The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Issues and Best Practices in Content Analysis
Abstract
This article discusses three issues concerning content analysis method and ends with a list of best practices in conducting and reporting content analysis projects. Issues addressed include the use of search and databases for sampling, the differences between content analysis and algorithmic text analysis, and which reliability coefficients should be calculated and reported. The “Best Practices” section provides steps to produce reliable and valid content analysis data and the appropriate reporting of those steps so the project can be properly evaluated and replicated.
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Citations
Cites
Content Analysis: An Introduction to its Methodology.
Shelley, M., & Krippendorff, K. (1984) · Journal of the American Statistical Association · doi:10.2307/2288384 · Cited by 24,312
The Content Analysis Guidebook
Neuendorf, K. A. (2017) · doi:10.4135/9781071802878 · Cited by 9,286 · cited 2×
“From Berelson (1952) and Holsti (1969), through Krippendorff (1980), Riffe, Lacy, and Fico (1998), and Neuendorf (2002), texts have aimed to explain and codify standards for content analysis data generation and reporting.”
BackgroundOpinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
Answering the Call for a Standard Reliability Measure for Coding Data
Hayes, A. F., & Krippendorff, K. (2007) · Communication Methods and Measures · doi:10.1080/19312450709336664 · Cited by 4,225 · cited 3×
“Among the three coefficients commonly used for content analysis, Krippendorff proposes that Alpha should become the accepted coefficient (Hayes & Krippendorff, 2007; Krippendorff, 2004b).”
BackgroundMethodText as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297 · cited 4×
“…the algorithmic coder can be validated (e.g., Conway, 2006), the algorithmic coder is capable of analyzing texts at a level of granularity—i.e., counts of individual words—that humans cannot reliably replicate, particularly over a large number of “big data” study units (Grimmer & Stewart, 2013).”
BackgroundInfluentialMass Media Research: An Introduction
Wimmer, R. D., & Dominick, J. R. (1991) · Tamkang University Institutional Repository (TKUIR) · Cited by 2,566
Computing inter‐rater reliability and its variance in the presence of high agreement
Gwet, K. L. (2008) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711006x126600 · Cited by 2,167
Content Analysis for the Social Sciences and Humanities
Parker, S. R. (1970) · Sociology · doi:10.1177/003803857000400231 · Cited by 2,155
Rethinking validity and reliability in content analysis
Potter, W. J., & Levine‐Donnerstein, D. (1999) · Journal of Applied Communication Research · doi:10.1080/00909889909365539 · Cited by 1,662
Content Analysis in Communication Research
Burns, T., & Berelson, B. (1953) · The British Journal of Sociology · doi:10.2307/587175 · Cited by 1,421
Cited by
Meier, A., & Reinecke, L. (2021) · Communication Research · doi:10.1177/0093650220958224 · Cited by 534
Studies of user-generated content: A systematic review
Naab, T. K., & Sehl, A. (2017) · Journalism · doi:10.1177/1464884916673557 · Cited by 151
Liu, X. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.02.021 · Cited by 140
Merkley, E., & Stecula, D. A. (2021) · British Journal of Political Science · doi:10.1017/s0007123420000113 · Cited by 135
Song, H., Tolochko, P., Eberl, J.-M., Eisele, O., Greussing, E., Heidenreich, T., Lind, F., Galyga, S., & Boomgaarden, H. G. (2020) · Political Communication · doi:10.1080/10584609.2020.1723752 · Cited by 123
Metadata
- Authors
- Lacy, S., Watson, B. R., Riffe, D., & Lovejoy, J.
- Venue
- Journalism & Mass Communication Quarterly
- Publisher
- SAGE Publications
- Published
- 2015-09-28
- Volume
- 92
- Issue
- 4
- Pages
- 791-811
- ISSN
- 1077-6990 (ISSN-L)
- 2161-430X
- OpenAlex
- W2175753910
- Cited by
- 450
- References
- 33
- FWCI
- 102.70
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
