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

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Recording of presentation introducing narrative analysis, outlining what it is, why it can be a useful approach, how to do it and where to find out more. Presentation given at methods@manchester seminar at University of Manchester on 4 March 2010.

What is Narrative Analysis

Part I. What's the Problem?: 1. What are they shouting about 2. Lineaments of contention 3. Comparisons, mechanisms, and episodes Part II. Tentative Solutions: 4. Mobilizations in comparative perspective 5. Contentious action 6. Transformations of contention Part III. Applications and Conclusions: 7. Revolutionary trajectories 8. Nationalism, national disintegration, and contention 9. Contentious democratization 10. Conclusions.

Dynamics of Contention

Distinguishing four sources of power in human societies – ideological, economic, military and political – The Sources of Social Power traces their interrelations throughout human history In this first volume, Michael Mann examines interrelations between these elements from neolithic times, through ancient Near Eastern civilizations, the classical Mediterranean age and medieval Europe, up to just before the Industrial Revolution in England It offers explanations of the emergence of the state and social stratification; of city-states, militaristic empires and the persistent interaction between them; of the world salvation religions; and of the particular dynamism of medieval and early modern Europe It ends by generalizing about the nature of overall social development, the varying forms of social cohesion and the role of classes and class struggle in history First published in 1986, this new edition of Volume 1 includes a new preface by the author examining the impact and legacy of the work

The sources of social power

Strike Data in Search of a Theory: The Italian Case in the Postwar Period:

Is a third passage to the past possible, beyond Elton's and Fogel's two roads of narrative history and scientific/quantitative history? One that would combine narrative history's focus on the event, on individuals and their actions, at a particular time and place, to scientific/quantitative history's emphasis on explicit behavioral models based on social-science theories? That is the question this article addresses. It illustrates a computer-assisted methodology for the study of narrative—quantitative narrative analysis (QNA)—that does just that. Based on the 5 Ws + H of narrative—Who, What, When, Where, Why, and How—QNA quantifies events without losing the event itself, without losing people behind numbers, diachronic time behind synchronic statistical coefficients. When used in conjunction with dynamic and interactive data visualization tools (and new natural language processing tools), QNA may provide a third unforeseen road to the past.

A third road to the past? Historical scholarship in the age of big data

On July 9–10, 2012, Professor Martin Bauer organized a third conference on text mining at the London School of Economics. Invited participants came from computer science and different social science disciplines. They were united by a common interest in texts and the extraction of meaning from those texts. They were divided by the way they approached those texts—their methods, a marker of more fundamental epistemological differences. Social scientists ranged in their approach. There are the content analysts who rely on CAQDAS packages to deal with text, either qualitatively, employing CAQDAS only as an organizational tool, or quantitatively, querying the codes they produce in CAQDAS for cross-relations, exporting the results, and analyzing these in statistical packages (SPSS, STATA, R), as do Michael White, Maya Judd, and Simone Poliandri (this volume, 2012:43–76). There are those who distance themselves from the text, relying on semi-automated approaches in the French tradition of analyse des données (e.g., using such software as Alceste or Lebart’s Dtm-Vic). The larger the body of text to be analyzed, the more likely social scientists are to look for automatic, quantitative solutions. And that’s where social and computer scientists meet. The textual analysis tools that computer science data miners have developed tend to work well only when dealing with ‘‘millions of words,’’ as Professor Nello Cristianini, a computer scientist at Bristol, UK, put it at the conference: ‘‘Modern AI is driven by large text corpora.’’ Their algorithms do allow them to reveal important properties of content, whether about topic, style, or even narrative structure. It is that narrative structure (the sequence of SVO of actors and their actions), that I painfully extract from text by hand, in my Quantitative Narrative Analysis approach—an approach that falls squarely in the category of those who treat text qualitatively but analyze the results quantitatively. Contrary to purely qualitative scholars, I am hoping (and optimistic) that computer scientists will one day put us out of our misery of hand coding. Thus, ultimately, I share what the authors write about CAQDAS approaches: that ‘‘the researcher always has ready access to the underlying qualitative data (e.g.,

Roberto Franzosi

Papers

Strike Data in Search of a Theory: The Italian Case in the Postwar Period:

A third road to the past? Historical scholarship in the age of big data

The Difficulty of Mixed-Method Approaches

Strikes in Australia.

Historical Knowledge and Evidence