The Reliability of Multi-Valued Coding of Data

Krippendorff, Klaus; Craggs, Richard

doi:10.1080/19312458.2016.1228863

Cited by 28 publications

(19 citation statements)

References 25 publications

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“…The coding of qualitative data, such as interviews, requires "observers or readers to categorise, scale, or measure each of a given set of predefined units of analysis, in effect characterising them by one value from each variable of analytical interest. However, there are many occasions in which texts have multiple interpretations" (Krippendorff and Craggs 2016). We use applied thematic analysis with predefined codes, and acknowledge that this method may omit some meaning intended by a respondent, but accept this limitation for pragmatic, rather than interpretivist application (Guest et al 2014).…”

Section: Coding and Analysismentioning

confidence: 99%

“…We use applied thematic analysis with predefined codes, and acknowledge that this method may omit some meaning intended by a respondent, but accept this limitation for pragmatic, rather than interpretivist application (Guest et al 2014). This study employs a coding analysis method that enables the evaluation of the reliability of multiple codes to each interview question response by two analysts, resulting in an overall reliability coefficient (Krippendorff and Craggs 2016).…”

Section: Coding and Analysismentioning

confidence: 99%

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Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

J. Big Data Anal. Transp.

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Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

show abstract

Section: Coding and Analysismentioning

confidence: 99%

Section: Coding and Analysismentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

J. Big Data Anal. Transp.

View full text Add to dashboard Cite

show abstract

“…Coding of qualitative data, such as interviews, requires "observers or readers to categorise, scale, or measure each of a given set of predefined units of analysis, in effect characterising them by one value from each variable of analytical interest. However, there are many occasions in which texts…have multiple interpretations" (Krippendorff & Craggs, 2016). We use applied thematic analysis with predefined codes, and acknowledge that this method may omit some meaning intended by a respondent, but accept this limitation for pragmatic, rather than interpretivist application (Guest, MacQueen, & Namey, 2014).…”

Section: Coding and Analysismentioning

confidence: 99%

“…We use applied thematic analysis with predefined codes, and acknowledge that this method may omit some meaning intended by a respondent, but accept this limitation for pragmatic, rather than interpretivist application (Guest, MacQueen, & Namey, 2014). This study employs a coding analysis method that enables the evaluation of the reliability of multiple codes to each interview question response by two analysts, resulting in an overall reliability coefficient (Krippendorff & Craggs, 2016).…”

Section: Coding and Analysismentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

Preprint

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“…Reporting of intercoder reliability tests is encouraged or even required for publication in many journals and has become a standard practice for most communication researchers (Lombard, Snyder-Duch, & Bracken, 2002;Lovejoy, Watson, Lacy, & Riffe, 2014;Riffe & Freitag, 1997). Communication Methods and Measures has served as an important outlet for this laudable development (Hayes & Krippendorff, 2007;Krippendorff, 2008Krippendorff, , 2011Krippendorff & Craggs, 2016;Lovejoy et al, 2014).…”

mentioning

confidence: 99%

Correcting Measurement Error in Content Analysis

Bachl

Scharkow

2017

Communication Methods and Measures

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Conducting and reporting reliability tests has become a standard practice in content analytical research. However, the consequences of measurement error in coding data are rarely discussed or taken into consideration in subsequent analyses. In this article, we demonstrate how misclassification in content analysis leads to biased estimates and introduce matrix back-calculation as a simple remedy. Using Monte Carlo simulation, we investigate how different ways of collecting information about the misclassification process influence the effectiveness of error correction under varying conditions. The results show that error correction with an adequate set-up can often substantially reduce bias. We conclude with an illustrative example, extensions of the procedure, and some recommendations.

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The Reliability of Multi-Valued Coding of Data

Cited by 28 publications

References 25 publications

Mitigating Bias in Big Data for Transportation

Mitigating Bias in Big Data for Transportation

Mitigating Bias in Big Data for Transportation

Correcting Measurement Error in Content Analysis

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