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Research Paper Writing

Research Paper Writing. Mavis Shang 97 年度第二學期. Section VII. Data Analysis, Interpretation, and Reporting. I. Qualitative analytic strategies: A. Recursive process: → analyze cases → generate findings→ draw conclusion → form grounded theory → write report

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Research Paper Writing

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  1. Research Paper Writing Mavis Shang 97年度第二學期 1 Section VII

  2. Data Analysis, Interpretation, and Reporting I. Qualitative analytic strategies: A. Recursive process: → analyze cases→ generate findings→ draw conclusion → form grounded theory → write report B. Nine qualitative data analysis principles: 1. Collect the data at the site and carefully study all the data to seek similarities and differences, concepts and reflections 2

  3. Data Analysis, Interpretation, and Reporting 2. Saturation and sufficiency of information: Never stop data analysis until the emergence of regularities; i.e., no new information emerges with additional data analysis 3. Accountability of information: Keep notes or transcripts if outsiders want to review the data analysis procedures and results 4. Divide the data (excerpts) into smaller units related to your major points 3

  4. Data Analysis, Interpretation, and Reporting 5. Organize the smaller units into categories (based on major points) 6. Build conceptual similarities, find negative evidences, and discover patterns 7. Modify categories as further patterns occur 8. Analyze negative cases to reflect their perceptions 9. Synthesize the patterns into the grounded theory 4

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  6. * SHOULD BE: - connected with what is being discussed in the major points - exact wording (excerpts) used in the statement * SHOULD NOT BE: - based on interviewer’s personal opinions - irrelevant to the major points Data Analysis, Interpretation, and Reporting 6

  7. Data Analysis, Interpretation, and Reporting • C. Six Steps in Qualitative Data Analysis: • Give codes from the notes (transcripts) • Note personal reflections or other minority’s comments • Sort the notes to identify similar and different relationships between patterns • Identify these patterns, similarities, and differences • Elaborate a small set of generalizations that cover the consistencies • Examine those generalizations and form grounded theory • (see “Content Analysis”) 7

  8. Data Analysis, Interpretation, and Reporting Grounded Theory (紮根理論): → a process of constructing various data → inductive process by collecting, analyzing, and comparing data systematically → theory is grounded on data to explain the phenomena 8

  9. Grounded Theory (by Marlene Pomrenke) Grounded theory is a method of social inquiry associated with a qualitative approach to research. This inductive research process utilizes generalized knowledge that is derived from specific observations of phenomena from the field. In turn, this can be used to build theory. For example, grounded theorists aim to create theoretical categories from collected data and then analyze relationships between key categories (Charmaz, 1990). Indeed, the main purpose of using a grounded theory approach is to develop theory through understanding concepts that are related by means of statements of relationships (Strauss & Corbin, 1990). Using the concepts from grounded theory, this study starts from understanding the experience of the research participants (i.e., how they construct their worlds). The data analysis stage focused on finding recurrent themes or issues in the data, and finally into developing or refining a theory about the phenomenon. 9

  10. Data Analysis, Interpretation, and Reporting D. Grounded Theory Analysis Strategies: 1. Recur by moving back and forth with the data, analyzing, collecting more data, and analyzing some more until reaching conclusions 2. An interactional method of theory building by comparing and analyzing the data 10

  11. Data Analysis, Interpretation, and Reporting 3. Three steps in the grounded theory analytic (coding) process: (1) Open coding: Break data into small parts → compare for similarities and differences → explain the meanings of the data by focusing on “who, when, where, what, how much, why” (ask questions to get a clear story) (2) Axial coding: After open coding, make connections (sort) between categories and confirm or disconfirm your hypotheses (3) Selective coding: Select the core category (match hypotheses) and explain the minor category (against hypotheses) with additional supporting data 11

  12. Coding Process • Open Coding MP 1 MP 2 MP 3 • Axial Coding • Selective Coding MP 1 MP 3 12

  13. Participant #1 Ah…I do not think I improve grammar and word dictions because my teacher did not correct my grammar and word dictions.Actually, I know I am not good at writing, and I really want to improve mywriting ability.Hmm……However, I also wrote articles which were asked from professors as homework while I wrote dialogue journal writing. Well, for the first time, I can accept that I had so many writing mistakes, and I know I still have room to improve itafter teacher’s correction.Unfortunately, after many times corrections, the articles which were corrected by professors still appeared many grammar problems and sometimes had word dictions problems. This iswhyI do not think dialogue journal writing can improve our writing ability.(Shake head) 13

  14. Data Analysis, Interpretation, and Reporting II. Interpretation Issues in Qualitative Data analysis: A. Triangulating data: Use multiple methods and data sources to support the strength of interpretations and conclusion (e.g., semi- structured interviews, consent form, grounded theory, etc.) 14

  15. Data Analysis, Interpretation, and Reporting B. Audits: Questions to examine the data for interpretations and conclusion 1. Is sampling appropriate to ground the findings? 2. Are coding strategies applied correctly? 3. Is the category process appropriate? 4. Do the results link hypotheses? 5. Are the negative cases explained? 15

  16. Data Analysis, Interpretation, and Reporting * Four steps of negative case testing: 1. Make a rough hypothesis 2. Conduct a thorough search 3. Discard or reformulate hypothesis 4. Examine all relevant cases 16

  17. Data Analysis, Interpretation, and Reporting C. Cultural bias: Discuss cultural differences with different groups of participants (compare the differences between western and Taiwanese students’ attitudes) D. Generalization: Not appropriate for qualitative research 1. Case-to-case transferability by providing thick description to apply to another setting 2. Generalize the result to a broader theory (e.g., use deviant cases) 17

  18. Data Analysis, Interpretation, and Reporting III. Writing Research Reports: A. Introduction B. Literature Review C. Methodology D. Results: Tie the results to study purpose (hypotheses) E. Discussions and Conclusion: Tie discussions to the literature; recommendations for practice; limitations of the study 18

  19. Data Analysis, Interpretation, and Reporting • Quantitative reports: • Report results by the use of tables and graphs • Avoid first-person pronoun • Use passive voice • Qualitative reports: • Look for a deep description (narrative style) • Look for well-grounded theory • Seek contextual meaning by understanding demographic • information (different experiences) 19

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