1. Research, Research, Research Understanding the Basics Jim Yonazi, Ph. D The Center for ICT Research and Innovations – C i RI yonazijim@gmail.comyonazijim@gmail.com, yonaz@ifm.ac.tzyonaz@ifm.ac.tz

2. The Flow ›Introduction ›Research

3. Introduction Knowing each other

4. Research Basics ›But What us Research? -How your pages are set up -It means creating some new knowledge -Lets have an example

5. Everyday Versus good Research ›Everyday thinking is often characterized by Poor data Incomplete data Hasty thinking ›Academic research us is often characterized by Sufficient Data sources Appropriate data sources Accurately recorded Properly analyses No hidden assumptions Conclusions well founded Properly presented

6. Approaching research: deduction vs Induction ›Deduction Starts with hypotheses then data then conclusions No new theory is produced, but modified ›Induction starts with data, then theory a new theory is produced

7. The general framework Identify a Problem Gather data Analyze data Interpret Data Draw Conclusion

8. Approaching research: deduction Hypothesis 1 Central Issue Data Collection Data Analysis Hypothesis 2 Hypothesis n Initial Research Model Hypothesis 1 Central Issue Hypothesis 3 Hypothesis n Modified Research Model

9. Approaching research: Induction Data Collection Data Analysis New theory Hypothesis 1 Central Issue Hypothesis 2 Hypothesis n

10. Research vs ICT development project ›You can do a research on ICT (e.g. software) development ›You need to have a justified scientific reason as to why you are conducting the research ›You can develop a software ›You need to have a justified reason as to why the software is the most relevant solution

11. Approach in Information systems ›The Design Science approach Design Relevance Rigor

12. Approach in Information systems Figure 1 ‑ 1: Design science Research Circles (Source: Hevner, 2007)

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14. Thinking of Data Analysis that matters

15. What is data Analysis ›Evaluating ›Validating ›Interpreting ›Reporting

16. Could be Quantitative and Qualitative ›Quantitative Use numerical analysis and inferences to extract meanings ›Qualitative Use narratives, graphics etc interpretations to extract meanings

17. Example of quantitative

18. Example of qualitative

19. References ›Hevner, A. R., 2007, ‘A three Cycle view of Design Science Research’, Scandinavian Journal of Information Systems, Vol. 19, Issue. 2, pp. 87-92

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