1. Qualitative Papers

2. Literature Review: Sensitizing Concepts Contextual Information Baseline of what reader should know Establish in prior research: Flaws Gaps Potential new directions Set up your research Support your subsequent research question

3. Qualitative Papers Focus on: Question to address or interest in something Methods: Collection of Data Why and how data were collected Description of persons and contexts Data analysis: Coding (putting identifiers on what you found) Looking for themes (main ideas) Putting it all together (what you learned) Data Analysis is often written as data are analyzed and then edited into the results

4. Analysis Qualitative analysis is typically systematic and intensely disciplined—not “purely subjective.” The tactics we cover will help us be systematic. Qualitative analysis: is documented in ways that others could come to the same conclusions as the researcher shuns discreet stages, making sense of the information begins as the first data are collected involves loop-like patterns of revisiting the data over and over to address additional questions, uncover new connections in the data, and draw out more complex formulations as understanding of the data deepens involves being very selective in the topics one chooses to address using the data (there always are multiple possibilities)

5. Analysis Throughout, the analyst should ask and re-ask these questions: What patterns and common themes keep popping up? How do these patterns help me answer my research questions or assess the issues of focus? Are there deviations from these patterns? What factors may explain the atypical? What interesting stories are emerging? How do these help me answer my research questions or assess the issues of focus? Does anything call for additional data? Do any study questions or issues need revision? Do my findings corroborate other research? If not, what might explain the differences?

6. Qualitative Papers Data Analysis Process: 1.Read Data, develop ideas and feel 2.Code Data, tag items with same meaning using a unique code 3.Search and extract instances of codes 4.Identify patterns among codes (pattern coding) 5.Create figures, tables, or descriptions of patterns ANALYSIS THEMES

7. Analysis Process of Qualitative Analysis: Data Reduction Data Display Conclusion Drawing and Verification

8. Analysis Data Reduction Refers to the process of selecting, focusing, simplifying, abstracting and transforming data that appear in notes, transcripts, documents, etc. Choices must be made on exactly what to describe, what to code, etc. Choices are guided by study questions and issues, but researcher is open to broadening or narrowing focus Determine relevance of strings of data for your study at hand (fascinating does not make relevance)

9. Analysis Data Reduction Process Read all data Mark data that are relevant to your questions or issues Code the data Reduce the data to short descriptions Categorize the descriptions Note links between codes (pattern coding) The next step is to create data displays

10. Analysis Data Displays Data displays are an organized way of compressing information and assembling it in ways that help you draw conclusions Can be text, diagrams, charts, matrices They show systematic patterns and interrelationships of the “chunks of meaning” (codes) in the data Displaying will often reveal new connections and themes in the data beyond those already noticed Can display intra-case analysis and/or cross- case analysis

11. Analysis Conclusion Drawing and Verification As one creates and views displays, the salient components of meaning and activities become apparent. In descriptive analysis, the researcher tries to represent the data (meanings, observations) to readers in such a way that they will “understand” what the researcher “sees” in the data. In causal analysis, the researcher tries to link concepts in the data together to explain observed meanings or phenomena, and to represent that in such a way that readers will “understand” what the researcher “sees.” This stage relies very heavily on logical evaluation and systematic description

12. Analysis Conclusion Drawing and Verification The researcher must describe what he or she sees in the data. The researcher always refers back to the data displays and raw data as descriptions or causal statements are made. Systematic, organized, and good coding and notes will really pay off at this point, allowing efficient, accurate access to data Conclusions are made through the process of writing up (describing) what is in the data

13. Analysis Conclusion Drawing and Verification Tips for accurate description and causal statements Be very attentive to patterns and themes—how do specific items form a general idea? Make contrasts and comparisons Try weighing the prevalence of events, themes, concepts in your data Search for disconfirming information or negative evidence Resolve disconfirmations Account for the exceptions to your explanations Look for clustering Think of information like you would variables Search for systematic relationships, causality (as one thing goes up, the other goes down) Search for intervening variables

14. Analysis Conclusion Drawing and Verification Tips for accurate description and causal statements Build a logical chain of evidence Set up “if-then” models and see if they hold Think theoretically, metaphorically Triangulate Reflect on how your biases may alter interpretations Involve others in the analysis Generate and check rival explanations or meanings Get feedback from informants Give it the old “smell test” Can you go back to the data or notes to document how you came to your conclusions

15. Qualitative Papers Results Write what the reader should know about the data. Description Causal Logic Writing should have been occurring during analysis Document statements with exemplary quotes

16. Qualitative Papers Conclusions Answer your research question Discuss how you added to our knowledge Find areas of dis/agreement with literature Make suggestions for future research