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Big Data in Psychological Research [Kietas viršelis]

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Edited by , Edited by , Edited by
  • Formatas: Hardback, 469 pages, aukštis x plotis: 254x178 mm, weight: 1000 g, 36 figures, 9 tables, 1 exhibit
  • Išleidimo metai: 23-Jun-2020
  • Leidėjas: American Psychological Association
  • ISBN-10: 1433831678
  • ISBN-13: 9781433831676
Kitos knygos pagal šią temą:
Big Data in Psychological ResearchDidesnis paveikslėlis
  • Formatas: Hardback, 469 pages, aukštis x plotis: 254x178 mm, weight: 1000 g, 36 figures, 9 tables, 1 exhibit
  • Išleidimo metai: 23-Jun-2020
  • Leidėjas: American Psychological Association
  • ISBN-10: 1433831678
  • ISBN-13: 9781433831676
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781433831676.html
Raktažodžiai:
Big Data in Psychological Research provides an overview of big data theory, research design and analysis, collection methods, applications, ethical concerns, best practices, and future research directions for psychologists.

"Technological advances have led to an abundance of widely available data on every aspect of life today. Psychologists today have more information than ever before on human cognition, emotion, attitudes, and behavior. Big Data in Psychological Research addresses the opportunities and challenges that this data presents to psychological researchers. This edited collection provides an overview of theoretical approaches to the utility and purpose of big data, approaches to research design and analysis, collection methods, applications, limitations, best practice recommendations, and key issues related to privacy, security, and ethical concerns that are essential to understand for anyone working with big data. The book also discusses potential future researchdirections aimed at improving the quality and interpretation of big data projects, as well as the training and evaluation of psychological science teams that conduct research using big data"--

Technological advances have led to an abundance of widely available data on every aspect of life today. Psychologists today have more information than ever before on human cognition, emotion, attitudes, and behavior. Big Data in Psychological Research addresses the opportunities and challenges that this data presents to psychological researchers. This edited collection provides an overview of theoretical approaches to the utility and purpose of big data, approaches to research design and analysis, collection methods, applications, limitations, best practice recommendations, and key issues related to privacy, security, and ethical concerns that are essential to understand for anyone working with big data. The book also discusses potential future research directions aimed at improving the quality and interpretation of big data projects, as well as the training and evaluation of psychological science teams that conduct research using big data.
Contributors vii
Introduction 3(10)
Sang Eun Woo
Louis Tay
Robert W. Proctor
I BACKGROUND AND OVERVIEW
13(74)
1 Big Data Science: A Philosophy of Science Perspective
15(20)
Brian D. Haig
2 From Small-Scale Experiments to Big Data: Challenges and Opportunities for Experimental Psychologists
35(24)
Robert W. Proctor
Aiping Xiong
3 Big Data for Enhancing Measurement Quality
59(28)
Sang Eun Woo
Louis Tay
Andrew T. Jebb
Michael T. Ford
Margaret L. Kern
II INNOVATIONS IN LARGE-SCALE DATA COLLECTION AND ANALYSIS TECHNIQUES
87(114)
4 Internet Search and Page View Behavior Scores: Validity and Usefulness as Indicators of Psychological States
89(20)
Michael T. Ford
5 Observing Human Behavior Through Worldwide Network Cameras
109(16)
Sara Aghajanzadeh
Andrew T. Jebb
Yifan Li
Yung-Hsiang Lu
George K. Thiruvathukal
6 Wearable Cameras, Machine Vision, and Big Data Analytics: Insights Into People and the Places They Go
125(20)
Andrew B. Blake
Daniel I. Lee
Roberto De La Rosa
Ryne A. Sherman
7 Human-Guided Visual Analytics for Big Data
145(34)
Morteza Karimzadeh
Jieqiong Zhao
Guizhen Wang
Luke S. Snyder
David S. Ebert
8 Text Mining: A Field of Opportunities
179(22)
Padmini Srinivasan
III APPLICATIONS
201(144)
9 Big Data in the Science of Learning
203(24)
Sidney K. D'Mello
10 Big Data in Social Psychology
227(28)
Ivan Hernandez
11 Big Data in Health Care Delivery
255(22)
Mohammad Adibuzzaman
Paul M. Griffin
12 The Continued Importance of Theory: Lessons From Big Data Approaches to Language and Cognition
277(20)
Brendan T. Johns
Randall K. Jamieson
Michael N. Jones
13 Big Data in Developmental Psychology
297(22)
Kevin J. Grimm
Gabriela Stegmann
Ross Jacobucci
Sarfaraz Serang
14 Applying Principles of Big Data to the Workplace and Talent Analytics
IV RECOMMENDATIONS FOR RESPONSIBLE AND RIGOROUS USE OF BIG DATA
345(80)
15 The Belmont Report in the Age of Big Data: Ethics at the Intersection of Psychological Science and Data Science
347(26)
Alexandra Paxton
16 Promoting Robust and Reliable Big Data Research in Psychology
373(20)
Joshua A. Strauss
James A. Grand
17 Privacy and Cybersecurity Challenges, Opportunities, and Recommendations: Personnel Selection in an Era of Online Application Systems and Big Data
393(18)
Talya N. Bauer
Donald M. Truxillo
Mark P. Jones
Grant Brady
18 Privacy Enhancing Techniques for Security
411(14)
Elisa Bertino
V CONCLUDING REMARKS
425(18)
19 Future Research Directions for Big Data in Psychology
427(16)
Frederick L. Oswald
Index 443(24)
About the Editors 467
Sang Eun Woo, PhD, is an associate professor in the Department of Psychological Sciences at Purdue University. Her research focuses on industrial-organizational psychology, particularly personality and motivation, work attitudes, withdrawal behaviors, and interpersonal relationships in the workplace.

Louis Tay, PhD, is an associate professor in the Department of Psychological Sciences at Purdue University. His research focuses on industrial-organizational psychology, with a particular focus on issues related to methodology (i.e., measurement, continuum specification, latent class modeling, and big data/data science) and well-being (i.e., societal well-being, wellness programs, and work-leisure interface).

Robert W. Proctor, PhD, is a professor in the Department of Psychological Sciences at Purdue University. His research focuses on human performance, human-computer interaction, human factors issues related to information security and web design, and experimental research methods.