SU3- Qualitative Data, Descriptive Research
Qualitative data analysis
Characteristics
Steps
Quantitative (descriptive) data analysis- survey
Error types
Survey methods
Person-administered
Cons- slow data collection, recording errors, interviewer-respondent interaction error, expensive
Selection criteria
Focus- increase understanding (vs. quantifying magnitude or explaining) of phenomenon
Criticism- lacks rigour (words/text, images/visuals vs. numbers)
Qualitative vs. quantitative
Qualitative
Quantitative
Data- text/words, visual/images
Focus- understanding
Process
Member checking- key informants verifies report/analysis accuracy; solicit feedback
Ongoing, iterative- simultaneous data collection + analysis; affects future data collection (e.g. qns asked)
Inductive- data > themes; grounded theory
Data- numerical
Focus- quantifying (descriptive), explaining (causal)
Process- deductive i.e. themes (defined before collection) > data
(Preliminary) managing data collection- transcription, data entry (observations, notes, respondents' narratives)
Data display (sum/reduce data) e.g. tables (explains key themes), figures, flow diagrams (suggests variable r/s), trees, maps, matrix (quotes for various themes from key informants)
Conclusion drawing, verificatioon
Data reduction
Categorisation- group by similar content
Coding- (labels/numbers attached to different categories)- for tracking, organisation
Comparison (similarities/differences in categories identified)- helps refine theories
Integration (theory-building)
Data reduction
Tabulation (response count)- number of times selected categories mentioned
Code sheet- list different categories + respective coding
Recursive, circular r/s i.e. variable is both cause, effect
Selective coding- one core/central category (frame) storyline built around
Iteration (repetitively working thru data)- modify/revise earlier analysis by uncovering issues unaddressed by alr collected data
Negative case analysis (deliberately looking for instances contradicting developing theories)- establishes theory conditions
Most frequently coded/mentioned- implies importance, further investigation/analysis/follow-up
Don't report % (implies statistically projectable results)
Co-occuring themes (concepts frequently mentioned together)- r/s
Does not imply magnitude of finding- use quantitative research
Bias check
Establish credibility
Emic validity- key members within culture agree with report findings
Cross-research reliability- data coding similarity by different researchers
Triangulation- multiple perspectives analysis e.g. different data collection methods, analyses, sets, researchers, time periods
Peer review- by external specialist/methodology
First impression salience
Selectivity (overconfidence of some data)- confirmation bias
Co-occurrences- taken as correlation/causal r/s
Extrapolating instances rate to population- when it is non-generalisable
Not accounting unreliable info sources
Report writing
Intro
Data analysis, findings
Conclusion, recommendations (actionable implications)
Methodology description
Research qn, objectives
Lit. review
Data displays
Findings- interpretation, summary
Topics covered
Methods used, location, date, time
Researcher info- qty, background
Participant selection procedure
Participant info- qty, characteristics
Data analysis procedures e.g. coding, peer review, etc.
Limitations
Usage
Research problem nature- describe specific situational characteristics/evaluate strategies
Research qn- focuses on who, what, when, where, how
Task
Identify meaningful r/s
Determine true differences- magnitude
Verify r/s validity
Sampling error- finding differences (sample vs. population true value)
Non-sampling error (not-related to sampling; systematic)
Reduce
Increase sample size
Appropriate sampling method
Sources
Respondent
Measurement/questionnaire design
Problem definition
Project administration
Characteristics
Creates systematic variation/bias
Controllable
Cannot be directly measured
Creates other non-sampling error
Cannot be reached
Unwilling to participate
Inaccurate response
Types
Nonresponse error- final sample different from planner sample i.e. significant number of preselected prospective respondents refuse to participate
Response error- respondents impaired memory/inaccurate response
Pros- adaptable, rapport, feedback, response quality
Telephone-administered/interviews
Self-administered- respondent record own response (without interviewer' presence)
Types
In-home
Executive- typically in office with biz executive
Mall-intercept
Purchase-intercept0 intercept after product purchase/selection
Pros- cheaper, faster, more respondents, close supervision of interviewers, wide geographic area, callback for respondents who did not answer, random digit dialing (random sample)
Cons- cannot present non-audio stimuli, respondent hang up (esp. for lengthy interview), limited by national borders (rarely used in international research), high refusal rates, annoyed by interruption, poor telephone iinterview perception due to sugging (selling under research disguise)
Computer-assisted telephone interviews (CATI
Interviewer reads qns from screen
Enters ans directly into comp.
Pros- lower cost, can send inbound calls to interviewer at later time, eliminates separate data entry and edits, real-time results tabulation
Types
Mail panels- individuals agree to participate in advance
Drop-off- respondent completes and returns survey to researcher
Internet- online surveys
Pros- no interviewer cost (training, traveling), search cost, can reach hard-to-interview ppl
Cons- low response rate (nonresponse bias), misunderstood/skipped qns, slow (time-lag btw survey mailing, returning)
Pros- screen in advance (ensure representative sample), high response rate
Useful for longitudinal research (across periods) - observe changes over time
Pros- respondents' availability, screening
Cons- expensive
Pros- cheaper (than other survey methods), most frequently used, no coding needed, can reach hard-to-reach samples, techonology has improved functional capabilities over paper surveys, can randomise qn orders (remove effects of qn order)
Situation
Task
Respondent
Budget
Time frame- longer = higher quality data
Data quality requirements
Difficulty- complex survey (need researcher presence, conduct)
Stimuli (e.g. product, ads) to elicit response- telephone limits visual stimuli
Amt of info
Topic sensitivity
Diversity- degree of shared/common characteristics
Incidence rate- % of general population making up research focus
Participation degree
Ability- mail survey eliminates need for continuous time to complete questions, telephone survey can call back at more convenient timing
Willingness- uninterested, busy, privacy concerns
Knowledge lvl- more detailed info requirement, respondent to be more highly knowledgeable
Affected by scale measurement, questionnaire design, sample design, data analysis
Completeness (data depth, breadth)- full vs. vague picture
Generalisability- represents studied population
Precision (response accuracy)
Non-generalisable- low response rate, small sample size
Mail, internet- precise, not generalisable (hard to get representative sample)
Telephone- generalisable but not precise (short qns, interview duration)
Large, detailed info
Needs trained interviewer
Lower response rates
Higher respondent fatigue
Online (vs. phone, face-to-face) survey- more honest
Low- need more resources to get respondents
Goal
Reduce search time, cost
Maximising data amt