Data Science and Machine Learning for Psychological Research

In spite of the popularity of data science and machine learning methods in industry, these technologies and methodologies have yet to be widely applied in academia. Put bluntly, most academic research endeavors are still tied to traditional statistics, especially the frequency school, which relies on the p-value for significance testing. It is generally agreed that originally traditional statistical procedures were developed for small-scale studies. However, very often small-sample studies generate findings that are typically non-replicable, resulting in a replication crisis. In the case of big data analytics, classical methods will result in overfitting or model misspecification. In addition, traditional data analysis focuses on structured data, but unstructured data, such as posts on social media and responses to open-ended questions in surveys, are untapped. Nevertheless, today researchers equipped with data science methods have the opportunity of investigating various psychological phenomena through a new lens. To uncover new insights on different psychological topics, we encourage the submission of innovative research projects that utilize artificial intelligence and machine learning, ensemble methods, or text mining.

The goal of this Research Topic is to :
• Develop innovative predictive models for improving academic research related to psychology using big data analytics.
• Introduce cutting-edge technologies for data management (e.g., Snowflake, Dataiku, TIBCO, Qilk…etc.) and data analytics (e.g., Amazon SageMaker, SAS Viya, Tableau…etc.) related to psychological research.
• Utilize natural language processing and text mining to analyze unstructured data related to psychological research (e.g., blogs, posts on social media, responses to open-ended questions in surveys, discussions in focus groups). Discuss how the mixed-method approach (quantitative and qualitative) can be enhanced by incorporating text mining.
• Discuss how data science and machine learning methods can overcome or alleviate the replication crisis.
• Discuss issues, limitations, and challenges of applying data science and machine learning in psychological research.
• Discuss ethical issues related to the impact of AI on academic research. For example, should research journals ban co-authorship between ChatGPT and human authors? Should IRB set restrictions on conducting experiments on social media that involve psychological manipulation (e.g., influence voting behaviors)?

This Research Topic welcomes:
• Original research based on primary or secondary (archival) data
• Systematic review that summarizes the current status and trend of data science and machine learning methods
• Methodological articles that discuss emerging data management and analytical approaches related to psychological research.
• Case study that documents a successful example of improving psychological research by employing data science and machine learning.