SOC200 - Qualitative Interviewing + Data Analysis (Chapter 11 + 13) Flashcards
Qualitative Interviewing
generally less structured: Survey interviews rigid set of standardized questions
questions are almost always open-ended
Qualitative Interviewing
Never know where you end up Structure in depth interview General interest – guide conversation Pursue issues in greater depth Explore unanticipated issues More powerful when used properly
Qualitative Interviewing
many of us already employ qualitative interviewing
qualitative interviewing is transformed from casual form of interaction, to a scientific tool
Interviewer has a general plan of inquiry but not a rigid set of questions that must be asked in particular words + order
Key Issues Related to Qualitative Interviewing
Degree of direction + structure from interviewer can vary depending on the research topic
Answers elicited by initial questions should shape following ones
Respondent’s line of discussion should never be halted or cut off, but subtly redirected back to your main concern
Key Issues Related to Qualitative Interviewing
Never halt – they will shut down and withdraw
need to be especially adept in following with questions that
Provide more detail on what respondent just said + redirects respondent back to the main concern
Key Issues Related to Qualitative Interviewing
Listening to what the respondent is saying, Interpreting his/her meaning, affects your next question
Using these skills almost simultaneously
Key Issues Related to Qualitative Interviewing
Rigid set can trip you up
Focus on negating own ego
Socially acceptable incompetent: might think you know what they’re saying, but you are actively engaging in question to find out what these experiences mean to them
Key Issues Related to Qualitative Interviewing
reviewing your interview records: prepare for subsequent interviews by: Identifying effective questions + questions you missed
Key Issues Related to Qualitative Interviewing
wording + tone used during your interviewing
How you responded to what was said + what you “should
have” followed up on
Identifying areas where you assumed the respondent’s meaning instead of asking for clarity
- In-Depth Interview Studies
qualitative interviews primary means of data gathering
Very effective for enabling researchers to access voices from “subordinated” groups in society
- In-Depth Interview Studies
More efficient data collection method than field observation
still allows issues + perceptions to emerge that would not come out of structured survey interviews
- In-Depth Interview Studies
What it means to them – asking the person is more valid than participating
What it means to them as opposed to what it means to you
- In-Depth Interview Studies
Limited by time consumption, hard to obtain, require great deal of cooperation from subjects
Too much to do large probablity samples, small non-probability samples are usually the main option
Good with exploratory studies
- In-Depth Interview Studies
limitations common among data collected through nonprobability sampling techniques
Data can often be used to develop future survey questions
Focus Groups
6-10 people brought together to discuss issue
discussion is almost always navigated by moderator
data generated via group interaction
popular in non-academic research (consumer, market, + political research)
Focus Groups
Depends on topic - # of ppl
Moderator: control conversation, probes, lets everyone talk, Keep them focused
Focus Groups
Interpret quantitative analysis like a survey
Designing questionnaires for stats can
Exploratory – small group, get info for bigger research project
Focus Groups
Only more recently have focus groups gained popularity in non-academic research
useful for helping researchers to interpret results of a quantitative analysis (like a survey)
testing the appropriateness of survey questions
Conducting Focus Groups
no more than 12 people
10-15, until you hit “saturation point” of responses - similar patterns and issues, themes
expect perspectives on topic to vary widely, then more groups may be necessary
Conducting Focus Groups
Stratification of groups may be necessary
If you know diff betw demographic groups – age groups, males/females
Participants for whom the topic is relevant only selected for focus groups
Conducting Focus Groups
Led by a Trained Moderator – keep the discussion on track, ensure everyone has a chance to speak + prevent anyone from dominating the exchange
Informal Environment – participants exchange with each other rather than with moderator
Conducting Focus Groups
Moderator’s Dilemma – involved enough to ensure exchange is on track, questions are answered + everyone has a chance to talk, while preventing exchange from being stifled by his/her involvement
Conducting Focus Groups
More ppl hard to control
Asking same questions to 15 diff groups of ppl
Complex/contraversial issues – need more
Advantages of Focus Groups
Captures real life data in social environment: high degree of face validity
Flexibility: adapted to fit a wide variety of research situations (# of participants, level of the moderator’s involvement, types of questions asked, # of sessions conducted)
Advantages of Focus Groups
Relatively low in cost: relatively inexpensive to conduct relative to cost of collecting the same information through other methods
Advantages of Focus Groups
Speedy results: relatively quick to conduct + low number of participants - results quickly compiled for decision making purposes
Advantages of Focus Groups
Reveals unanticipated information: enable aspects/issues of topic to emerge that would not have been possible through surveys/one-on-one interviews
Disadvantages of Focus Groups
Researcher control: researcher has less control
Data analysis issues: analyzing the data can still pose problem (analyzed like interview data?)
Moderator skills: require special skills & training. Lack of appropriate training & skills could compromise result
Disadvantages of Focus Groups
Difficulties with groups: group can be difficult to assemble Diff betw + within groups can also be difficult to deal with
Generalizability: not appropriate for making statistical descriptions of a large population - “one shot case studies”
Oral History
interviewee to recall their recollections + experiences
detailed information on given topic/individual, focus is always historical concerns
speaking to a respondent who has been exposed to set of experiences + asks them to describe those experiences + their feelings
Oral History
Anthropologists + historians
recently gained momentum in social sciences
aim is to gather information beyond particular life stories
Abstract social patterns from these stories
Advantages of Oral History
Correcting knowledge gaps: created by official documents and statistical overviews
Flexibility: issues pursued in-depth, reinforcing validity of info obtained
Relatively low in cost: All you need interviewer + a recording device
Disadvantages of Oral History
Reliability: hard to assess. Is reliance on memory, subjective feelings + stories a “proper” way to obtain “facts?” Depends on the issue under study + interpretation being offered
Is memory correct
Disadvantages of Oral History
Data analysis issues: separate fact from fiction/abstract to larger social issues may be difficult
Reactivity: interactive process can alter outcome of data
Disadvantages of Oral History
Generalizability: individs seldom selected by probability sampling. not a concern if aim is to document person’s story
Qualitative Data Analysis
nonnumerical examination + interpretation of observations
goal of identifying patterns/themes from observed data
inductive logic is central to identification of patterns in qualitative data: pool together patterns to contribute to theory
3 Main Approaches to Identifying Themes in Qualitative Data
1.Theory Driven: coding qualitative data according to researcher’s/existing theories.
coding scheme rationalized by logic of theory
have preconceived set of themes that you slot observations in, when there’s already knowledge
3 Main Approaches to Identifying Themes in Qualitative Data
Prior Data/Prior Research Driven: applying/modifying coding scheme used in previous research
3 Main Approaches to Identifying Themes in Qualitative Data
Inductively Driven: applying codes created from researcher’s own reading of data (grounded theory)
most time consuming
Searching for Patterns in the Data
similarities + dissimilarities
Searching for Patterns in the Data
- Structures: diff types of the behaviour?
- Processes: is there an order to types of behaviour?
- Frequencies of behaviour
Searching for Patterns in the Data
- Magnitudes of behaviour
- Causes: what appear to be the causes of behaviour/of any of its subtypes
- Consequences: how does the behaviour affect people?
Searching for Patterns in the Data
In searching for patterns (similarities and dissimilarities), many researchers strive to continuously refine generalized understandings over course of observations
Conclusions from one set of observations provide foundation for further refinement of observations
Searching for Patterns in the Data
- You may observe that verbal confrontations occur equally among males and females, but tend to escalate more among males
- may only escalate among males when each party is with all-male company
- it’s not all-male company, but that larger crowds of all males tend to encourage confrontation
The Role of Theory
Grounded theory fundamental part
ongoing iteration betw theory, data, + observation (recursiveness)
Observations of patterns in data lead to formulation of a provisional theory: interem theory – beginning to form theory
The Role of Theory
theory then compared against further observations to see whether it is true to data (constant comparative method: mini hypothesis testing)
The Role of Theory
Hypotheses come out of this constant comparison that lead to new directions for analysis + observation
role of grounded theory: iteration betw induction + deduction
Processing Qualitative Data
amorphous mass of raw observations
Prepare to mark in your observation notes, preliminary codes of some common themes you see emerging
Processing Qualitative Data: Coding and Memoing
iterative + highly integrated process
Coding: classification of phenomena under observation.
Processing Qualitative Data: Coding and Memoing
identifying + labeling parts of raw data that illustrate idea/concept related to phenomena
Goal is to group observations of similar events, objects + behaviours under a common classification
Processing Qualitative Data: Coding and Memoing
Complicated: grouping concepts into themes
Memoing: make notes + commentaries about patterns
your notes + commentaries about the patterns, relationships or differences you observe to make your codes
Processing Qualitative Data: Coding and Memoing
- Codes Notes: write down in plain language, what you mean by codes you use
Processing Qualitative Data: Coding and Memoing
- Theoretical Notes: make connections + relationships among concepts
- Operational Notes: describe how data were collected + situations encountered
