Qualitative-Data-Analysis-Coding-and-Categorizing-Data.pdf
- 1. Qualitative Data Analysis: Coding and Categorizing Data Understanding how to transform raw data into meaningful insights. Presented by: Minal Sanjay Patil Class: SIBMT (Div-A) Roll No: 44
- 2. Introduction to Qualitative Data Analysis What is Qualitative Data? Non-numerical data including text, audio, images, or observations. Importance in Research Explores human experiences, meanings, and social phenomena deeply. Analysis Process Collect, code, categorize, and interpret to generate insights.
- 3. What is Coding in Qualitative Research? Definition Coding is labeling data segments to organize information. Purpose Transforms raw text into manageable categories for analysis. Examples Interview transcripts Open-ended survey responses
- 4. Types of Coding 1 2 3 Open Coding Initial identification of concepts and labels. Axial Coding Linking codes by relationships and categories. Selective Coding Developing core themes and narratives.
- 5. Manual vs. Software-Based Coding Manual Coding Using printed texts or spreadsheets for hands-on labeling. Software Tools NVivo, Atlas.ti, MAXQDA streamline and organize coding efficiently.
- 6. Steps in Coding Qualitative Data 1 Familiarize Read and immerse in data carefully. 2 Initial Coding Assign labels to important data segments. 3 Identify Themes Look for patterns and connections among codes. 4 Review & Refine Adjust codes to ensure consistency and clarity.
- 7. Categorizing Data Group Codes Combine related codes into broader categories for clarity. Data Interpretation Summarize findings by category to extract meaning. Example Codes like stress, fatigue, burnout grouped as work pressure.
- 8. Benefits of Coding and Categorizing Structure Data Organizes unstructured text into meaningful segments. Reveal Insights Uncovers hidden patterns and meanings within data. Support Theory Informs development of new theories or models.
- 9. Challenges in Qualitative Coding Subjectivity Interpretations may vary between coders affecting consistency. Time Intensive Coding large volumes of data can be slow and demanding. Maintaining Consistency Requires clear protocols to ensure reliable and valid coding.
- 10. Next Steps and Key Takeaways Practice Coding Engage hands-on with sample qualitative data sets. Use Software Explore NVivo or similar tools to improve efficiency. Stay Consistent Develop and follow detailed coding guidelines. Reflect & Refine Regularly review codes to enhance data insight quality.