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An introductory guide for conducting psychological research with big data.
Michela Vezzoli1, Cristina Zogmaister1
1Department of Psychology, University of Milano-Bicocca.
Psychological Methods
|March 13, 2023
Summary
This guide introduces Big Data research processes for psychologists, addressing skepticism by detailing data sourcing, preprocessing, and analysis techniques using R and Python. It aims to foster interdisciplinary collaboration in psychological Big Data science.
Area of Science:
- Psychology
- Data Science
- Computational Social Science
Background:
- Psychological researchers often exhibit skepticism towards Big Data research due to unfamiliarity and perceived complexity.
- Difficulties include conceptualizing Big Data applications in specific psychological fields and lacking specialized knowledge.
Purpose of the Study:
- To provide an introductory guide for psychologists interested in conducting Big Data research.
- To demystify Big Data processes and facilitate the adoption of data-driven approaches in psychology.
Main Methods:
- The guide follows the Knowledge Discovery from Database (KDD) process.
- It outlines methods for identifying suitable psychological data, data preprocessing techniques, and analysis strategies.
- Key programming languages, R and Python, are highlighted for implementation.
Main Results:
- Provides practical indications for data acquisition relevant to psychological research.
- Describes essential data preprocessing steps tailored for psychological datasets.
- Lists analytical techniques and programming tools (R, Python) for Big Data research in psychology.
Conclusions:
- Familiarizing with data science terminology is crucial for psychologists to overcome initial apprehension.
- This overview aims to build a foundational understanding of Big Data research steps and foster a common language for interdisciplinary collaboration.
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