Data Science in Psychology : Using Python in Psychological Research

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Data Science in Psychology : Using Python in Psychological Research

Kovac, NatasaEDT/ Simeunovic, MarkoEDT/ Farahani, HojjatollahEDT

ウェブストア価格 ¥45,042（本体¥40,948）
Springer, Berlin; Springer（2026/07発売）
外貨定価 EUR 171.19
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ポイント 2,045pt

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製本 Hardcover:ハードカバー版
商品コード 9783032183118

Full Description

This book is an innovative resource designed to bridge the gap between traditional psychological research methods and contemporary data science techniques. This book provides a comprehensive introduction to using Python for analyzing psychological data, enabling researchers, educators, and students to harness the analytical power of data science within their work. The volume is structured into four parts, encompassing programming skills, data preparation, advanced data processing, and the interpretation of results, each reinforced with practical examples and case studies.

The content starts with the basics of Python programming, tailored specifically for psychological research applications. It then progresses to the sophisticated analysis of psychological data using statistical models, machine learning, and artificial intelligence, with a strong focus on Python's capabilities in these areas. This includes detailed discussions on Confirmatory Factor Analysis, machine learning algorithms like SVMs, and innovative techniques such as metaheuristics and simulations.

This book is particularly timely as psychological research becomes increasingly data-driven, necessitating a deeper understanding of complex datasets and the development of more sophisticated analytical tools. "Data Science in Psychology" addresses this need by providing not only the technical skills required but also a deep understanding of how these techniques can be applied specifically to psychological research. The primary audience, including psychology researchers, academics, and advanced students, will find this book invaluable for integrating data science into their daily toolkit, thus leveling up their research capabilities and broadening their methodological approaches in an era where interdisciplinary skills are an added value.

Part I. Python and Psychological Sciences Background.- Chapter 1. Introduction to Python programming.- Chapter 2. Introduction to Psychological Sciences.- Chapter 3. Datasets for Psychological Sciences.- Part II. Data preparation.- Chapter 4. Collecting the data in psychological sciences.- Chapter 5. Datasets preparation.- Chapter 6. Data pre-processing.- Part III. Data processing.- Chapter 7. The difference between data science, statistics, machine learning and artificial intelligence.- Chapter 8. Data modelling.- Chapter 9. Confirmatory Factor Analysis.- Chapter 10. Graded item response theory: developing and validating a psychological scale.- Chapter 11. Bootstrap Exploratory Graph Analysis.- Chapter 12. Clustering methods.- Chapter 13. Latent Class Analysis and Latent Profile Analysis.- Chapter 14. Time-dependent Modelling in Psychological Data.- Chapter 15. Shrinkage Regression: Ridge Regression, LASSO, Elastic Net Regression.- Chapter 16. Bayesian Data Analysis.- Chapter 17. Support Vector Machine Algorithm.- Chapter 18. Classification in Psychological Research.- Chapter 19. Natural Language Processing and Psychological Sciences.- Chapter 20. Metaheuristics in Psychological Data Analysis.- Chapter 21. Simulations in Psychological Research.- Part IV. Interpretation of results.- Chapter 22. Interpreting Statistical Results.- Chapter 23. Advanced Data Interpretation Strategies.- Chapter 24. Reporting and Discussing Findings.