Dr Ellen Peters visits this week to give a presentation in the SWE Colloquia series. Dr. Peters is a Distinguished Professor of Psychology, Director of the Decision Sciences Collaborative, Professor of Medicine in the Department of Internal Medicine (by courtesy), and Professor of Marketing & Logistics at the Fisher College of Business (by courtesy) at The Ohio State University.

Innumeracy in the lab and in the wild: A focus on efficacy and action with numbers

Research has demonstrated the importance of objective numeracy (defined as the ability to understand and use probabilistic and other mathematical concepts) to judgments and decisions in the lab and in life.  However, not everybody can understand and use numeric information effectively. In addition, researchers have largely ignored the potentially motivating power of numeric self-efficacy, independent of objective numeracy. In this talk, I’ll introduce: 1) the extent of innumeracy, 2) why these differences matter, and 3) how objective numeracy and subjective numeracy (numeric self-efficacy) relate to numeric persistence and life outcomes. Objective numeracy and numeric efficacy capture distinct psychological construct important to judgments and decisions.

The first speaker in this semester’s SWE Colloquia series was Douglas Bates, Professor Emeritus, Department of Statistics, University of Wisconsin, Madison.

Fitting complex mixed-effects models to large datasets

Mixed-effects models are a type of statistical model that incorporate both fixed-effects parameters and random effects.  From the point of view of experimental design, fixed-effects are associated with experimental factors (e.g. priming or not) or with covariates that have a fixed, reproducible set of levels (e.g. sex of the subject, socio-economic status).  Random-effects are associated with blocking factors – a known source of variability for which we wish to control.  The most common such blocking factor is “Subject”.  In many studies “Item” will be another blocking factor.  Mixed-effects models provide a way of taking into account these different sources of variability in the analysis of the data, but only in the last decade or so has software been available to fit complex mixed-effects models, especially those with crossed random effects such as “Subject” and “Item”, to large data sets.  As usually happens, the models that researchers wish to fit are becoming more and more complex and the data sets are becoming larger and larger, straining the capabilities of some of the software used to fit these models.  Dr Bates’ presentation on 20 June included a discussion of these models, some of the software used to fit them, and future directions in R/lme4 and the recently developed Julia language.