دانلود کتاب A Step-by-Step Guide to Qualitative Data Coding

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Cover Half Title Title Page Copyright Page Dedication Brief contents Detailed contents Exhibits List of Figures List of Tables List of Boxes Acknowledgment 1 Introduction to qualitative data analysis Emergence of qualitative research as an acceptable research inquiry What is your paradigm? Assumptions associated with the interpretative paradigms Conclusion References 2 Review of qualitative approaches and their data analysis methods Qualitative approaches and their respective data analysis processes Phenomenological approach When to use the phenomenological approach Giorgi’s (1975) approach towards analyzing phenomenological data Hermeneutic phenomenological approach When to use the hermeneutic phenomenological approach Conducting hermeneutic phenomenological analysis Interpretative phenomenological analysis When to use interpretative phenomenological analysis Conducting interpretative phenomenological analysis at the data analysis stage Transcendental phenomenological approach When to use the transcendental phenomenological approach Conducting transcendental phenomenological analysis Ethnography When to use ethnography Conducting analysis of ethnographic data Narrative approach When to use the narrative approach Conducting narrative analysis Case study approach When to use the case study approach Conducting case study analysis Grounded theory approach When to use the grounded theory approach Conducting grounded theory analysis Conclusion References 3 Understanding the art of coding qualitative data What is qualitative coding? What comprises the art of coding? Systematic process Maintaining consistency Promoting repeatability Ensuring believability Data reduction Subjectivity Transparency Orientation to the art of qualitative coding Qualitative coding strategies Description-focused coding strategy Characteristics of description-focused coding When to use description-focused coding Demonstration 3.1 Interpretation-focused coding strategy Characteristics of interpretation-focused coding When to use interpretation-focused coding Demonstration 3.2 Presumption-focused coding strategy Characteristics of presumption-focused coding When to use presumption-focused coding Demonstration 3.3 Qualitative data for practice: Conclusion Note References 4 Preparing data to code What kind of data can be qualitatively analyzed? Transcribing audio files Using existing data for qualitative analysis demonstrations Using qualitative analysis tools Using manual qualitative analysis tools When to use manual qualitative analysis tools Using qualitative data analysis software (QDAS) When to use QDAS Conclusion References 5 Reflecting on, acknowledging and bracketing your perspectives and preconceptions Knowing the ‘self’ Knowing your perspectives Knowing your preconceptions Knowing your lens Bracketing your perspectives and preconceptions Importance of bracketing at the data analysis stage Steps in engaging in Epoché (bracketing) at the data analysis stage Disclosing your perspectives, preconceptions, lens and expectations Demonstration 5.1 Conclusion References 6 Documenting personal reflections and the analytical process Memo writing Importance of memo writing Types of memos Conclusion References 7 Manually assigning codes to data Overview Deciding on an appropriate coding strategy Demonstration 7.1 Assigning labels to the research questions Demonstration 7.2 Creating codes using the interpretation-focused coding strategy Demonstration 7.3 Compiling codes and tallying code frequencies Demonstration 7.4 Q&A about qualitative coding Conclusion References 8 Developing categories and themes Transforming codes into categories and themes Code transformation or categorization strategies Presumption-focused coding strategy Demonstration 8.1 Individual-based sorting strategy Demonstration 8.2 Group-based sorting strategy Demonstration 8.3 Conclusion References 9 Connecting themes, and developing tables and diagrams When to examine the relationships among categories/themes How relationships among categories/themes are built Demonstration 9.1 Using tables and diagrams Tools for designing tables and diagrams Demonstration 9.2 Demonstration 9.3 Demonstration 9.4 Conclusion References 10 Using QDA Miner Lite to analyze qualitative data QDA Miner Lite overview Preparing qualitative data Importing the interview transcripts Demonstration 10.1 Overview of the QDA Miner lite software interface (see Exhibit 10.4) Creating demographic variables and attributes Demonstration 10.2 Demonstration 10.3 Exploring data Coding empirical indicators Demonstration 10.4 Developing categories/themes Demonstration 10.5 List of themes and their respective codes for ‘GM concerns factors (RQ1)’ List of themes and their respective codes for ‘GM communication strategies (RQ3)’ Generating tables and diagrams Demonstration 10.6 Conclusion References 11 Using NVivo 12 to analyze qualitative data NVivo 12 software overview Preparing qualitative data Demonstration 11.1 Exploring data Demonstration 11.2 Coding empirical indicators Demonstration 11.3 Developing categories/themes Demonstration 11.4 Visualizing outcomes Demonstration 11.5 Exporting outcomes Demonstration 11.6 Conclusion References 12 Using Dedoose to analyze qualitative data Dedoose software overview Functions of Dedoose across qualitative analysis stages Demonstration 12.1 Preparing qualitative data Coding excerpts (empirical indicators) Demonstration 12.2 Rating excerpts (empirical indicators) Developing categories/themes List of themes and their respective codes for ‘GM concerns factors (RQ1)’ List of themes and their respective codes for ‘GM communication strategies (RQ3)’ Demonstration 12.3 Retrieving and exporting outcomes Conclusion References 13 Presenting qualitative findings Introduction Elements of a qualitative findings report Overview Example 13.1 Context of the data and findings Example 13.2 Disclosure of preconceptions, perspectives, lens and expectations Example 13.3 Data analysis process Example 13.4 Coding data Developing themes Presenting the main findings Findings presentation structures Example 13.5a (Individualized theme-driven format) Example 13.5b (Synthesized theme-driven format) Summary Example 13.6 Conclusion References 14 Ensuring the credibility of the analysis process and findings The concept of credibility Ensuring consistency Being systematic Promoting transparency Auxiliary credibility strategies Implementing member checking Utilizing theoretical sampling Actively involving participants Having a data analysis team Conclusion References Appendix A Appendix B Index