Content analysis : an introduction to its methodology / Klaus Krippendorff.

Format:

Book

Author/Creator:

Krippendorff, Klaus, author.

Language:

English

Subjects (All):

Content analysis (Communication).

Physical Description:

xiv, 453 pages : illustrations ; 26 cm

Edition:

Fourth edition.

Place of Publication:

Los Angeles : SAGE, [2019]

Summary:

Since the first edition published in 1980, Content Analysis has helped shape and define the field. In the highly anticipated Fourth Edition, award-winning scholar and author Klaus Krippendorff introduces readers to the most current method of analyzing the textual fabric of contemporary society. Students and scholars will learn to treat data not as physical events but as communications that are created and disseminated to be seen, read, interpreted, enacted, and reflected upon according to the meanings they have for their recipients. Interpreting communications as texts in the contexts of their social uses distinguishes content analysis from other empirical methods of inquiry. Organized into three parts, Content Analysis first examines the conceptual aspects of content analysis, then discusses components such as unitizing and sampling, and concludes by showing readers how to trace the analytical paths and apply evaluative techniques. The Fourth Edition has been completely revised to offer readers the most current techniques and research on content analysis, including new information on reliability and social media. Readers will also gain practical advice and experience for teaching academic and commercial researchers how to conduct content analysis. -- Provided by publisher.

Contents:

Part I Conceptualizing Content Analysis

1.1 Some Precursors p. 10

1.2 Quantitative Newspaper Analysis p. 11

1.3 Early Content Analysis p. 12

1.4 Propaganda Analysis p. 14

1.5 Content Analysis Generalized p. 17

1.6 Computer Text Analysis p. 18

1.7 Qualitative Approaches p. 21

Chapter 2 Conceptual Foundation p. 24

2.2 Epistemological Elaborations p. 27

2.4.4 Analytical Constructs p. 42

2.4.5 Inferences p. 43

2.4.6 Validating Evidence p. 45

2.5 Contrasts and Comparisons p. 46

Chapter 3 Uses and Inferences p. 51

3.1 Traditional Overviews p. 51

3.2 Extrapolations p. 54

3.2.3 Differences p. 58

3.3.2 Evaluations p. 62

3.3.3 Judgments p. 64

3.4 Indices and Symptoms p. 65

3.5 Linguistic Re-Presentations p. 69

3.6 Conversations p. 73

3.7 Institutional Processes p. 74

3.8 Areas of Likely Success p. 80

Part II Components of Content Analysis

Chapter 4 The Logic of Content Analysis Designs p. 86

4.1 Content Analysis Designs p. 86

4.1.1 Components p. 87

4.1.2 Quantitative and Qualitative Content Analysis p. 91

4.2 Designs Preparatory to Content Analysis p. 94

4.2.1 Operationalizing Available Knowledge of the Context p. 94

4.2.2 Testing Analytical Constructs as Hypotheses p. 95

4.2.3 Developing Analytical Constructs by Trial and Error p. 96

4.3 Designs Going Beyond Content Analysis p. 98

4.3.1 Comparing Similar Phenomena Inferred From Different Bodies of Texts p. 99

4.3.2 Testing Relationships among Phenomena Inferred From One Body of Texts p. 99

4.3.3 Testing Hypotheses Concerning How Content Analysis Results Relate to Other Variables p. 100

Chapter 5 Unitizing p. 102

5.1 Units p. 102

5.2 Types of Units p. 103

5.2.1 Sampling Units p. 103

5.2.2 Recording/Coding Units p. 104

5.2.3 Context Units p. 105

5.3 Ways of Defining Units p. 107

5.3.1 Physical Distinctions p. 107

5.3.2 Syntactical Distinctions p. 108

5.3.3 Categorical Distinctions p. 109

5.3.4 Propositional Distinctions p. 110

5.3.5 Thematic Distinctions p. 111

5.4 Productivity, Efficiency, and Reliability p. 112

Chapter 6 Sampling p. 115

6.1 Sampling in Theory p. 115

6.2 Sampling Techniques Applicable to Texts p. 117

6.2.1 Random Sampling p. 118

6.2.2 Systematic Sampling p. 118

6.2.3 Stratified Sampling p. 119

6.2.4 Varying Probability Sampling p. 119

6.2.5 Cluster Sampling p. 120

6.2.6 Snowball Sampling p. 121

6.2.7 Relevance Sampling p. 122

6.2.9 Convenience Sampling p. 124

6.3 Sample Size p. 124

6.3.1 Statistical Sampling Theory p. 124

6.3.2 Sampling Experiments p. 125

6.3.3 The Split-Half Technique p. 127

Chapter 7 Recording/Coding p. 128

7.1 The Function of Coding and Recording p. 128

7.2 Coder Qualifications p. 130

7.2.1 Cognitive Abilities p. 130

7.2.3 Frequency p. 131

7.3 Coder Training p. 131

7.4 Crowdcoding p. 134

7.4.1 Methodological Advantages p. 135

7.4.2 Efforts to Assure Crowdcoding Quality p. 136

7.4.3 Uses of Reliability Tests p. 136

7.4.4 Limitations and Warnings p. 137

7.5 Approaches to Defining the Semantics of Data p. 138

7.5.1 Verbal Designations p. 139

7.5.2 Extensional Lists p. 140

7.5.4 Decision Schemes p. 142

7.5.5 Magnitudes and Scales p. 143

7.5.6 Simulation of Hypothesis Testing p. 145

7.5.7 Simulation of Interviewing p. 146

7.5.8 Constructs for Closure p. 148

7.6 Records p. 149

7.6.1 Administrative Information p. 151

7.6.2 Information on the Organization of Records p. 151

7.6.3 Substantive Information About the Phenomena of Interest p. 153

Chapter 8 Data Languages p. 157

8.1 The Place of Data Languages in Analytical Efforts p. 157

8.3 Variables p. 161

8.4 Nominal Variables p. 167

8.5 Ordered Variable p. 167

8.5.1 Chains p. 168

8.5.2 Recursions p. 169

8.5.3 Cubes p. 169

8.5.4 Trees p. 171

8.5.5 Multi-Valued Sets p. 171

8.6.1 Ordinal Metrics p. 173

8.6.2 Interval Metrics p. 175

8.6.3 Ratio Metrics p. 176

8.7 Mathematical Operations p. 176

Chapter 9 Analytical Constructs p. 178

9.1 The Role of Analytical Constructs p. 178

9.2 Sources of Certainty p. 180

9.2.1 Previous Successes and Failures p. 180

9.2.2 Expert Knowledge and Experience p. 181

9.2.3 Established Theories p. 183

9.2.2 Embodied Practices p. 185

9.3 Types of Constructs p. 186

9.3.1 Extrapolations p. 186

9.3.2 Applications of Standards p. 186

9.3.3 Indices and Symptoms p. 186

9.3.4 Re-Presentations p. 188

9.3.5 Conversations/Interactions p. 189

9.3.6 Institutional Processes p. 189

9.4 Sources of Uncertainty p. 191

9.4.1 Variance of the Target Domain p. 191

9.4.2 Confidence Levels p. 192

9.4.3 Stability of the Analytical Constructs Model p. 192

Part III Analytical Paths and Evaluative Techniques

Chapter 10 Analytical/Representational Techniques p. 196

10.1 Counts p. 197

10.2 Cross-Tabulations, Associations, and Correlations p. 200

10.3 Multivariate Techniques p. 203

10.4 Factor Analysis and Multidimensional Scaling p. 205

10.5 Images, Portrayals, Semantic Nodes, and Profiles p. 207

10.6 Contingencies and Contingency Analysis p. 210

10.7 Clustering p. 212

Chapter 11 Computer Aids p. 215

11.1 What Computers Do p. 215

11.2 How Computers Can Aid Content Analyses p. 216

11.3 Text Analyses p. 220

11.3.1 Accounts of Character Strings p. 220

11.3.2 Text Mining p. 227

11.3.3 Mining Textual Evidence for Theories p. 236

11.3.4 Analysis of Networked Texts p. 239

11.4 Computational Content Analyses p. 246

11.4.1 Coding/Dictionary Approaches p. 248

11.4.2 Machine Learning of Discriminant Functions p. 254

11.4.3 Statistical Association Approaches p. 256

11.4.4 Semantic Network Approaches p. 258

11.4.5 Memetic Approaches p. 264

11.5 Qualitative Data Analysis Support p. 268

11.6 Frontiers p. 272

11.6.1 Intelligent Browsers p. 272

11.6.2 Common Platforms p. 272

11.6.3 Computational Theories of Meaning p. 273

11.6.4 Utilization of Intertextualities p. 274

11.6.5 Analyzing Networked Text p. 274

11.6.6 Natural Interfaces p. 275

12.1 Why Reliability? p. 277

12.2 Reliability Designs p. 280

12.2.1 Types of Agreement; Types of Reliability p. 280

12.2.2 Conditions for Generating Reliability Data p. 283

12.2.3 Reliability Data p. 285

12.3 Agreement on Coding Predefined Units p. 289

12.3.1 The Direct Path to ̜α-Agreement p. 293

12.3.2 The Path to ̜α via Coincidence Matrices p. 296

12.3.3 Difference Functions for Data With Various Metrics p. 299

12.3.4 Some Typical Examples and Important Properties p. 306

12.3.4.1 Binary Data, None Missing p. 306

12.3.4.2 Coincidences and Contingencies p. 308

12.3.4.3 ̜α's Decomposition of Observed Coincidences p. 310

12.3.4.4 Two Observers, Several Nominal Values, No Missing Data p. 310

12.3.4.5 Uninformative Values in Ordered Variables p. 312

12.3.4.6 Trading Information for Reliability p. 314

12.3.5 Some Contested Coefficients and Correspondences p. 315

12.4 Accuracy, Surrogacy, and the Decisiveness of Majorities p. 323

12.4.1 Accuracy of or Surrogacy for Crowd Members' Judgments p. 325

12.4.2 Accuracy of or Surrogacy for Majorities or Aggregates p. 326

12.4.3 The Decisiveness of Majorities or Aggregates p. 328

12.5 The Reliability of Text Mining and Information Retrieval p. 331

12.5.1 For Replications of Searches p. 334

12.5.2 For Replications of Judgments on the Relevance of Retrieval Results p. 336

12.6 Agreement on Unitizing and Coding Finite Continua p. 337

12.6.1 Reliability Data from Unitizing p. 338

12.6.2 Observed and Expected Coincidences p. 340

12.6.3 Agreement on the Partitions of a Continuum Into Unequal Segments p. 342

12.6.4 Agreement on the Distinction between Relevant and Irrelevant Segments p. 343

12.6.5 Agreement on the Coding of Single-Valued Segments Independent of Unitizing p. 344

12.6.6 Agreement on Individual Values among Valued Intersections p. 346

12.7 Agreement on Multi-Valued Coding p. 347

12.7.1 Two Reasons for Multi-Valued Coding p. 348

12.7.2 Reliability Data for Multi-Valued Coding p. 349

12.7.3 Multi-Valued Difference Functions p. 349

12.7.4 Disagreements for Multi-Valued Data p. 350

12.7.5 ̜α for Multi-Valued Coding p. 351

12.8 Statistical Properties of ¿ p. 351

12.8.1 Insufficient Variation p. 351

12.8.2 Statistical Significance p. 353

12.8.3 Sampling Considerations p. 354

12.8.4 Standards for the Reliability of Data p. 356

13.1 Validity Defined p. 361

13.2 A Typology for Validating Evidence p. 365

13.2.1 Sampling Validity p. 368

13.2.2 Semantic Validity p. 370

13.2.3 Structural Validity p. 376

13.2.4 Functional Validity p. 377

13.2.5 Correlative Validity p. 378

13.2.6 Predictive Validity p. 381

Chapter 14 A Practical Guide p. 383

14.1 Designing an Analysis p. 384

14.1.1 Text-Driven Analyses p. 384

14.1.2 Problem-Driven Analyses p. 386

14.1.2.1 Formulating Research Questions p. 387

14.1.2.2 Ascertaining Stable Correlations (With the Research Questions) p. 388

14.1.2.3 Locating Relevant Texts p. 390

14.1.2.4 Defining and Identifying Sampling Units Among Relevant Texts p. 392

14.1.2.5 Sampling a Sufficiently Large Number of These Units p. 393

14.1.2.6 Developing Appropriate Coding Categories and Recording Instructions p. 394

14.1.2.1 Selecting Appropriate Analytical Procedures p. 395

14.1.2.8 Adopting Standards for the Reliability of Generated Data and Statistical Significance Levels for the Results p. 396

14.1.2.9 Allocating Resources p. 397

14.1.3 Method-Driven Analyses p. 398

14.2 Writing a Research Proposal p. 400

14.2.1 Rhetorical Function p. 400

14.2.2 Contractual Function p. 401

14.2.3 Outline for a Research Proposal p. 402

14.3 Applying the Research Design p. 403

14.4 Narrating the Results p. 404

14.4.1 Outline for a Research Report p. 405.

Notes:

Includes bibliographical references and index.

ISBN:

9781506395661

150639566X

OCLC:

1019840156

Publisher Number:

40028301191