Talks
Selected presentations by Dr. Louis Tay on assessing well-being, big data, machine learning bias, and experience sampling. Click any thumbnail to watch on YouTube.
Dr. Louis Tay makes the case that well-being should serve as the ultimate criterion for the organizational sciences — discussing why employee well-being matters in its own right and how organizations and researchers can place it at the center of theory, measurement, and practice.
Dr. Louis Tay presents his talk at the Westwood Lecture Series, exploring emerging opportunities and challenges in assessing well-being in the modern world and offering insights into effective measurement in today's society.
Dr. Tay is featured as a guest on the McGill–UofT Well-being Research Seminar Series to discuss the psychometric, methodological, and data science issues to consider in assessing well-being in societies.
Dr. Tay presents ExpiWell, a platform he created for researchers to more easily perform experience sampling methodology and ecological momentary assessment for well-being outcomes. He shares current trends in well-being using ESM/ESA methods, highlights key technological features necessary to conduct this type of research, and explains how ExpiWell has enabled the success of these projects.
There is an ever-growing interest in big data across multiple areas of psychology. In this talk, Dr. Tay presents the key reasons for this interest and how it can advance psychological science theoretically and practically. At the same time, psychology can contribute considerably to big data research, particularly in big-data assessments where there is a significant need to evaluate and enhance reliability and validity and reduce machine learning bias.
Given significant concerns about fairness and bias in the use of AI and machine learning for assessing psychological traits, Dr. Tay provides a conceptual framework for investigating and mitigating machine learning measurement bias (MLMB) from a psychometric perspective. This talk was presented at the 2021 DAISY Conference, “Tackling Bias in Data Science: from Prediction to Intervention.”
From a psychometric perspective, Dr. Tay and colleagues clarify the meanings of measurement bias and fairness and critically evaluate six major sources of information widely used in graduate admissions: grade point average, personal statements, resumes/CVs, letters of recommendation, interviews, and the GRE—reviewing the validity, bias, and fairness issues associated with each and suggesting directions for improving admissions decisions.
A webinar on the Experience Sampling Method (ESM) and Ecological Momentary Assessment (EMA) conducted by Dr. Louis Tay. It covers the history of ESM and EMA; trends in ESM and EMA in psychological and organizational research; the practical issues in implementing ESM and EMA; statistical analyses of ESM and EMA data; and software implementation in ExpiWell.
Dr. Tay discusses how to advance the measurement of subjective well-being, especially in the contexts of national assessments.