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Research Methods in Education 6th Edition

Research Methods in Education 6th Edition

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  • 2. STRUCTURE OF THE CHAPTER • Data analysis, thick description and reflexivity • Ethics in qualitative data analysis • Computer assisted qualitative data analysis (CAQDAS)
  • 3. QUALITATIVE DATA • There is no one single or correct way to analyze and present qualitative data – Abide by fitness for purpose. • Qualitative data analysis is often heavy on interpretation, with multiple interpretations possible. • Data analysis and interpretation may often merge. • Data analysis often commences early. • Results of the analysis also constitute data for further analysis.  
  • 4. TO TRANSCRIBE OR NOT TO TRANSCRIBE INTERVIEWS • Transcriptions can provide important detail and an accurate verbatim record of the interview. • Transcriptions may omit non-verbal aspects, and contextual features of the interview. Transcriptions are very time consuming to prepare. • Transcriptions must clarify conventions used.
  • 5. DATA ANALYSIS, THICK DESCRIPTION AND REFLEXIVITY • Fitness for purpose: – To describe; – To portray; – to summarize; – to interpret; – to discover patterns; – to generate themes; – to understand individuals and idiographic features; – to understand groups and nomothetic features.
  • 6. DATA ANALYSIS, THICK DESCRIPTION AND REFLEXIVITY • Fitness for purpose: – to raise issues; – to prove or demonstrate; – to explain and seek causality; – to explore; – to test; – to discover commonalities, differences and similarities; – to examine the application and operation of the same issues in different contexts.
  • 7. QUALITATIVE DATA ANALYSIS • The movement is from description to explanation and theory generation. • Problems of data overload: data reduction and display become important. • Double hermeneutic: the researcher interprets and already-interpreted world. • The researcher is part of the world that is being interpreted, therefore reflexivity is required. • Subjectivity is inescapable. • The researcher’s own memory may be fallible, selective and over-interpreting a situation.
  • 8. QUALITATIVE DATA ANALYSIS • Use a range of data and to ensure that these data include the views of other participants in a situation. • Address reflexivity. • The analysis becomes data in itself, for further analysis (e.g. for reflexivity).
  • 9. RESPONDENT VALIDATION • Respondent validation may be problematic as participants: – May change their minds as to what they wished to say, or meant, or meant to say but did not say, or wished to have included or made public; – May have faulty memories and recall events over- selectively, or incorrectly, or not at all; – May disagree with the researcher’s interpretations; – May wish to withdraw comments made in light of subsequent events in their lives; – May have said what they said in the heat of the moment or because of peer pressure or authority pressure; – May feel embarrassed by, or nervous about, what they said.
  • 10. ETHICS IN QUALITATIVE DATA ANALYSIS • Identifiability, confidentiality and privacy of individuals • Non-maleficence, loyalties (and to whom), and beneficence
  • 11. COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS (CAQDAS) • To make notes • To transcribe field notes and audio data • To manage and store data in an ordered and organized way • For search and retrieval of text, data and categories • To edit, extend or revise field notes • To code and arrange codes into hierarchies (trees) and nodes (key codes) • To conduct content analysis • To store and check data • To collate, segment and copy data
  • 12. COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS (CAQDAS) • To enable memoing, with details of the circumstances in which the memos were written • To attach identification labels to units of text • To annotate and append text • To partition data into units • To sort, re-sort, collate, classify and reclassify pieces of data to facilitate constant comparison and to refine schemas of classification • To assemble, re-assemble, recall data into categories • To display data in different ways
  • 13. COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS (CAQDAS) • To cross-check data to see if they can be coded into more than one category, enabling linkages between categories and data to be found • To establish the incidence of data that are contained in more than one category • To search for pieces of data which appear in a certain sequence • To filter, assemble and relate data according to preferred criteria • To establish linkages between coding categories
  • 14. COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS (CAQDAS) • To display relationships of categories • To draw and verify conclusions and hypotheses • To quote data in the final report • To generate and test theory • To communicate with other researchers or participants
  • 15. TYPES OF CAQDAS SOFTWARE • Those that act as word processors • Those that code and retrieve text • Those that manage text • Those that enable theory building • Those that enable conceptual networks to be plotted and visualized • Those that work with text only • Those that work with images, video and sound
  • 16. SOFTWARE FUNCTIONS • Search for and return text, codes, nodes and categories; • Search for specific terms and codes, singly or in combination; • Filter text; • Return counts; • Present the grouped data according to the selection criterion desired, both within and across texts;
  • 17. SOFTWARE FUNCTIONS • Perform the qualitative equivalent of statistical analyzes, such as: ─ Boolean searches ─ Proximity searches ─ Restrictions, trees, crosstabs • Construct dendrograms of related nodes and codes; • Present data in sequences and locate the text in surrounding material in order to provide the necessary context;
  • 18. SOFTWARE FUNCTIONS • Locate and return similar passages of text; • Look for negative cases; • Look for terms in context (lexical searching); • Select text on combined criteria; • Enable analyzes of similarities, differences and relationships between texts and passages of text; • Annotate text and enable memos to be written about text.
  • 19. CONCERNS ABOUT CAQDAS • Researchers may feel distanced from their data • Software is too strongly linked to grounded theory rather than other forms of qualitative data analysis • Software is best suited to data which require coding and categorization for developing grounded theory • Too heavy a focus on coding and retrieving • Removes data from context • The software drives the analysis rather than vice versa • Relegates the real task of hermeneutic understanding