The content analysis guidebook
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| Format: | Buch |
| Sprache: | English |
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Los Angeles ; London ; New Delhi ; Singapore ; Washington, DC ; Melbourne
Sage
[2017]
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| Ausgabe: | Second edition |
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| Online-Zugang: | Inhaltsverzeichnis |
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| 100 | 1 | |a Neuendorf, Kimberly A. |e Verfasser |0 (DE-588)1106278038 |4 aut | |
| 245 | 1 | 0 | |a The content analysis guidebook |c Kimberly A. Neuendorf, Cleveland State University |
| 250 | |a Second edition | ||
| 264 | 1 | |a Los Angeles ; London ; New Delhi ; Singapore ; Washington, DC ; Melbourne |b Sage |c [2017] | |
| 264 | 4 | |c © 2017 | |
| 300 | |a xvii, 438 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Content analysis (Communication) | |
| 650 | 4 | |a Sociology |x Research |x Methodology | |
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Datensatz im Suchindex
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| _version_ | 1824053009545428992 |
| adam_text | Contents
LIST OF BOXES xi
LIST OF TABLES AND FIGURES xiii
PREFACE xv
ACKNOWLEDGMENTS xvi
Chapter 1: Defining Content Analysis 1
An Introduction 1
The Growing Popularity of Content Analysis 2
The Myths of Content Analysis 5
Myth 1: Content Analysis Is
Limited to Simple Analyses 5
Myth 2: Anyone Can Do Content Analysis;
It Doesn’t Take Any Special Preparation 7
Myth 3: The Term Content Analysis
Applies to All Examinations of Messages 9
Myth 4: Content Analysis Is for Academic Use Only 15
A Six-Part Definition of Content Analysis 16
1. Content Analysis as Following
the Standards of the Scientific Method 18
2. The Message as the Unit of Analysis,
the Unit of Data Collection, or Both 20
3. Content Analysis as Quantitative 21
4. Content Analysis as Summarizing 23
5. Content Analysis as Applicable to All Contexts 24
6. All Message Characteristics Are
Available to Content Analyze 31
Chapter 2: An Integrative Approach to Content Analysis 36
The Language of the Scientific Method 36
Human Coding Versus Computer
Coding: A Brief Overview
39
How Content Analysis Is Done: A Flowchart
for the Typical Process of Content-Analytic Research 39
Approaches to Content Analysis 42
Descriptive Content Analysis 43
Inferential Content Analysis 44
Psychometric Content Analysis 45
Predictive Content Analysis 46
Toward an Integrative Model of Content Analysis 50
Using the Integrative Model of Content Analysis 52
Integrative Data Linking 53
Integrative Logical Linking 54
The S - M Data Link:
Linking Source and Message Data 54
The S - M Logical Link:
Linking Source and Message Findings 59
The M - R Data Link:
Linking Message and Receiver Data 60
The M - R Logical Link:
Linking Message and Receiver Findings 63
The Growing Use of Integrative Studies 65
Developing New Data Linkages 67
Chapter 3: Message Units and Sampling 70
Units 70
Unitizing a Continuous Stream of Information 72
Defining the Population 74
Cross-Sectional Versus Longitudinal Populations 77
Availability-Based Versus
Exposure-Based Populations 78
Archives 80
The Evaluation of Archives 83
Sampling 84
Probability Sampling 84
Nonprobability Sampling 89
Sample Size 90
Full Reportage 91
Chapter 4: Variables and Predictions 96
Identifying Critical Variables 97
A Consideration of Universal Variables 98
Using Theory and Past Research
for Variable Collection 101
A Grounded or Emergent Process
of Variable Identification iq§
Attempting to Find
Medium-Specific Critical Variables no
Hypotheses and Research Questions 113
Conceptual Definitions 114
Hypotheses 115
Research Questions 117
Full Reportage 119
Chapter 5: Measurement and Validity 121
Defining Measurement 121
Validity, Reliability, Accuracy, and Precision 122
Validity 122
Reliability 122
Accuracy 123
Precision 123
How the Standards Interrelate 123
Types of Validity Assessment 124
Internal Validity and External Validity 125
Face Validity 125
Criterion Validity 126
Content Validity 127
Construct Validity 127
Validation Procedures in Content Analysis 129
Validation in Human-Coded Analyses 129
Validation in CATA Analyses 129
Ethics in Content Analysis 130
Operationalization 131
Categories or Levels That Are Exhaustive 131
Categories or Levels That Are Mutually
Exclusive 131
An Appropriate Level of Measurement 133
Expanding Terminology and Merging Traditions:
Big Data, Text Mining, Natural Language Processing,
Computational Linguistics, Sentiment Analysis/Opinion
Mining, and CATA 143
CATA (Computer-Aided Text Analysis) 146
Dictionaries for Text Analysis 148
CATA Versus Human Coding 153
Selection of a Computer Text
Content Analysis Program 154
Human Coding 156
Codebooks and Coding Forms 156
Coder Selection 157
Coder Training 158
The Processes 159
Medium Modality and Coding 159
Index Construction in Content Analysis 160
Full Reportage 161
Minority Portrayals 294
Movies 295
Music 297
Sentiment Analysis 297
Academic Literatures 298
Commercial, Governmental, and Other
Client-Based Applications of Content Analysis 299
Legal Applications of Content Analysis 301
Future Directions 301
Resource Is CATA—Computer-Aided Text Analysis Options 304
Kimberly A. Neuendorf, Jeffery “Phoenix” Allen, Paul D. Skalski,
and Julie A. Cajigas
Part I. Computer-Aided Text Analysis (CATA) Programs 307
CATPACII 307
Concordance 33 308
Diction 7 308
General Inquirer 310
Hamlet II 3.0 310
LIWC2015 311
MCCALite 312
PCAD 312
Profiler Plus 313
SALT 2012 314
SentiStrength 2.2 315
TextAnalyst 316
Text Analytics for Surveys 4.0 (IBM SPSS) 316
TEXTPACK 317
TextQuest 4.2 318
T-LAB Pro 9.1.3 318
WordSmith 6.0 319
WordStat 7.0 320
Yoshikoder 320
Part II. Yoshikoder Example 321
Resource 2: The Content Analysis Guidebook Online (CAGO) 326
Content Analysis Resources 326
Message Archives and Corpora 326
Reliability 327
Human Coding Sample Materials 327
Introduction to Computer Content Analysis (CATA) 327
REFERENCES 328
AUTHOR INDEX 404
SUBJECT INDEX 422
ABOUT THE AUTHORS
List of Boxes
Box 1.1 Defining Content Analysis 16
Box 1.2 Analyzing Communication in Crisis 27
Box 1.3 The Variety of Content Analysis 28
Box 2.1 A Flowchart for the Typical
Process of Content Analysis Research 40
Box 2.2 The Practical Prediction of Advertising Readership 47
Box 2.3 Approaching Causality---Does Press
Coverage Cause Public Opinion? 49
Box 2.4 From Violent Words to Violent Deeds:
Predicting Threat Outcomes in the FBI 49
Box 3.1 Standard Error and Confidence Intervals 92
Box 4.1 The Critical Variables That Almost
Got Away: Camera Technique in Music Videos 97
Box 4.2 Message Complexity: An Example of a
Potential Universal Variable for Content Analysis 101
Box 5.1 Sample Codebook: Female
Characters in James Bond Films 135
Box 5.2 Sample Coding Form:
Female Characters in James Bond Films 141
Box 5.3 The Evolution of a Dictionary Set:
Political Speech Analysis With Diction 150
Box 5.4 The (Not-So) Secret Life of LIWC 151
Box 6.1 Humor, A Problematic Construct 171
Box 6.2 Example Calculations for
Agreement Coefficients for Nominal Data 181
THE CONTENT ANALYSIS GUIDEBOOK
xii
Box 6.3 Example Calculations for Covariation 185
Coefficients for Ordinal and Ratio Data
Box 7.1 Interactive Media and Content Analysis 205
Box 7.2 Technologies in Archiving; Evernote 214
Box 7.3 Not-Quite Content Analysis 236
Box 8.1 Selecting Appropriate Statistics 247
List of Tables
and Figures_
Figure 1.1 Timeline of Content
Analysis Publications by Year 4
Figure 1.2 Female-Male Attractiveness
Emphasis in World Folktales 6
Table 3.1 Comparing Units of Sampling,
Data Collection, and Analysis 71
Table 3.2 Basic Research and Statistical Terminology
Applicable to Populations and Samples 75
Table 3.3 Summary of Necessary Sample
Sizes for Selected Confidence Intervals 91
Figure 4.1 Four Images and Two Variables 109
Figure 4.2 More Images and More Variables 109
Figure 4.3 Hypothesized Model of Applicant Attraction 117
Figure 5.1 Comparing Reliability, Accuracy, and Precision 124
Table 6.1 Programs and Calculators for Reliability Coefficients 192
Figure 7.1 Sample Codebook “Demo” Page 228
Figure 8.1 Univariate Frequencies, Pie Chart 250
Figure 8.2 Graphical Presentation of Co-Occurrences 252
Figure 8.3 Timeline, Simple 254
Figure 8.4 Timeline With Histograms 255
Figure 8.5 Histogram, Bivariate 257
Figure 8.6 Bivariate Correlation Scatterplot 259
XIV
THE CONTENT ANALYSIS GUIDEBOOK
Figure 8.7 Multidimensional Scaling 269
Figure 8.8 Path Model 270
Table 8.1 Univariate Statistics: Types of
Features Present on the Web Sites (n = 53) 251
Table 8.2 KWIC Analysis of Fear in
Coleridge’s The Ancient Mariner 252
Table 8.3 Crosstabulation (With Chi-Square): Distribution
of Advertisements by Cast Type and Program Index 256
Table 8.4 Means Comparisons (With F Tests):
Table 5 Comparison of Family Firms to Nonfamily
Firms on Entrepreneurial Orientation Dimensions 258
Table 8.5 Bivariate Correlations: Facial Features
With the General Hard Times Measure 258
Table 8.6 ANCOVA 260
Table 8.7 Factor Analysis: Varimax-Rotated
Factor Solution for the 20 Framing Items 262
Table 8.8 Multiple Regression: Analyses
Predicting Applicant Attraction 264
Table 8.9 Logistic Regression: Prediction of Mortality
(Death) of Female Characters in Bond Films 265
Table 8.10 Canonical Correlation: Estimates of Canonical
Correlations and Their Statistical Significance 267
Table 8.11 Cluster Analysis: Member
Clusters---3-Cluster Solution 268
Table R1.1 CATA Software Options 305
|
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| indexdate | 2025-02-14T17:56:55Z |
| institution | BVB |
| isbn | 9781412979474 |
| language | English |
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| publishDate | 2017 |
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| spellingShingle | Neuendorf, Kimberly A. The content analysis guidebook Content analysis (Communication) Sociology Research Methodology Sozialwissenschaften (DE-588)4055916-6 gnd Inhaltsanalyse (DE-588)4027005-1 gnd Methodologie (DE-588)4139716-2 gnd |
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| title | The content analysis guidebook |
| title_auth | The content analysis guidebook |
| title_exact_search | The content analysis guidebook |
| title_full | The content analysis guidebook Kimberly A. Neuendorf, Cleveland State University |
| title_fullStr | The content analysis guidebook Kimberly A. Neuendorf, Cleveland State University |
| title_full_unstemmed | The content analysis guidebook Kimberly A. Neuendorf, Cleveland State University |
| title_short | The content analysis guidebook |
| title_sort | the content analysis guidebook |
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