Big Data in Psychology
Hogrefe Publishing (Verlag)
978-0-88937-551-2 (ISBN)
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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.
Mike W. L. Cheung; PhD; National University of Singapore, Singapore. Suzanne Jak; PhD; University of Amsterdam, The Netherlands.
Editorial
Challenges of Big Data Analyses and Applications in Psychology Mike W.-L. Cheung and Suzanne Jak
Original Articles Revealing the Joint Mechanisms in Traditional Data Linked With Big Data Niek C. de Schipper and Katrijn Van Deun
Digital Footprints of Sensation Seeking: A Traditional Concept in the Big Data Era Ramona Schoedel, Quay Au, Sarah Theres Voelkel, Florian Lehmann, Daniela Becker, Markus Buhner, Bernd Bischl, Heinrich Hussmann, and Clemens Stachl
Predictive Modeling With Psychological Panel Data Florian Pargent and Johannes Albert-von der Goenna
Replicability of Machine Learning Models in the Social Sciences: A Case Study in Variable Selection Ranjith Vijayakumar and Mike W.-L. Cheung
Integrating the Split/Analyze/Meta-Analyze (SAM) Approach and a Multilevel Framework to Advance Big Data Research in Psychology: Guidelines and an Empirical Illustration via the Human Resource Management Investment-Firm Performance Relationship Yucheng Eason Zhang, Siqi Liu, Shan Xu, Miles M. Yang, and Jian Zhang
Volume Information Reviewers 2018
| Erscheinungsdatum | 30.01.2019 |
|---|---|
| Reihe/Serie | Zeitschrift für Psychologie ; 226/4 |
| Verlagsort | Toronto |
| Sprache | englisch |
| Maße | 210 x 277 mm |
| Themenwelt | Geisteswissenschaften ► Psychologie ► Allgemeines / Lexika |
| Geisteswissenschaften ► Psychologie ► Test in der Psychologie | |
| Schlagworte | Big Data • experimental psychology • Research methods |
| ISBN-10 | 0-88937-551-8 / 0889375518 |
| ISBN-13 | 978-0-88937-551-2 / 9780889375512 |
| Zustand | Neuware |
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