Janice M. Morse

Bio: Janice M. Morse is an academic researcher from University of Utah. The author has contributed to research in topics: Qualitative research & Grounded theory. The author has an hindex of 79, co-authored 399 publications receiving 55188 citations. Previous affiliations of Janice M. Morse include Pace University & University of York.

Verification Strategies for Establishing Reliability and Validity in Qualitative Research

Abstract: The rejection of reliability and validity in qualitative inquiry in the 1980s has resulted in an interesting shift for "ensuring rigor" from the investigator’s actions during the course of the research, to the reader or consumer of qualitative inquiry. The emphasis on strategies that are implemented during the research process has been replaced by strategies for evaluating trustworthiness and utility that are implemented once a study is completed. In this article, we argue that reliability and validity remain appropriate concepts for attaining rigor in qualitative research. We argue that qualitative researchers should reclaim responsibility for reliability and validity by implementing verification strategies integral and self-correcting during the conduct of inquiry itself. This ensures the attainment of rigor using strategies inherent within each qualitative design, and moves the responsibility for incorporating and maintaining reliability and validity from external reviewers’ judgements to the investigators themselves. Finally, we make a plea for a return to terminology for ensuring rigor that is used by mainstream science.

README FIRST for a User's Guide to Qualitative Methods

Abstract: Preface Why Readme First? Goals Methods and Their Integrity Methodological Diversity and Informed Choice No Mysteries! Learning by Doing It: Qualitative Research as a Craft Qualitative Research as a Challenge Using Readme The Shape of the Book What to Expect Doing Qualitative Research Resources Thinking Research The Integrity of Qualitative Research Methodological Purposiveness Methodological Congruence The Armchair Walkthrough And Now-Your Topic? What can you aim for? Summary Resources Selecting a Method Commonalities and Differences Phenomenology Ethnography Grounded Theory Additional Qualitative Methods Summary Resources Qualitative Research Design The Levels of Design Planning Design Doing Design Designing for Validity Project Pacing Choosing Your Software Overall Project Design Combining Qualitative and Quantitative Projects Using Your Software for Research Design Summary Resources Inside Analysis Making Data What Are Data (and What Are Not) Ways of Making Data Who Makes Data? Transforming Data Managing Data The Role of Data Yourself as Data Using Your Software for Managing Data Summary Resources Coding Getting Inside the Data Storing Ideas Doing Coding Theme-ing Purposiveness of Coding Tips and Traps: Handling Codes and Coding Using Your Software for Coding Summary Resources Abstracting The First Step: Categorizing The Next Step: Conceptualizing Doing Abstraction Managing Abstraction Using Your Software for Managing Ideas Summary Resources Revisiting Methodological Congruence Phenomenology Ethnography Grounded Theory Summary Resources Getting It Right On Getting It Right and Knowing If It's Wrong Ensuring Rigor in the Design Phase Ensuring Rigor While Conducting a Project When Is It Done? Demonstrating Rigor on Completion of the Project Summary Resources Writing It Up Ready to Write? Re-revisiting Methodological Congruence Protecting Participants Evaluate Your Writing Polishing Using your Software for Writing Writing your Thesis or Dissertation Writing an Article for Publication After Publication, Then What? Summary Resources Beginning Your Project Groundwork for Beginning Your Project Writing Your Proposal Ensuring Ethical Research Summary Resources Getting Started Why Is It So Hard to Start? How to Start? Congratulations, You've Started! Resources Appendix 1: Using the Software Tutorials Appendix 2: Applying for funding

Three Approaches to Qualitative Content Analysis

Abstract: Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.

The Coding Manual for Qualitative Researchers

Abstract: 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 qualitative content analysis process

Abstract: Aim This paper is a description of inductive and deductive content analysis. Background Content analysis is a method that may be used with either qualitative or quantitative data and in an inductive or deductive way. Qualitative content analysis is commonly used in nursing studies but little has been published on the analysis process and many research books generally only provide a short description of this method. Discussion When using content analysis, the aim was to build a model to describe the phenomenon in a conceptual form. Both inductive and deductive analysis processes are represented as three main phases: preparation, organizing and reporting. The preparation phase is similar in both approaches. The concepts are derived from the data in inductive content analysis. Deductive content analysis is used when the structure of analysis is operationalized on the basis of previous knowledge. Conclusion Inductive content analysis is used in cases where there are no previous studies dealing with the phenomenon or when it is fragmented. A deductive approach is useful if the general aim was to test a previous theory in a different situation or to compare categories at different time periods.

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

Abstract: 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...