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Big Data in Psychology
Methods and Applications
Taschenbuch von Mike W.-L. Cheung (u. a.)
Sprache: Englisch

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Beschreibung
Big data is becoming more prevalent in psychology and the behavioral sciences, and so are the methodological and statistical issues that arise from its use. Psychologists need to be equipped to deal with these. Big data can be generated in experimental studies where, for example, participants' physiological and psychological responses are tracked over time or where human brain imaging is employed. Observational data from websites such as Facebook, Twitter, and Google is also of increasing interest to psychologists. These sometimes huge data sets, which are often too large for standard computers and can also contain multiple types of data, bring with them challenging questions about data quality and the generalizability of the results as well as which statistical tools are suitable for analyzing them. The contributions in this volume explore these challenges, looking at the potential of applying machine learning techniques to big data in psychology as well as the split/analyze/meta-analyze (SAM) approach, which allows big data to be split up into smaller datasets so they can be analyzed with conventional multivariate techniques on standard computers. The issues of replicability, prediction accuracy, and combining types of data are also investigated.
Big data is becoming more prevalent in psychology and the behavioral sciences, and so are the methodological and statistical issues that arise from its use. Psychologists need to be equipped to deal with these. Big data can be generated in experimental studies where, for example, participants' physiological and psychological responses are tracked over time or where human brain imaging is employed. Observational data from websites such as Facebook, Twitter, and Google is also of increasing interest to psychologists. These sometimes huge data sets, which are often too large for standard computers and can also contain multiple types of data, bring with them challenging questions about data quality and the generalizability of the results as well as which statistical tools are suitable for analyzing them. The contributions in this volume explore these challenges, looking at the potential of applying machine learning techniques to big data in psychology as well as the split/analyze/meta-analyze (SAM) approach, which allows big data to be split up into smaller datasets so they can be analyzed with conventional multivariate techniques on standard computers. The issues of replicability, prediction accuracy, and combining types of data are also investigated.
Details
Erscheinungsjahr: 2019
Fachbereich: Allgemeines
Genre: Psychologie
Rubrik: Geisteswissenschaften
Thema: Lexika
Medium: Taschenbuch
Seiten: 80
Inhalt: IV
80 S.
ISBN-13: 9780889375512
ISBN-10: 0889375518
Sprache: Englisch
Ausstattung / Beilage: Großformatiges Paperback. Klappenbroschur
Redaktion: Cheung, Mike W.-L.
Jak, Suzanne
Herausgeber: Mike W -L Cheung/Suzanne Jak
Hersteller: Hogrefe Publishing
Maße: 278 x 206 x 4 mm
Von/Mit: Mike W.-L. Cheung (u. a.)
Erscheinungsdatum: 15.03.2019
Gewicht: 0,256 kg
preigu-id: 113987236
Details
Erscheinungsjahr: 2019
Fachbereich: Allgemeines
Genre: Psychologie
Rubrik: Geisteswissenschaften
Thema: Lexika
Medium: Taschenbuch
Seiten: 80
Inhalt: IV
80 S.
ISBN-13: 9780889375512
ISBN-10: 0889375518
Sprache: Englisch
Ausstattung / Beilage: Großformatiges Paperback. Klappenbroschur
Redaktion: Cheung, Mike W.-L.
Jak, Suzanne
Herausgeber: Mike W -L Cheung/Suzanne Jak
Hersteller: Hogrefe Publishing
Maße: 278 x 206 x 4 mm
Von/Mit: Mike W.-L. Cheung (u. a.)
Erscheinungsdatum: 15.03.2019
Gewicht: 0,256 kg
preigu-id: 113987236
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