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QUALITATIVE DATA ANALYSIS

QUALITATIVE DATA ANALYSIS. Week 11. Lesson objectives. Review qualitative research Managing qualitative data Analysing qualitative data. What is Qualitative Research.

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QUALITATIVE DATA ANALYSIS

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  1. QUALITATIVE DATA ANALYSIS Week 11

  2. Lesson objectives • Review qualitative research • Managing qualitative data • Analysing qualitative data

  3. What is Qualitative Research • “….research that describes phenomena in words instead of numbers or measures….Quantitative research: research that describes phenomena in numbers and measures instead of words.” (Krathol: 1993)

  4. Why qualitative approach? • Not everything that counts can be counted, and not everything that can be counted counts -Albert Einstein

  5. Common features of Qualitative Research • In-depth and interpreted understanding of the social world of research subjects- how they make meanings of social circumstances, experiences and interactions in the ‘fields’ or life situations. • Describing social phenomena as they occur • Using data collection methods suitable to the social context (interactive, flexible and sensitive) • Maintaining close contacts between researcher and research subjects for emergent issues to be explored jointly. • Rich data- as perceived from ‘inside’- empathetic understanding without bias or preconceived ideas. • Producing detailed data, extensive, rich and inductively extracted. (Holmes, et.al. pg99)

  6. Types of Qualitative Research • Phenomenology • Grounded Theory • Ethnography • Case Study

  7. CASE STUDY/IES • A case study is an exploration of a “bounded system” or a case (multiple cases) over time through detailed, in-depth data collection involving multiple sources of information rich in context (Cresswell, 1998). • A case study design is employed to gain an in-depth understanding of the situation and meaning for those involved (Merriam, 2001)

  8. CASE STUDY/IES • The process of conducting a case study begins with the selection of the ‘case’. • The selection is done purposefully, not randomly; selection of a particular person, site, program, process, community or other bounded system because it exhibits characteristics of interest to the researcher. (Merriam, 2002)

  9. Quantitative Data analysis consist of statistical analysis Data analysis involves describing trends, comparing group differences or relating variables Interpretation compares results with prior predictions and past research Qualitative Data analysis consist of text analysis Data analysis involve describing the information and developing themes Interpretation situates the findings within the larger, more abstract meanings Analyzing and Interpreting Data

  10. Data Analysis: Qualitative Data • Definition: • An inductive process of organizing the data into categories and identifying patterns among themes and categories. • Data collected from interviews, observation and documents – in overwhelming volume

  11. Steps in Data Analysis • Generally these are the common steps in data analysis • Organizing data • Need to organize because of the volume and large amount of information • Organizing materials by type: all interviews, all observations, all documents, all photographs and visual materials • Organizing by participants, site, location etc. • Keeping duplicate copies of all forms of data

  12. Steps in Data Analysis • Transcribing Data • Process of converting audiotape recordings or fieldnotes into text data (written data) • Highlighting pauses, questions, clarification, intonation and voice modulation (lengthy break, silence, laugh, inaudible etc.)

  13. Steps in Data Analysis • Exploring Data • Preliminary exploring the data • Read several times- write memos/notes in the margin or hunches, ideas, concepts • Helps forms an initial analysis • Basis for general understanding

  14. Steps in Data Analysis • Describing and developing themes • Forming an in-depth understanding of the phenomenon under study • The processes from data to generating a description and themes are • Indexing /Coding data • Using code to develop themes • Connecting and interrelating themes

  15. (i) Coding Data • The process of segmenting and labeling text to form descriptions and broad themes in the data • The process of dividing data into parts by some classification system • Make sense of the data • The research questions and research concerns becomes the basis for generating themes/categories

  16. Steps in coding data Identifying text segments: • Tags/labels for assigning units of meanings • Codes are labels used to describe a segment of text • Assigning a code word or phrase that accurately describes the meaning of the text statement

  17. Steps in coding data Identifying text segments: • Codes can address many different topics: • Setting/ context code • Description of a school • Processes code – what happens • Sequence of events e.g disruptions • Periods, stages, phases

  18. Steps in coding data • Activities code • Regularly occurring e.g attendance, lunch breaks, working session/ classroom session starts and ends. • Relationship code • Friendships, enemies, relatives, superiors and subordinates

  19. Steps in coding data • Feelings code • Angry, happy, enthusiastic • Event code • Specific events • Unexpected/ significant • Riot, commotion, strike, disagreements

  20. (ii) Developing themes • After coding the entire text, make a list of all code words. • Cluster together similar codes • Look for redundant codes • Reduce codes to a more manageable number e.g 25-30 • Go back to data, look for new codes

  21. (ii) Developing themes • Identify themes from codes • Themes – similar codes put together to form a major idea/ perspective • Support with evidence/ specific quotes

  22. (iii) Connecting and interrelating themes • Minor themes are subsumed within major themes • Major themes lead to broader themes • Towards more abstraction • Display a chronology, sequence or patterns showing interconnection of themes • A pattern that explains the research questions • Linking the themes together in some meaningful way- to show interaction and interrelatedness of the findings (may use diagrams or model)

  23. Memorising Memorising for better understanding Gain knowledge Seek new knowledge Performing ritual Practical application Moral duty Muslims’ duty Good Muslim Obligatory learning God’s guidance To seek heaven/paradise Cleanse heart and soul To seek God’s pleasure Pattern of Themes: Learning Beliefs among Muslim adult learners in non-formal learning environment • Acquiring knowledge • (ii) Practical knowledge • (iv) Religious duty • (v) Spiritual pleasure • Temporal reasons/ belief • B. Temporal and Hereafter reasons/ belief • C. Godly and Hereafter reasons/ belief

  24. Pattern of Themes: Learning Beliefs among Muslim adult learners in non-formal learning environment Temporal and Hereafter reasons/ beliefs Temporal reasons/ beliefs Hereafter reasons/ beliefs LEARNING BELIEFS

  25. Let’s analyse some qualitative data

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