Savitribai Phule Pune University, Pune

Jayakar Knowledge Resource Centre

Reliability of generating data / by Klaus Krippendorff.

By: Krippendorff, Klaus [author.]Contributor(s): Taylor and Francis Group [Publisher]Material type: TextTextPublication details: Boca Raton : Chapman & Hall, CRC Press, 2021Edition: 1 st edDescription: ix, 315 p. 26 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780367630713 (hbk.)Subject(s): Quantitative research -- Reliability | Data mining -- Reliability | Reliability of Generating DataAdditional physical formats: Online version:: The reliability of generating dataDDC classification: 006.312 Summary: "All data are the result of human actions whether by experimentations, observations, or declarations. As such, the presumption of knowing what data are about is subject of imperfections that can affect the validity of research efforts. With calls for data-based research comes the need to assure the reliability of generated data. Especially the reliability of converting texts into analyzable data has become a burning issue in several areas. However, this issue has been met by only a few limited, and sometimes misleading measures of the extent to which data can be trusted as surrogates of the phenomena of analytical interests. The statistic proposed by the author - "Krippendorff's Alpha" - is widely used in the social sciences, not only where human judgements are involved but also where measurements are compared. This book expands on the author's seminal work in content analysis and develops methods for assessing the reliability of the kind of data that previously defied evaluations for this purpose. It opens with a discussion of the epistemology of reliable data, then presents the most basic alpha coefficient for the single-valued coding of predefined units. This largely familiar way of measuring reliability provides the platform for the succeeding chapters which start with an overview of alternative coefficients and then expand alpha one quality after another, including to cope with the reliabilities of multi-valued coding, segmenting texts into meaningful units, big data, and information retrievals. It also includes a chapter on how to diagnose and remedy imperfections and one on applicable standards, all converging on the statistical issues of the reliability of generating data. Features: Provides an overview of methods for assessing the reliability of generating data Expands a statistic proposed by the author, already widely used in the social sciences Includes many easy to follow numerical examples to illustrate the measures Written to be useful to beginning and advanced researchers from many disciplines, notably linguistics, sociology, psychometric and educational research, and medical science"-- Provided by publisher.
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Books Jayakar Knowledge Resource Centre
Jayakar Knowledge Resource Centre
006.312 KRI.K (Browse shelf(Opens below)) Available 105.00 Pound 519078
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Includes bibliographical references and index.

"All data are the result of human actions whether by experimentations, observations, or declarations. As such, the presumption of knowing what data are about is subject of imperfections that can affect the validity of research efforts. With calls for data-based research comes the need to assure the reliability of generated data. Especially the reliability of converting texts into analyzable data has become a burning issue in several areas. However, this issue has been met by only a few limited, and sometimes misleading measures of the extent to which data can be trusted as surrogates of the phenomena of analytical interests. The statistic proposed by the author - "Krippendorff's Alpha" - is widely used in the social sciences, not only where human judgements are involved but also where measurements are compared. This book expands on the author's seminal work in content analysis and develops methods for assessing the reliability of the kind of data that previously defied evaluations for this purpose. It opens with a discussion of the epistemology of reliable data, then presents the most basic alpha coefficient for the single-valued coding of predefined units. This largely familiar way of measuring reliability provides the platform for the succeeding chapters which start with an overview of alternative coefficients and then expand alpha one quality after another, including to cope with the reliabilities of multi-valued coding, segmenting texts into meaningful units, big data, and information retrievals. It also includes a chapter on how to diagnose and remedy imperfections and one on applicable standards, all converging on the statistical issues of the reliability of generating data. Features: Provides an overview of methods for assessing the reliability of generating data Expands a statistic proposed by the author, already widely used in the social sciences Includes many easy to follow numerical examples to illustrate the measures Written to be useful to beginning and advanced researchers from many disciplines, notably linguistics, sociology, psychometric and educational research, and medical science"-- Provided by publisher.

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