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Qualitative Data Analysis

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'The interpretive act remains mysterious in qualitative and quantitative data analysis. It is a process of bringing meaning to raw, inexpressive data that is necessary ... – PowerPoint PPT presentation

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Title: Qualitative Data Analysis


1
SWP32RES Research for Social Work Practice B
Lecture 7 Qualitative Data Analysis
2
  • The interpretive act remains mysterious in
    qualitative and quantitative data analysis. It
    is a process of bringing meaning to raw,
    inexpressive data that is necessary whether the
    researchers language is ANOVAs and means or rich
    descriptions of ordinary events (Marshall and
    Rossman 1989, p.114)

3
Major differences between the quantitative data
analysis and qualitative data analysis
  • 1) Quantitative analysis uses numbers as the
    unit of analysis whereas qualitative analysis
    uses words (derived from observations and
    interviews and then recorded in this form)

4
2) With quantitative data, analysis may well be
(and should be) worked out in the planning
stages eg. what statistical tests are going to
be used whereas with qualitative data it
simply cannot be decided upon in the planning
stages.
5
3) With quantitative data, analysis takes place
at the conclusion of data collection but in
qualitative data research it may take place
over the course of the research process,
especially during data collection.
6
PRINCIPLES OF PATTERN RECOGNITION (KELLEHEAR,
1993)
  • 1) CONTENT ANALYSIS
  • 2) THEMATIC ANALYSIS
  • 3) SEMIOTIC ANALYSIS

7
PRACTICAL PRINCIPLES IN CONTENT ANALYSIS
(KELLEHEAR, 1993)
  • 1) DECIDE ON CERTAIN CATEGORIES
  • 2) CHOOSE THE SAMPLE
  • 3) SELECT THE TIME PERIOD FOR SAMPLING

8
  • 4) DECIDE ON THE NUMBER OF EVENTS TO BE
    OBSERVED, ISSUES TO BE READ ETC.
  • 5) RECORD THE OBSERVATIONS
    SYSTEMATICALLY

9
MEASURES IN CONTENT ANALYSIS (KELLEHEAR, 1993)
  • SIZE OF TIME/SPACE ALLOCATIONS
  • SIMPLE APPEARANCE
  • FREQUENCIES
  • INTENSITY

10
CATEGORIES USED IN CONTENT ANALYSIS (KELLEHEAR,
1993)
  • COMPREHENSIVE
  • SPECIFIC CLEAR
  • SUPPORTED BY PRECISE DEFINITIONS

11
BASIC PRINCIPLES IN THEMATIC ANALYSIS (MILES
HUBERMAN, 1984)
  • 1) COUNT (LOOK FOR REPETITION)
  • 2) NOTE THEMES, PATTERNS
  • 3) MAKE METAPHORS, ANALOGIES OR SYMBOLS OF
    WHAT IS HAPPENING

12
  • 4) CHECK TO SEE IF SINGLE OCCURRENCES ARE
    REALLY SEVERAL
  • 5) CONNECT PARTICULAR EVENTS TO GENERAL ONES
  • 6) NOTE DIFFERENCES SIMILARITIES

13
  • 7) NOTE TRIGGERING, CONNECTING OR
    MEDIATING VARIABLES
  • 8) NOTE IF PATTERNS IN THE DATA RESEMBLE
    CONCEPTS /OR THEORIES

14
PRACTICAL PRINCIPLES OF SEMIOTIC ANALYSIS
(KELLEHEAR, 1993)
  • 1) ASSEMBLE ALL AVAILABLE MATERIAL TOGETHER,
    SUPPLYING AS MUCH INFORMATION ABOUT
    CONTEXT AS POSSIBLE
  • 2) IDENTIFY THE OPPOSITIONAL ELEMENTS

15
  • 3) COMPARE THE DIFFERENT ACCOUNTS/ELEMENTS
  • 4) TRACE THEIR SOURCE BEYOND THE SETTING

16
  • 5) CRITICALLY APPLY THEORETICAL TRADITIONS
    WHICH ARE SENSITIVE AND COGNISANT OF SOCIAL
    PROCESSES WHICH REPRESS, DISTORT,
    MYSTIFY OR OVERSIMPLIFY SOCIAL
    RELATIONS

17
PROCESS OF DISCOURSE ANALYSIS (POTTER
WEATHERALL, 1994)
  • 1) IDENTIFY VARIETY WITHIN THE INFORMATION
  • 2) IDENTIFY PAUSES, HESITATIONS OR
    NON-VERBALS
  • 3) SEEK DIFFERENT LEVELS OF MEANINGS
    ALTERNATIVE VIEWS

18
  • 4) QUESTIONS WHICH VALUES OR IDEOLOGIES ARE
    BEING DRAWN UPON
  • 5) QUESTION WHICH POWER RELATIONS HAVE SHAPED
    THE DISCOURSES TRACE THEM HISTORICALLY

19
  • 6) SEEK EVIDENCE OF CHALLENGE OR
    RESISTANCE TO IDENTIFIABLE DISCOURSES
  • 7) WHAT ARE THE RESULTS OF THESE CHALLENGES?

20
Coding
  • The process of identifying persistent words,
    phrases, themes or concepts within the data, so
    that underlying patterns can be identified and
    analysed
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