? • Need evidence from interviews and observations or documents • Need to create a persuasive story about the situation, structured around the themes

36. Themes • Look for themes that are consistent across • Different people • Different departments • Different organizations • Different cultures • Need to focus • To make sense of themes

37. Metaphors • The concept of understanding one thing in terms of another

38. Metaphors • Knowledge has a short half-life • The system died • Killer apps • We should embrace IT • This function is a cornerstone of the program

39. Metaphors (CityU strategic plans) • We need to forge links with industry • We are on the edge of a new beginning • We have a large pool of graduates • We immerse our students in an IT-rich environment • We are moving into the knowledge society • The university feels the pulse of the community

40. Metaphors (IS literature) • Information is power • Organization is information • Systems implementation is a war between users and developers • Information systems are competitive weapons • The system is a waste of space • The CIO is a dinosaur

41. Types of metaphors • Look for certain metaphorical themes • Industrial, domestic, military, technical, poetic • Common or popular metaphors • on the edge; forge links • Something comfortable, reliable, gentle • We are a family; IT is happiness • Something new as a way to express a novel concept • Do universities really “feel the pulse”? • Metaphors can hint at the tone of a document, culture, style

42. How are Metaphors Interpreted? • Metaphors are interpreted – and misinterpreted. • Carefully placed metaphors can help to create a culture, a community • “We are all members of the same team” • But use of a metaphor that people dislike can be counter-productive • “We live or die together”; “Freedom or death”; • Even “we” is problematic. Who is ‘we’ and who is not? Do you want to be one of the ‘we’?

43. Metaphors • What kind of text might be most richly metaphorical? • Why do people use metaphors? Who uses metaphors? When? • Do metaphors conceal or reveal? • What can we learn from a metaphorical analysis? • Look for themes in the metaphors

44. Data Coding Exercise • See the document on the website titled Week 7 Data Coding Exercise • This contains 22 statements from employees at a firm in Hong Kong called Scatex. I mentioned this firm in class 2 weeks ago. • You need to code this data into themes. Work as a team, discuss carefully, and then come to share your themes on the whiteboard. Each item (1-22) should fit into at least one theme.