Speaker: Mark Adkins, York University
Department of Psychology
Title: Predictors of Problem Gambling Among Seniors in Ontario
Abstract: Seniors are a particularly vulnerable group to gambling problems due to age-related cognitive decline, limited income, and other lifecycle events such as the loss of a partner. Using the Ontario Seniors Gambling data (N=2,103), an analysis was conducted to explore the effects of person-level, environmental, and person-level by environmental effects on gambling related outcomes. The primary analyses focused on a subset of meaningful predictors, demographic covariates, and gambling outcomes which were initially identified by the original analyses conducted by McCready et al. (2014). Logistic regression models were used to examine the predictors and possible interactions. Being married and formally employed were negative predictors of problem gambling, while specific avoidance motives, attitudes regarding the relative benefits versus harms of gambling, frequency of slot play, and spending more than $1000 annually all predicted problem gambling.
Speaker: Joo Ann Lee, York University
Department of Psychology
Title: Statistical Modeling: The Two Cultures, with Implications for Quantitative Psychology
Abstract: Dr. Leo Breiman’s 2001 article discusses aspects of statistical modeling and the seemingly two opposite cultures: stochastic and algorithmic modeling. Stochastic modeling, akin to ‘traditional statistics’ first assumes that the data is generated by a data model such as Y = B0 + B1X1. The values of the parameters are estimated by means of a modeling technique such as linear regression, structural equations modeling, and so on, that is appropriate for the data model. Algorithmic modeling (also known as ‘machine learning’) on the other hand, makes no such assumption and modeling techniques are used to uncover the functional relations of the data. Dr. Breiman argued that stochastic modeling, to which many quantitative psychologists are trained in, may be limited in use and potentially misleading. I will introduce the key ideas of the paper and discuss the potential implications for quantitative psychology.
