Assessing Well-being in Societies: Issues for Consideration
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.
Experience Sampling and Well-being Research
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. In this video, he shares some of the current trends in well-being using ESM/ESA methods by presenting some of the research that has been conducted using ExpiWell. Additionally, he highlights some key technological features necessary to conduct this type of research and how ExpiWell has enabled the success of these projects.
Big Data and Psychology
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 also 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.
Machine Learning Measurement Bias
Given significant concerns about fairness and bias in the use of artificial intelligence (AI) and machine learning (ML) for assessing psychological traits, I provide a conceptual framework for investigating and mitigating machine learning measurement bias (MLMB) from a psychometric perspective. I provide a definition of MLMB and discuss how biased data and algorithm training bias are potential sources of MLMB. Further, I explain how these potential sources of bias may manifest during ML model development and share initial ideas on how to mitigate them. This talk was presented at the 2021 DAISY Conference "Tackling Bias in Data Science: from Prediction to Intervention".
Bias, Fairness, and Validity in Graduate Admissions: A Psychometric Perspective
As many schools and departments are considering the removal of the Graduate Record Examination (GRE) from their graduate admission processes to enhance equity and diversity in higher education, controversies arise. From a psychometric perspective, we see a critical need for clarifying the meanings of measurement bias and fairness, in order to create common ground for constructive discussions within the field of psychology, higher education, and beyond. We critically evaluate six major sources of information that are widely used in graduate admission assessment: grade point average, personal statements, resumes/CVs, letters of recommendation, interviews, and GRE. We review empirical research evidence available to date on the validity, bias, and fairness issues associated with each of these admission measures, and identify potential issues that have been overlooked in the literature. We conclude by suggesting several directions for practical steps to improve the current admissions decisions, as well as highlighting areas in which future research would be beneficial.
Experience Sampling Method (ESM) Webinar
This is a webinar on the Experience Sampling Method (ESM) and Ecological Momentary Assessment (EMA) conducted by Dr. Louis Tay. This webinar 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.
Advancing the Measurement of Subjective Well-Being
In the talk, Dr. Tay discusses how to advance the measurement of subjective well-being, especially in the contexts of national assessments.