Qualitative data analysis
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The document discusses data analysis techniques for quantitative, qualitative, and mixed research. It covers methods for collecting and analyzing both numerical and non-numerical data, emphasizing the use of computer software for analysis and highlighting the importance of validity in qualitative research. Furthermore, it outlines processes such as coding, identifying relationships among categories, and strategies for corroborating results.
Qualitative data analysis
- 1. DATA ANALYSIS Dr Ramakanta Mohalik
- 2. Points to be Discussed • Data Analysis for Quantitative Research • Data Analysis for Qualitative Research • Data Analysis for Mixed Research
- 3. Research Continuum Research Continuum Quantitative Mixed Qualitative
- 4. A. Quantitative Research • It relies on collection of quantitative data (numerical data) from tests, scales and checklist. • The research method like experimental, causal comparative, correlational, • Examples: Impact of cooperative learning on achievement in social science at secondary level.
- 5. A. Quantitative Research • There are various statistical techniques such as Descriptive and Inferential are used for analyzing data. • The Computer software like MS EXCEL and SPSS are useful for quantitative analysis.
- 6. B. Qualitative Research • It relies on the collection of qualitative data (non numerical data) such as words and pictures. • Data are collected from interviews, observation, focus group discussion, documents, physical artifacts, field notes etc . • The research like phenomenological, ethnography, case study, grounded theory, historical
- 7. B. Process of Qualitative Data Analysis • Examples: Identification of professional needs of teachers at secondary level. An analysis of in- service teacher education programmes organised by SCERT, Odisa • Qualitative data can be analysed by use of Computer Aided methods for Qualitative Data Analysis (CAQDAS). The software like Lexical analysis of SphinxSurvey, Non-Numerical Unstructured Data Indexing Searching and Theorising (NUD-IST), ATLAS.ti
- 8. Data collection Constructing diagrams, Tables graphs Corroborating & Validating result Data entry & storage Segmenting, coding & Developing category system Identify relationship Themes, pattern
- 9. B1. Data collection • It relies on the collection of qualitative data (non numerical data) such as words and pictures. • Data are collected from interviews, observation, focus group discussion, documents, physical artifacts, field notes etc
- 10. B2. Data entry & Storage • The data collected by using different tools to be Transcribe for analysis. • Transcription is the process of transforming qualitative data such as audio recordings of interviews or field notes written from observation into typed text. The typed text is known as Transcript. • The researcher can use Voice recognition computer programme ( Via Voice and Dragons Naturally Speaking) for transcription
- 11. B3. Segmenting, Coding and Developing Category System • The text data is to be segmented for analysis. Segment is a meaningful unit, may be a word, a single sentence or several sentences or larger paragraph. • Segmenting is process of dividing the text data into meaningful analytical units.
- 12. B3. Coding and Developing Category System • Coding is the process of marking segments of data with symbols, descriptive words or category names. • How to develop codes? There are two kinds of codes-Inductive and A priori codes. • Inductive codes: are generated by the researcher by directly examining the data during the coding process.
- 13. B3. Coding and Developing Category System • A priori codes: are developed before examining the current data may be before data collection. • Categories are basic building blocks of qualitative data analysis. It is a point or themes of researcher interest. Category consist of many segments. Researcher may think for sub-categories under each category. For eg Teaching-qualities of teacher, students, teaching aid
- 14. B4.Identifying Relationship among Categories • After categorizing the data, researcher need to think for relationship among different categories and sub-categories. • For qualitative researcher relationship refers to many kinds of relations or connections including but not limited to variables. • Many kinds of relationship is possible like
- 15. B4.Different Relationship among CategoriesTitle • Strict inclusion • Spatial • Cause-effect • Rationale • Location for action • Function • Means and end • Sequence • Attribution Form of relationship • X is a kind of Y • X is a place in Y, X is part • X is result of Y • X is reason for doing Y • X is place for doing Y • X is used for doing Y • X is way to do Y • X is a step in Y • X is an attribute of Y
- 16. B5.Drawing Diagrams • A useful tool for showing relationship among categories is called Diagramming. • Diagram is a plan, sketh,drawing, or outline designed to demonstrate or explain how something works or to clarify the relationship between the parts of a whole. • The diagrams such as Network diagram, Flow charts
- 17. B6. Corroborating and Validating Results • As qualitative research is subjective, influenced by researcher bias, its validity must be established. • There are different strategies to promote validity.
- 18. B6.Validating Results Strategy 1. Researcher as detective 2. Extended field work 3. Low inference descriptors 4. Triangulation (data/method/investi gator/theory) Description 1. Careful consideration of potential cause and effect and systematically eliminating rival explanation until final case is made 2. Collect data over an extended time period 3. Direct quotations/participants account/field notes are commonly used low inference 4. Cross checking information and conclusion through use of procedures or sources
- 19. B6.Validating Results Strategy 5. Participant feedback 6. Peer review 7. External audit 8. Reflexivity Description 5. The feedback and discussion of researchers interpretation and conclusion with actual participants 6. Discussion with peers who are familiar/unfamiliar with research help to provide check 7. Using outside expert to assess study quality 8. Self awareness & critical reflection by researcher on his potential biases and predispositions.
- 20. C.Data Analysis in Mixed Research • Here researcher uses both quantitative and qualitative analytical techniques in a single study. • Researcher may utilise both technique at same time (concurrently) or different times (sequentially)