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QUALITATIVE DATA ANALYSIS

QUALITATIVE DATA ANALYSIS. A/Professor Denis McLaughlin School of Educational Leadership. QUALITATIVE DATA ANALYSIS. Y ou have a book of readings with relevant extracts from the following books. They must be read Dey, I (1993) Qualitative data analysis , London: Routledge

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QUALITATIVE DATA ANALYSIS

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  1. QUALITATIVE DATA ANALYSIS A/Professor Denis McLaughlin School of Educational Leadership

  2. QUALITATIVE DATA ANALYSIS You have a book of readings with relevant extracts from the following books. They must be read Dey, I (1993) Qualitative data analysis, London: Routledge Miles, M & Huberman, A (1984). Qualitative data analysis, Newbury park: Sage Miles, M & Huberman, A (1994). Qualitative data analysis : An expanded source book (2nd edition), Thousand Oakes: Sage Coffey, A. & Atkinson, P.(1996).Making sense of qualitative data, Thousand Oaes: Sage Marshall, C. & Rossman, G. (1989).Designing qualitative research. Newbury Park: Sage Tesch, R. (1990). Qualitative research, New York: Falmer Press Creswell, J. (1998). Qualitative inquiry and research design, Thousand Oaks: Sage Creswell, J. (2002). Analyzing and interpreting qualitative data (pp256-283). In J Creswell, Educational research, Thousand Oaks: Sage Maykut, P. & Morehouse, R. (1994) Qualitative data analysis: using the constant comparative method , In P. Maykut & R. Morehouse, Beginning qualitative research, London Falmer Press

  3. RESEARCH STRATEGY IDENTIFICATION • RESEARCH PROBLEM RESEARCH PURPOSE RESEARCH QUESTIONS ISSUES TO BE EXPLORED APPROPRIATE TECHNIQUES

  4. OVERVIEW OF QUALITATIVE ANALYSIS Data Collection Data display Data reduction Conclusions: drawing / verifying (Miles & Huberman, 1984; 1994)

  5. INTERACTIVE PROCESS OF DATA ANALYSIS Data display • Data collection Reflection on Data ITERATIVE SIMULTANEOUS Data Coding Data distillation (reduction Generation of Themes Story interpretation Research Conclusions

  6. QUALITATIVE ANALYSIS(Dey, 1993) describing Connecting Classifying

  7. Qualitative analysis as an iterative spiral Dey, 1993

  8. DATA ANALYSIS PROCEDURES In this section of your Design chapter mention the following characteristics of the process Data analysis is an eclectic process (Tesch,1990) Occurssimultaneouslyand iterativewith data collection, data interpretation and report writing (Creswell, 2002; Miles & Huberman, 1984) Is based on the on data reduction and interpretation-decontextualisation & recontextualisation(Marshall & Rossman, 1989; Tesch, 1990)

  9. 2. Data Analysis Procedures • Represents information in matrices-displays of information , spatial format that presents information systematically to reader • (Miles and Huberman, 1984) A I page example of this must be placed in this chapter eventually • Display categories by informants, sites and other … • Tables of tabular information showing relationships among categories of information • Identifies the coding procedure to be used to reduce information to themes / categories(Read Tesch, 1990, pp142-145).

  10. Categorisation and Themes • Constant comparative content analysis • Themes generated from the literature review • Themes embedded in instrument questions • Themes embedded in research questions • Combination of any of above

  11. DATA ORGANISATION(Miles & Huberman, 1994) DEVELOP MATRICES : VISUAL IMAGES OF INFORMATION Comparison tables –themes, participants, sites Heirarchical trees visually representing themes & their relations Figures in boxes to indicate the processes, time sequence, evolution of themes Organising the data by typeinterviews, observations, documents Organising by participants or sites combinations See Michael Dredge’s Power point at the end of this sequence on this issue

  12. DATA ANALYSIS • MANUAL • LESS THAN 500 PAGES OF TRANSCRIPTS OR FIELD NOTES • WANT TO “FEEL” CLOSE TO DATA • CANNOT AFFORD TO HAVE ALL INTERVIEWS TRANSCRIBED • (4 HRS TO TRANSCRIBE 1 HR TAPE INTERVIEW) • COMPUTER • MORE THAN 500 PAGES OF DATA • CAN AFFORD PROGRAM AND TRANSCRIBER • ATLAS.ti • QSR N5 (NUD8IST 5.0) • NVivo • Ethnograph • WinMAX • HyperResearch

  13. CODING DATA(see Tesch, pp142 -145) • 1. Get sense of whole: read all carefully • 2. Pick one document “what is its underlying meaning” write thoughts themes in margin • 3. Do this for several informants; Cluster together similar topics; arrange topics into major topics, unique topics, left overs • 4. Revisit data with topics; Abbreviate the topics as codes; Re-analyse. Do new codes emerge? • 5. Turn topics into themes • 6. Reduce number of themes by grouping similar themes • 7. Diagrammatize the basics of the numbers 5 & 6 • 8. Finalise abbreviations- alphabetise codes • 9. Perform preliminary analysis on material belonging to each theme • 10. If necessary, recode existing data • Always include in your design chapter a page of text (exhibit 4.x) illustrating the how you code the text

  14. Read text data Divide text into segments of information Code segments Reduce Codes Collapse codes into themes Codes reduced to 20 Many pages of texts Many segments of texts 30 – 40 codes Codes reduced to 5 -7 themes CODING PROCESS (Creswell, 2002) (Matrix example)

  15. Initial data analysis Major and minor topics Theme 1 Theme 2 Theme 3 Theme 4 Final interpretation Description of Data Analysis(Matrix example) In your analysis chapter you would present a diagram such as this at the beginning but with actual contextual material to illustrate the flow of your analysis. You would “flag” this overview in your Design chapter and refer specifically to it Stage 1 Data collection, display reflection Stage 2 Data coding & distillation Stage 3 Generation of key themes Stage 4 Story report & conclusions

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