Content Analysis: An Introduction to Its Methodology

By Klaus Krippendorff. 1980

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

Subfields: Natural Language Processing

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Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning (2018, background): “…surprisingly, while relative feedback is often considered easier for humans to provide (Thurstone, 1927), our investigation shows that α-reliability (Krippendorff, 2013) for intra- and inter-rater agreement is similar for both tasks, with highest inter-rater reliability for standardized 5-point…”
Ex Machina: Personal Attacks Seen at Scale (2016, background): “In the case of a model trained on OH labels, the attack score represents the probability that the majority of annotators would consider the comment an attack.”
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021, neutral): “Instead of approaching our structured content analysis through a process of independently coding data [68], the authors split up the responsibility of coding each of the datasets, with the majority of the coding being performed by the first author.”
The Values Encoded in Machine Learning Research (2021, methodology): “To perform the line-by-line analysis and annotate the uplifted values (Figure 1), we used a hybrid inductive-deductive content analysis methodology and followed best practices [9, 30, 37, 45]: (i) We began with several values of interest based on prior literature, specifically seven ethical principl”
Quark: Controllable Text Generation with Reinforced Unlearning (2022, background): “In terms of Krippendorf’s α [33], which is scaled from -1 (perfect systematic disagreement) to 1 (perfect agreement), agreement rates for “overall", “ﬂuency", and “coherence" respectively are α = .”
How to Analyze Political Attention with Minimal Assumptions and Costs (2010, background): “Krippendorff (2004) considers this the most relevant form of validity for evaluating a content analysis measure.”
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes (2015, methodology): “We measure turker agreement with Krippendorff’s α (Krippendorff, 1980), which is a statistical measure of agreement between any number of coders.”
Making the most of corporate social responsibility reporting: disclosure structure and its impact on performance (2011, background): “Disclosure depth, measured as the number of sentences referring to each theme, is an indication of the importance assigned to an issue by a reporting entity ( Krippendorf, 1980”
The tone of business model disclosure: an impression management analysis of the integrated reports (2015, methodology): “The reliability of the classification procedure was assessed following Krippendorff (2013).”
Corporate Governance Quality and CSR Disclosures (2013, methodology): “…the main assumptions behind the use of quantitative content analysis as an empirical research tool is that volume of disclosure signiﬁes importance (Krippendoff 1980; Gray et al. 1995; Deegan and Rankin 1996; Neu et al. 1998), it seems inconsistent to omit counting the volume of disclosure…”
The Impact of Corporate Social Responsibility Disclosure on Financial Performance: Evidence from the GCC Islamic Banking Sector (2018, methodology): “Furthermore, (Krippendorff 1980) identiﬁed three forms of reliability for content analysis and disclosure measurement, namely stability, reproducibility and accuracy.”
Measures of Reading Comprehension: A Latent Variable Analysis of the Diagnostic Assessment of Reading Comprehension (2006, methodology): “To assess reliability, a random sample of 20 narratives was scored by multiple raters for protocol accuracy (98%–100%), for transcription accuracy (90%–98%), for the narrative structure score (Krippendorff’s α = .74), and for the subordination index (α = .96; Krippendorff, 1980).”
Assessing ExxonMobil’s climate change communications (1977–2014) (2017, unknown): “Our analysis compares these documents with ExxonMobil’s public outreach in the form of paid, editorial-style advertisements— known as ‘advertorials’—published on the Op-Ed page of The New York Times (NYT) [19].”