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Action Research Workshop Data Analysis GI Group April 2011

Action Research Workshop Data Analysis GI Group April 2011. Bendik Bygstad Institute of Informatics, UiO Norwegian School of IT. Data analysis in the research process. Values, world view. Value claims. Research question . Epistemology. Knowledge claims. Research review.

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Action Research Workshop Data Analysis GI Group April 2011

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  1. Action Research WorkshopData Analysis GI Group April 2011 Bendik Bygstad Institute of Informatics, UiO Norwegian School of IT

  2. Data analysis in theresearchprocess Values, world view Value claims Research question Epistemology Knowledge claims Research review Discussion Interpretations explanations Theories Concepts Results External validity Episte- mological lens Method Findings Data analysis Research design Constructs, variables Records Internal validity Investi- gative lens Observed events and objects Data collection

  3. Qualitative Data Analysis • How to make sense of the “raw information” • Material is unstructured: interviews, field notes, documents, photographs... • Want to find patterns and explanations, while retaining sense of original accounts and observations • What does it all mean? • Fundamental tasks are: defining, categorising, mapping, exploring, explaining, theorising... • Will it help you to use a software package, such as Atlas TI? • Yes, it will help you to keep track of data • No, it will not do the analysis for you

  4. Qualitative Data Analysis: Miles and Huberman Data Collection Data Display Data Reduction Conclusions: drawing/verifying

  5. Data ReductionLadder ofAnalyticalAbstraction 3. Identifyingpatterns and proposingexplanations 2. Identifyingthemes and trends 1. Summarizinginterviews and technicaldocuments Climbingthe ladder is a processoftransformation. From a validity perspective each step constitutes a threat AfterCarney (1990), Miles and Huberman (1994)

  6. Key tool: Data Displays • Display: A visual format that presents informationsystematically, in to order to helptheresearcher to identify findings. • ”You know whatyou display” (p. 91.) • Viewingthecondensed ”full data set” in oneview • It is creative and fun to make good data displays! • Theyarealsoveryuseful in publications

  7. Display types: Tables (data matrix)

  8. Display types: Tables Orlikowski, 1993, CASE Tools as Organizational Change: Investigating Incremental and Radical Changes in Systems Development, MISQ 17(3)

  9. Data dispays: Timelines Moens, Broerse and Munders (2008). Evaluating a participatory approach to information and communication technology development: The case of education in Tanzania. International Journal of Education and Development using ICT, 4(4).

  10. Data displays: Networks SHEPPARD, B. & J. BROWN. " Meeting the challenge of information technology through educational partnerships: A case study ", International Electronic Journal for Leadership in Learning, 2(11), 1998.

  11. Display types: Networks • Thisarrived by wayofStanley Wasserman at theSOCNETListserv (from theInternational Network of Social Network Analysts) – The NYT’s Social Network analysisofwhoAcademyAwards

  12. Data displays: Process Hagmann, J. R., E. Chuma, K. Murwira, M. Connolly, and P. Ficarelli. 2002. Success factors in integrated natural resource management R&D: lessons from practice. Conservation Ecology 5(2): 29.

  13. Data displays:Tableofevents and outcomes Table 3: Summarizing the project, using DeLone and McLean's key concepts. Bygstad, B. (2003) The Implementation Puzzle of CRM Systems in Knowledge Based Organizations.Information Resources Management Journal. Nov 2003.

  14. Data displays: Explanations Orlikowski, 1993, CASE Tools as Organizational Change: Investigating Incremental and Radical Changes in Systems Development, MISQ 17(3)

  15. Workingwith data displays 8. Suggest re-analysis 6. Integrate/elaborate 4. Suggest comparisons 2. Make sense • Display • Findings 1. Summarize 3. Seethemes/patters/clusters 5. Discoverrelationships 7. Developexplanations After M&H fig 5.4

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