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.

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Three Approaches to Qualitative Content Analysis

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

http://www.bwgriffin.com/gsu/courses/edur7130/content/Qualitative_content_analysis_Graneheim_Lundman_2003.pdf

Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness

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.

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The qualitative content analysis process

How to Do Things With Words

History Conceptual Foundations Uses and Kinds of Inference The Logic of Content Analysis Designs Unitizing Sampling Recording Data Languages Constructs for Inference Analytical Techniques The Use of Computers Reliability Validity A Practical Guide

Content analysis: an introduction to its methodology

In content analysis and similar methods, data are typically generated by trained human observers who record or transcribe textual, pictorial, or audible matter in terms suitable for analysis. Conclusions from such data can be trusted only after demonstrating their reliability. Unfortunately, the content analysis literature is full of proposals for so-called reliability coefficients, leaving investigators easily confused, not knowing which to choose. After describing the criteria for a good measure of reliability, we propose Krippendorff's alpha as the standard reliability measure. It is general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data. To facilitate the adoption of this recommendation, we describe a freely available macro written for SPSS and SAS to calculate Krippendorff's alpha and illustrate its use with a simple example.

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Answering the Call for a Standard Reliability Measure for Coding Data

In a recent article in this journal, Lombard, Snyder-Duch, and Bracken (2002) surveyed 200 content analyses for their reporting of reliability tests, compared the virtues and drawbacks of five popular reliability measures, and proposed guidelines and standards for their use. Their discussion revealed that numerous misconceptions circulate in the content analysis literature regarding how these measures behave and can aid or deceive content analysts in their effort to ensure the reliability of their data. This article proposes three conditions for statistical measures to serve as indices of the reliability of data and examines the mathematical structure and the behavior of the five coefficients discussed by the authors, as well as two others. It compares common beliefs about these coefficients with what they actually do and concludes with alternative recommendations for testing reliability in content analysis and similar data-making efforts.

https://faculty.washington.edu/jwilker/559/Krippendorf.pdf

Reliability in Content Analysis: Some Common Misconceptions and Recommendations

History and Aim Brief History of Product Semantics Trajectory of Artificiality The Changing Environment of Design Redesigning Design (Discourse) Basic concepts of human-centered design Predecessors The Axiomaticity of Meaning Sense, Meaning, and Context Stakeholders in Design Second-Order Understanding Ethics in a Design Culture Meaning of Artifacts in Use Interfaces Disruptions and Usability Recognition Explorations Reliance Design Principles Meaning of Artifacts in Language Language Categories Characters Identities Verbal Metaphors Narratives Culture Meanings in the Lives of Artifacts Life Cycles Stakeholder Networks Projects Genetic Meanings Critical Sizes of Supportive Communities Whole Life Cycle Accounting Meanings in an Ecology of Artifacts Ecology Ecology of Artifacts Ecological Meanings Technological Cooperatives Mythology Design methods, research, and a science for design A New Science for Design Methods for Creating Spaces of Possible Futures Methods for Inquiring into Stakeholders' Concepts and Motivations Human-Centered Design Methods Validating Semantic Claims Advancing Design Discourse Distantiations Semiotics Cognitivism Ergonomics Aesthetics Functionalism Marketing Textualism Roots in the Ulm School of Design? Bill's Functionalism Bense's Information Philosophy Maldonado's Semiotics Chernyshevsky's Political Economy of Aesthetics Rittel's Methodology Barriers to Considerations of Meaning and Some Exceptions REFERENCES INDEX

The semantic turn: a new foundation for design

Krippendorff’s alpha () is a reliability coefficient developed to measure the agreement among observers, coders, judges, raters, annotators or measuring instruments drawing distinctions among typically unstructured phenomena or assign computable values to them.  emerged in content analysis but is widely applicable wherever two or more methods of generating data are applied to the same set of objects, predefined units of analysis or items and the question is how much the resulting data can be trusted to represent something worthy of analysis.

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Klaus Krippendorff

Papers

Content analysis: an introduction to its methodology

Answering the Call for a Standard Reliability Measure for Coding Data

Reliability in Content Analysis: Some Common Misconceptions and Recommendations

The semantic turn: a new foundation for design

Computing Krippendorff's Alpha-Reliability