An Introduction to Codes and Coding Chapter Summary Purposes of the Manual What Is a Code? Codifying and Categorizing What Gets Coded? The Mechanics of Coding The Numbers of Codes Manual and CAQDAS Coding Solo and Team Coding Necessary Personal Attributes for Coding On Method Writing Analytic Memos Chapter Summary The Purposes of Analytic Memo-Writing What Is an Analytic Memo? Examples of Analytic Memos Coding and Categorizing Analytic Memos Grounded Theory and Its Coding Canon Analytic Memos on Visual Data First-Cycle Coding Methods Chapter Summary The Coding Cycles Selecting the Appropriate Coding Method(s) Overview of First-Cycle Coding Methods The Coding Methods Profiles Grammatical Methods Elemental Methods Affective Methods Literary and Language Methods Exploratory Methods Forms for Additional First-Cycle Coding Methods Theming the Data Procedural Methods After First-Cycle Coding Chapter Summary Post-Coding Transitions Eclectic Coding Code Mapping and Landscaping Operational Model Diagramming Additional Transition Methods Transitioning to Second-Cycle Coding Methods Second-Cycle Coding Methods Chapter Summary The Goals of Second-Cycle Methods Overview of Second-Cycle Coding Methods Second-Cycle Coding Methods Forms for Additional Second-Cycle Coding Methods After Second-Cycle Coding Chapter Summary Post-Coding and Pre-Writing Transitions Focusing Strategies From Coding to Theorizing Formatting Matters Writing about Coding Ordering and Re-Ordering Assistance from Others Closure Appendix A: A Glossary of Coding Methods Appendix B: A Glossary of Analytic Recommendations Appendix C: Field Note, Interview Transcript and Document Samples for Coding Appendix D: Exercises and Activities for Coding and Qualitative Data Analytic Skill Development References Index

The Coding Manual for Qualitative Researchers

Guidelines for determining nonprobabilistic sample sizes are virtually nonexistent. Purposive samples are the most commonly used form of nonprobabilistic sampling, and their size typically relies on the concept of “saturation,” or the point at which no new information or themes are observed in the data. Although the idea of saturation is helpful at the conceptual level, it provides little practical guidance for estimating sample sizes, prior to data collection, necessary for conducting quality research. Using data from a study involving sixty in-depth interviews with women in two West African countries, the authors systematically document the degree of data saturation and variability over the course of thematic analysis. They operationalize saturation and make evidence-based recommendations regarding nonprobabilistic sample sizes for interviews. Based on the data set, they found that saturation occurred within the first twelve interviews, although basic elements for metathemes were present as early as six...

https://www.rincondepaco.com.mx/rincon/Inicio/Seminario/Documentos/Met_otros/Docs_Dra_Fleiz/Lecturas_sugeridas/Guest_data_saturation_interviews.pdf

How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability

As qualitative research becomes increasingly recognized and valued, it is imperative that it is conducted in a rigorous and methodical manner to yield meaningful and useful results. To be accepted ...

https://journals.sagepub.com/doi/pdf/10.1177/1609406917733847

Thematic Analysis: Striving to Meet the Trustworthiness Criteria

Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears to be used most commonly in implementation research. However, combining sampling strategies may be more appropriate to the aims of implementation research and more consistent with recent developments in quantitative methods. This paper reviews the principles and practice of purposeful sampling in implementation research, summarizes types and categories of purposeful sampling strategies and provides a set of recommendations for use of single strategy or multistage strategy designs, particularly for state implementation research.

/pdf/purposeful-sampling-for-qualitative-data-collection-and-2zi83jnr1f.pdf

Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research.

Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation—as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

https://link.springer.com/content/pdf/10.1007%2Fs11135-017-0574-8.pdf

Saturation in qualitative research: exploring its conceptualization and operationalization

Chapter 1. Introduction to Applied Thematic Analysis Chapter 2. Planning and Preparing the Analysis Chapter 3. Themes and Codes Chapter 4. Validity and Reliability (Credibility and Dependability) in Qualitative Research and Data Analysis Chapter 5. Supplemental Analytic Techniques Chapter 6. Data Reduction Techniques Chapter 7. Comparing Thematic Data Chapter 8. Integrating Qualitative and Quantitative Data Chapter 9. Choosing Qualitative Data Analysis Software Chapter 10. Writing Up Thematic Analyses

Applied Thematic Analysis

Chapter 1. Qualitative Research: Defining and Designing Chapter 2. Sampling Chapter 3. Participant Observation Chapter 4. In-Depth Interviews Chapter 5. Focus Groups Chapter 6. Additional Qualitative Data Collection Methods Chapter 7. Qualitative Data Management Chapter 8. Ethical Dimensions of Qualitative Research

Collecting Qualitative Data: A Field Manual for Applied Research

In-Depth Interviews

Few empirical studies exist to guide researchers in determining the number of focus groups necessary for a research study. The analyses described here provide foundational evidence to help research...

Greg Guest

Papers

How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability

Applied Thematic Analysis

Collecting Qualitative Data: A Field Manual for Applied Research

In-Depth Interviews

How Many Focus Groups Are Enough? Building an Evidence Base for Nonprobability Sample Sizes