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Data Analysis

Data Analysis. Data analysis in the research process. Values, world view. Value claims. Research question. Epistemology. Knowledge claims. Research review. Discussion. Interpretations explanations. Theories. Concepts. Results. External validity. Episte- mological lens.

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Data Analysis

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  1. Data Analysis

  2. Data analysis in the research process 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 of Analytical Abstraction 3. Identifying patterns and proposing explanations 2. Identifying themes and trends 1. Summarizing interviews and technical documents Climbing the ladder is a process of transformation. From a validity perspective each step constitutes a threat After Carney (1990), Miles and Huberman (1994)

  6. Data ReductionLadder of Analytical Abstraction

  7. Key tool: Data Displays • Display: A visual format that presents information systematically, in to order to help the researcher to identify findings. • ”You know what you display” (p. 91.) • Viewing the condensed ”full data set” in one view • It is creative and fun to make good data displays! • They are also very useful in publications

  8. Display types: Tables (data matrix)

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

  10. 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).

  11. 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.

  12. Display types: Networks This arrived by way of Stanley Wasserman at the SOCNET Listserv (from the International Network of Social Network Analysts) – The NYT’s Social Network analysis of who Academy Awards                                  

  13. 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.

  14. Data displays:Table of events 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.

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

  16. Java Application vs Browser Source: Braa, Roland, Sanner

  17. More examples from Miles & Huberman

  18. Working with data displays 8. Suggest re-analysis 6. Integrate/elaborate 4. Suggest comparisons 2. Make sense Display Findings 1. Summarize 3. See themes/patters/clusters 5. Discover relationships 7. Develop explanations After M&H fig 5.4

  19. Til neste gang • Lag 3 foiler: • Motivasjon (real life problem) og scope • Forskningsspørsmål • Forskningsdesign

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