Data Analysis Data analysis in the research process

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Data Analysis

Data Analysis

Data analysis in the research process Values, world view Epistemology Research Theories review Value

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

Qualitative Data Analysis – How to make sense of the “raw information” • Material

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 3

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

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

Data Reduction Ladder of Analytical Abstraction 3. Identifying patterns and proposing explanations 2. Identifying

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 5

Data Reduction Ladder of Analytical Abstraction

Data Reduction Ladder of Analytical Abstraction

Key tool: Data Displays • Display: A visual format that presents information systematically, in

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

Display types: Tables (data matrix) Topic Informant A Informant B Informant C Informant D

Display types: Tables (data matrix) Topic Informant A Informant B Informant C Informant D 1 2 3 4 5 6 7. . 8

Display types: Tables Orlikowski, 1993, CASE Tools as Organizational Change: Investigating Incremental and Radical

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

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 11 SHEPPARD, B. & J. BROWN. " Meeting the challenge of

Data displays: Networks 11 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.

Display types: Networks This arrived by way of Stanley Wasserman at the SOCNET Listserv

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 12

Data displays: Process Hagmann, J. R. , E. Chuma, K. Murwira, M. Connolly, and

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: Table of events and outcomes Period Implementation Actual use strategy Software Engineering

Data displays: Table of events and outcomes Period Implementation Actual use strategy Software Engineering Medium User satisfaction Individual impact Organizationa l impact Low Low 1994 -97 Elephant Method Team development High (but variable) 1995 -98 Giraffe Project Organization development Medium Variable Medium (and variable) Voluntary, individual use Medium Variable Low 1993 -94 1998 -2000 Table 3: Summarizing the project, using De. Lone and Mc. Lean'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

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

Java Application vs Browser Source: Braa, Roland, Sanner

Java Application vs Browser Source: Braa, Roland, Sanner

More examples from Miles & Huberman

More examples from Miles & Huberman

Working with data displays 8. Suggest re-analysis 6. Integrate/elaborate 4. Suggest comparisons 2. Make

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 18 After M&H fig 5. 4

Til neste gang • Lag 3 foiler: – Motivasjon (real life problem) og scope

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