Graduate Writers’ Workshop Week 5 Data Analysis: Best Practices in Presenting Data and Methods

1. Graduate Writers’ WorkshopWeek 5Data Analysis:Best Practices in Presenting Data and Methods Dr. Erica Cirillo-McCarthy Assistant Director of Graduate and ADEP Writing The California Lutheran University Writing Center

2. Today’s Presentation • Brief overview of the writing process • Components of Introduction section • Components of Data Analysis: Description, Validity, Discussion, and Conclusion

3. The Recursive Nature of the Writing Process • Inventing • Drafting • Revising • Editing In between all of these stages, you return again and again to the sources, to the research, to the theories, to the beginning, middle, and end

4. Before you begin writing: • When reviewing results, consider the following: • Significance of results • Generalizability of the research (limitations) • Reliability • Validity

5. Invention Approaches • Free write a narrative regarding what brought you to this research • Why are you interested in this research? • What personal connections do you have to this method, the participants, the location, the problem? • Free write what you would like to see done with this research • Consider audience – for whom is this research conducted? • Who would use this research? In what way? • Who are the key stake holders? • What end result do you envision for this research, i.e. new policy implementation? • What is the next step in this research, i.e., broader population or multiple sites?

6. Introduction Components • Brief overview of the context behind the study • Ask yourself: what was the problem that led you to this research? • That same question could be the big “Why?” Why did you decide as a researcher to look at this phenomenon, case, comparison? • Go back to the funnel process: start general, then move to more specific • Write this section last

7. Data Analysis: Description • Contextualize data production (where, how, who, what, why) • Talk about the end of data – what was used and what was kept out and why • How were the findings produced? • Consider themes and concepts – how were they derived? • Show evidence of thinking – what led to what? How were the connections uncovered? Rule of thumb for description: readers should have enough information to be able to recreate data collection

8. Data Analysis: Validity – Qualitative Research • Some questions to consider when analyzing qualitative data in regards to validity: • How well does this analysis explain why people behave in the way that they do? • How comprehensive would this explanation be to a thoughtful participant? • How well does the data cohere with what we already know?

9. Data Analysis: Methods Section Components • Clarity is most important • Consider this reason behind the methods section: so that others can replicate your study • The slow process of your research should be clearly described in this section • If you use a specific methodology (ethnography, case study, action research) then make that clear and refer to the experts in that methodology (Geertz for ethnography, Stake for case studies) • Finally, JUSTIFY your methods – why this method and not another?

10. Data Analysis: Discussion Contextualize the research • Look back: • Where does this research fit into the larger conversation? • What is new or different from previous work? • Look at similarities/differences in data, methods, location, theoretical grounding • Look forward: • What are the implications of this study? • Is it transferable? • What are the weaknesses and limitations?

11. Data Analysis: Conclusion • Summarize results • Bring in validity • Look ahead – what is the next step in research • Reiterate key stake holders and potential of results

12. Bibliography • Anderson, Claire. “Presenting and Evaluating Qualitative Research.” American Journal of Pharmaceutical Education 74.8 (2010): 141-145. • “Purdue OWL.” Purdue University. Accessed 11/1/12. http://www.owl.purdue.english.edu