Action Research workshop data display.ppt
- 1. Action Research Workshop Data Analysis
- 2. Action Research Workshop: Data Analysis
- 3. Data analysis in the research process Observed events and objects Constructs, variables Concepts Method Theories Epistemology Values, world view Records Findings Results Interpretations explanations Knowledge claims Value claims Research review Research design Data collection Data analysis Discussion Internal validity External validity Investi- gative lens Episte- mological lens Research question 3
- 4. 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
- 5. Qualitative Data Analysis: Miles and Huberman Data Collection Data Display Data Reduction Conclusions: drawing/verifying 5
- 6. Data Reduction Ladder of Analytical Abstraction 3. Identifying patterns and proposing explanations 2. Identifying themes and trends 1. Summarizing interviews and technical documents After Carney (1990), Miles and Huberman (1994) Climbing the ladder is a process of transformation. From a validity perspective each step constitutes a threat 6
- 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 7
- 8. Display types: Tables (data matrix) 8 Topic Informant A Informant B Informant C Informant D 1 2 3 4 5 6 7..
- 9. Display types: Tables 9 Orlikowski, 1993, CASE Tools as Organizational Change: Investigating Incremental and Radical Changes in Systems Development, MISQ 17(3)
- 10. Data dispays: Timelines 10 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. 11
- 12. Display types: Networks 12 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 13 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 14 Period Implementation strategy Actual use User satisfaction Individual impact Organizationa l impact 1993-94 Software Engineering Medium Low Low Low 1994-97 Elephant Method Team development High High High High (but variable) 1995-98 Giraffe Project Organization development Medium Medium Variable Medium (and variable) 1998-2000 Voluntary, individual use Medium Medium Variable Low 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 15 Orlikowski, 1993, CASE Tools as Organizational Change: Investigating Incremental and Radical Changes in Systems Development, MISQ 17(3)
- 16. Working with data displays After M&H fig 5.4 Display Findings 1. Summarize 3. See themes/patters/clusters 5. Discover relationships 7. Develop explanations 8. Suggest re-analysis 6. Integrate/elaborate 4. Suggest comparisons 2. Make sense 16
- 17. Action Research Workshop: Data Analysis