Qualitative data analysis

Qualitative Data Qualitative Data AnalysisAnalysis

Part 1 QDA Methods

Outcomes – learners understand the origin and current practice of QDA methods in general, grounded theory in particular, and possible applications of the methods.

1.      Methods

a.      useful for inductive researchb.      useful in naturalistic inquiryc.      qualitative methods growing consensusd.      collection ↔ analysis ↔ collection

2.      Qualitative Data (QD)

a.      open b.      exploratoryc.      useful when questions to ask not yet been definedd.      allows insights

3.      Overview of QDA 

4.      QDA cycle 

5.      overview of process

6.      Work plan of research process

7.      Characteristics of QDA

a.      constructivist - many meaningsb.      context bound ie "book is in the pen"c.      uses inflection ie "THIS was good."d.      can be sorted in many ways e.      QD by itself has meaning ie “apple”

8.      Sources of QD

a.      interviewsb.      focus Groupsc.      field observationsd.      survey comments e.      historical recordsf.        secondary datag.      photos, paintings, songs ... 

9.      Types of Qualitative Data

a.      structured text, (writings, stories, survey comments, news articles, books etc)

b.      unstructured text (transcription, interviews, focus groups, conversation)

c.      audio recordings, as above, music d.      video recordings, as above (graphics, art, pictures,

visuals)

10. Methods Matched to Type of Data

a.      structured text b.      unstructured textc.      audio/ ie. interviews, anecdotes, “stories”

d.      video/graphics

11. Methods Matched to Principle Task

a.      reduce amount of data                   Matrix analysisb.      derive new ideas and insights                   Phenomenologyc.      test significance of ideas                   Post coding, Matrix analysis d.      map theoretical relation of ideas                    Grounded Theory Mapping

12. Principles of QDA (J. Morse)

a.      Data entry (gathering)b.      Comprehending (immersion)c.      Synthesizing (sifting)d.      Theorizing (sorting)e.      Re‑Contextualizing (emerging theory)

13. Data entry (analogous demonstration)

a.      not easily mechanizedb.      important part of processc.      often done by analystd.      concurrent with analysis e.      transcribe thoroughly, ASAP f.        write memos (reflect)g.      coding (start with few)

14. Comprehending (immersion)

a.      begin while entering data b.      start QDA immediatelyc.      “live with it”d.      line by line examinatione.      create new questions for collection 

15. Synthesizing (sifting) “quotes” (decontextualize)

a.      use inductive categoriesb.      find common threadsc.      compare transcriptsd.      aggregate stories

16. Theorizing (sorting) “coding”

a.      ask questions of the datab.      find alternative explanationsc.      allow sufficient time d.      be open to insights

17. Re‑Contextualizing (grounded theory)

a.      develop theoretical “elegance”b.      apply to other settingsc.      examine fit to literature/researchd.      describe emerging theory 

18. Data Management Principles

a.      stay close to the datab.      be sensitive to emergent theoryc.      allow recontextualizing d.      it is a non‑linear process

19. Grounded Theory

Primary documents (comprehending)

immerse in the primary documents

begin as data are collected

read/view/listen to the data

b.      Quotations (synthesizing)

select and mark salient quotations/passages

compare each line to other data

c.      Coding (theorizing)

assign codes in margin

group, sort, categorize codes into families

collect new data based on emerging theory, memos, codes

d.      Memos (aids in all processes)

record insights on memos or post-it notes ie: ideas for emerging theories, thematic ideas, linked memos

e.      Network (re-contextualizing)

create a network view (mind map)

    add and arrange network nodes (quotes, memos and codes)

collect more data as needed

f.        Presenting (computer methods)

 display quote and code lists

display node “mind-map” – export network .jpg into Word

export quotation data into SPSS.sps, – run crosstab (matrix)

export .html web page for multimedia use

20. Q&A Questions and answers

Part 2. EXERCISESPart 2. EXERCISES

Part 3. QDA SOFTWARE OVERVIEW

 Outcomes – learners will know how to choose among a few types of qualitative data analysis software, and their application to analyzing various qualitative data using QDA methods.

Some QDA Methods (and Software)

1.      Post-coding    ie. using SPSS, Excel

2.      Matrix analysis    ie. using Nud●Ist, SPSS

3.      Phenomenology - using mind maps    ie. Inspiration, Atlas-ti

4.      Grounded Theory Mapping    ie. Atlas-ti

Uses of computer software in Qualitative Studies

a. writing/editing the data

b. storage of datac. coding data (keywords or tags)d. search and retrieval of datae. data linking of related textf.  writing/editing memos about the datag. content analysish. display of selected reduced datai.  conclusion drawingj.  theory building - create explanationsk. graphic mappingl.  preparing reports Miles and Huberman 1994 

How to choose software - Key Questions

a. kind and amount of data?

b. choosing for 1 project or next few yrs?

c. theoretical approach to analysis?

d. time to learn vs time to analyze?

e. simplicity or detailed analysis?

f.  desired “closeness” to the data

g. any desired quantification of results?

h. individual or working as a team?

i.  peer software support available?

j.  Any cost constraints?   (Weitzman and Miles 1995; Lewins and Silver 2005).

Audio Data Analysis Using Atlas-ti 5.0 - demo

These slides at cesbc.ca Go to Links

QDA software

Atlas-ti (Scientific Software Germany)NVivo (QSR Australia) Inspiration (USA)Concept Systems (USA)Excel, Access, SPSS (USA)

Methodological foundations

Grounded Theory (i.e. Glaser and Strauss)Coding (i.e. Morse) Thematic Analysis (i.e. Giorgi)

Atlas-ti

Visual Qualitative Data Analysis Management and Model Building

Steps in Atlas-ti

1. Load primary documents

2. Mark “quotes”

3. Create codes, drag and drop on quotes

4. Retrieve coded quotes, conduct queries

5. Write memos

6. Draw network models, draw conclusions

Features

• Allows many types of primary documents: graphics, audio, video

All share the same code set

• Quotations to the letter or milli-second

• Queries retrieve and (dis)play all quotes

• Networks (dis)play all nodes and quotes

• Exports to SPSS data and syntax

• Exports to html

Disadvantages

• No matrix output display

• Cannot edit Word docs or use Word tables

• No drag and drop onto audio/video player