Bobby Milstein

Bio: Bobby Milstein is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Public health & Health equity. The author has an hindex of 24, co-authored 38 publications receiving 3467 citations. Previous affiliations of Bobby Milstein include University of California, Los Angeles & Centers for Disease Control and Prevention.

Codebook Development for Team-Based Qualitative Analysis:

Abstract: One of the key elements in qualitative data analysis is the systematic coding of text (Strauss and Corbin 1990:57%60; Miles and Huberman 1994:56). Codes are the building blocks for theory or model building and the foundation on which the analyst’s arguments rest. Implicitly or explicitly, they embody the assumptions underlying the analysis. Given the context of the interdisciplinary nature of research at the Centers for Disease Control and Prevention (CDC), we have sought to develop explicit guidelines for all aspects of qualitative data analysis, including codebook development.

Practical Challenges of Systems Thinking and Modeling in Public Health

Abstract: Objectives. Awareness of and support for systems thinking and modeling in the public health field are growing, yet there are many practical challenges to implementation. We sought to identify and describe these challenges from the perspectives of practicing public health professionals.Methods. A systems-based methodology, concept mapping, was used in a study of 133 participants from 2 systems-based public health initiatives (the Initiative for the Study and Implementation of Systems and the Syndemics Prevention Network). This method identified 100 key challenges to implementation of systems thinking and modeling in public health work.Results. The project resulted in a map identifying 8 categories of challenges and the dynamic interactions among them.Conclusions. Implementation by public health professionals of the 8 simple rules we derived from the clusters in the map identified here will help to address challenges and improve the organization of systems that protect the public’s health.

Systems thinking and modeling for public health practice.

Abstract: Many public health workers will regard this issue of the Journal, devoted to the theme of systems thinking and modeling, as a welcome affirmation that our endeavors to protect the public’s health do indeed depend on more than the sum of their parts. As Midgley observes, “The whole concept of public health is founded on the insight that health and illness have causes or conditions that go beyond the biology and behavior of the individual human being.”1(p466) Animated by this systemic insight, public health leaders have worked for more than a century to identify and transform the processes that leave people vulnerable to afflictions of all sorts. As this work has evolved, our understanding of population health dynamics—and of our power to navigate change—has improved through innovations in the concepts, methods, and moral frameworks that shape the field. The present exploration of systems thinking and modeling, therefore, springs from the very core of our discipline, adding to our repertoire novel and far-reaching tools that the pioneers of public health work could scarcely have imagined.

Understanding Diabetes Population Dynamics Through Simulation Modeling and Experimentation

Abstract: Health planners in the Division of Diabetes Translation and others from the National Center for Chronic Disease Prevention and Health Promotion of the Centers for Disease Control and Prevention used system dynamics simulation modeling to gain a better understanding of diabetes population dynamics and to explore implications for public health strategy. A model was developed to explain the growth of diabetes since 1980 and portray possible futures through 2050. The model simulations suggest characteristic dynamics of the diabetes population, including unintended increases in diabetes prevalence due to diabetes control, the inability of diabetes control efforts alone to reduce diabetes-related deaths in the long term, and significant delays between primary prevention efforts and downstream improvements in diabetes outcomes.

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

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...

Techniques to Identify Themes

Abstract: Theme identification is one of the most fundamental tasks in qualitative research. It also is one of the most mysterious. Explicit descriptions of theme discovery are rarely found in articles and reports, and when they are, they are often relegated to appendices or footnotes. Techniques are shared among small groups of social scientists, but sharing is impeded by disciplinary or epistemological boundaries. The techniques described here are drawn from across epistemological and disciplinary boundaries. They include both observational and manipulative techniques and range from quick word counts to laborious, in-depth, line-by-line scrutiny. Techniques are compared on six dimensions: (1) appropriateness for data types, (2) required labor, (3) required expertise, (4) stage of analysis, (5) number and types of themes to be generated, and (6) issues of reliability and validity.