The new statistics (Cumming, 2007) are simply not that new anymore. Effect sizes have been around since the 1940-1950s (Huberty, 2002), yet despite their popularity in recent decades (speculatively in reaction to known flaws in NHST) there are still problems surrounding reporting practices, misconceptions about effect sizes, and potential gaps in our pedagogical approaches to teaching about effect sizes. Within biomedical and psychological research, there are numerous effect sizes measures which have can be categorized into 8 techniques for estimating and interpreting effect sizes (Cook et al., 2014; Lakens & Caldwell, 2019). This project focuses on first assessing the prevalence of these 8 techniques and then on identifying technique and/or instructor related barriers which hinder the use of these techniques within classrooms. Hopefully, steps towards mitigating those barriers and closing the educational gap for students as they transition to becoming researchers and consumers of information can be found and implemented.

Data cleaning is a crucial skill required by every researcher, yet there is little time dedicated (or even available) to formally teach data cleaning principles and procedures within psychology statistics classes. The aim of this current project is to develop online learning-focused content centered around data cleaning as way to promote a deeper understanding of data. However, designing this type of content, and its organizational structure, needs to consider both user experience (UX) and behavioural design right from its inception if it aims to effectively accomplish its learning objectives. Evaluation methods for UX will be discussed along with how behavioural design elements can be leveraged to increase the overall UX and promote the desired learning outcomes.

The goal of this workshop is to demonstrate how to write safe, effective, and intuitive R code for Monte Carlo simulation experiments presented at the PsyPag & MSCP-Section Simulation Summer School

Free-recall paradigms have greatly influenced our understanding of memory. The majority of this research involves laboratory-based events (e.g., word lists) that are studied and tested within minutes. This literature shows that adults recall events in a temporally organized way, with successive responses often coming from neighboring list positions (i.e., temporal clustering) and with enhanced memorability of items from the end of a list (i.e., recency). Temporal clustering effects are so robust that temporal organization is described as a fundamental memory property. Yet relatively little is known about the development of this temporal structure across childhood, and even less about children’s memory search for real-world events occurring over an extended period. In the present work, children (N = 144; 3 age groups: 4–5-year-olds, 6–7-year-olds, 8–10-year-olds) took part in a 5-day summer camp at a local zoo. The camp involved various dynamic events, including daily animal exhibit visits. On day 5, children were asked to recall all the animals they visited. We found that overall recall performance, in terms of number of animals recalled, improved steadily across childhood. Temporal organization and recency effects showed different developmental patterns. Temporal clustering was evident in the response sequences for all age groups and became progressively stronger across childhood. In contrast, the recency advantage, when characterized as a proportion of total responses, was stable across age groups. Thus, recall dynamics in early childhood parallel that seen in adulthood, with continued development of temporal organization across middle to late childhood.

Open, reproducible, and replicable research practices are a fundamental part of science. Training is often organized on a grassroots level, offered by early career researchers, for early career researchers. Buffet style courses that cover many topics can inspire participants to try new things; however, they can also be overwhelming. Participants who want to implement new practices may not know where to start once they return to their research team. We describe ten simple rules to guide participants of relevant training courses in implementing robust research practices in their own projects, once they return to their research group. This includes (1) prioritizing and planning which practices to implement, which involves obtaining support and convincing others involved in the research project of the added value of implementing new practices; (2) managing problems that arise during implementation; and (3) making reproducible research and open science practices an integral part of a future research career. We also outline strategies that course organizers can use to prepare participants for implementation and support them during this process.

The purpose of this tutorial is to discuss and demonstrate how to write safe, effective, and intuitive computer code for Monte Carlo simulation experiments containing one or more simulation factors. Throughout this tutorial the SimDesign package (Chalmers, 2020), available within the R programming environment, will be adopted due to its ability to accommodate a number of desirable execution features. The article begins by discussing a selection of attractive coding strategies that should be present in Monte Carlo simulation experiments, showcases how the SimDesign package can satisfy many of these desirable strategies, and provides a worked mediation analysis simulation example to demonstrate the implementation of these features. To demonstrate how the package can be used for real-world experiments, the simulation explored by Flora and Curran (2004) pertaining to a confirmatory factor analysis robustness study with ordinal response data is also presented and discussed.

Objective: We conducted 2 experiments using machine learning to better understand which lineup looking behaviors postdict suspect guilt., Hypotheses: We hypothesized that (a) lineups with guilty suspects would be subject to shorter viewing duration of all images and fewer image looks overall than lineups with innocent suspects, and (b) confidence and accuracy would be positively correlated. The question of which factors would combine to best postdict suspect guilt was exploratory. Method: Experiment 1 included 405 children (6–14 years; 43% female) who each made 2 eyewitness identifications after viewing 2 live targets. Experiment 2 included 342 adult participants (Mage = 21.00; females = 75%) who each made 2 identifications after viewing a video including 2 targets. Participants made identifications using an interactive touchscreen simultaneous lineup in which they were restricted to viewing one image at a time and their interaction with the lineup was recorded. Results: In Experiment 1, five variables (filler look time, suspect look time, number of suspect looks, number of filler looks, and winner look time) together postdicted (with a 67% accuracy score) target presence. In Experiment 2, four variables (number of suspect looks, number of filler looks, number of loser looks, and winner looks) together postdicted (with a 73% accuracy score) target presence. Conclusions: Further exploration of witness search behaviors can provide context to identification decisions. Understanding which behaviors postdict suspect guilt may assist with interpretation of identification decisions in the same way that decision confidence is currently used.

Seniors are at particular risk for problem gambling due to lifecycle events such as retirement, loss of a partner, limited income, and normative age-related cognitive decline. In addition to these person-level factors, the environmental context may also influence seniors’ gambling. We analyzed the Ontario Seniors Gambling data (N = 2,103) to explore the effects of person-level, environmental, and person-level by environmental effects of gambling outcomes. Person-level predictors included attitudes towards the benefits and harms of gambling, whether gambling should be legalized, and a set of gambling approach and avoidance motives. Environmental predictors included proximity to the nearest casino, being part of an organized group visiting the casino, and coming alone to the casino. Three outcomes were modeled: frequently gambling at slot and electronic gaming machines (EGMs) which are increasingly favoured by problem gamblers, gambling expenditure, and problem gambling. Older age, male sex, avoidance motives for gambling, and geographic proximity were positively associated with frequent gambling at slot machines and EGMs. Female sex, negative views about the legalization of certain types of gambling, approach and avoidance motives, visiting the casino without an organized group or coming alone, and frequently playing slot machines and EGMs predicted higher gambling expenditure. Problem gambling was more likely in younger unmarried and employed seniors who were born out of Canada. Additionally, problem gambling was also more likely in seniors who considered gambling more beneficial than harmful, endorsed avoidance motives, frequently played slots and EGMs, and had relatively high gambling expenditures. Taken together, these findings clarify linkages between sociodemographic, motivational, attitudinal factors, and environmental factors, on the one hand, and economic and psychosocial gambling outcomes, on the other hand. Finally, the findings converge with extant literature in identifying an association between playing slots and EGMs and problem gambling. These results have implications for policymakers and researchers, and highlight points of intervention to reduce the incidence of problem gambling among seniors in Ontario and beyond.