Simulation Studies as a Tool to Understand Bayes Factors

When social scientists wish to learn about an empirical phenomenon, they perform an experiment. When they wish to learn about a complex numerical phenomenon, they can perform a simulation study. The goal of this Tutorial is twofold. First, it introduces how to set up a simulation study using the relatively simple example of simulating from the prior. Second, it demonstrates how simulation can be used to learn about the Jeffreys-Zellner-Siow (JZS) Bayes factor, a currently popular implementation of the Bayes factor employed in the BayesFactor R package and freeware program JASP. Many technical expositions on Bayes factors exist, but these may be somewhat inaccessible to researchers who are not specialized in statistics. In a step-by-step approach, this Tutorial shows how a simple simulation script can be used to approximate the calculation of the Bayes factor. We explain how a researcher can write such a sampler to approximate Bayes factors in a few lines of code, what the logic is behind the Savage-Dickey method used to visualize Bayes factors, and what the practical differences are for different choices of the prior distribution used to calculate Bayes factors

Assessing and Reporting Non-Cognitive Skills: A Cross-Canada Survey

Canadian educational jurisdictions require teachers to assess and report upon aspects of student performance beyond academic achievement. These skills and competencies are often called “non-cognitive skills” (NCS). This study used document analysis to determine which NCS are assessed across provinces, identifying commonalities, variations in the skills assessed, and how these skills are reported. While substantial variability was found in the labelling of these skills, the assessments of collaboration, responsibility, organization, and independence commonly appeared. Further, these skills are typically reported upon using a 3 or 4-point rating scale. Of interest, provinces typically used economic arguments to justify their inclusion of NCS in students’ report cards

What Motivates People to Become Teachers.

There is no shortage of attempts to explain why people want to be teachers, and many of these attempts are used to frame this paper. Most of the research cited comes from the United States, some comes from Australia, and a few references are made to research from other countries. What attracts people to teaching has been a popular research theme for several decades, and it has been simultaneously quite popular in many countries. In Israet for example, 9.9% of all educational research done between 1974 and 1985 related to teachers\u27 education, attitudes and behaviours (Peritz, 1989:62). Prominent among the questions were why people wanted to be teachers in the first place, and what motivated them to make the choice. From almost all the studies, something loosely called altruism provided the most compelling answer to the question of why the choice was made. Joseph and Green\u27s (1986:31) study offers the conclusion: .. .indeed, there may be few reasons for considering the teaching profession except the altruistic motive

The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development

Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automation of diagnosis and monitoring of neurological disorders using non-invasive, cost effective methods within a patient’s home could improve accessibility to testing. Furthermore, said testing could be conducted over a longer period, enabling greater confidence in diagnoses, due to increased data availability. This work proposes a new method to assess the movements in children. Twelve parent and infant participants were recruited (children aged between 3 and 12 months). Approximately 25 min 2D video recordings of the infants organically playing with toys were captured. A combination of deep learning and 2D pose estimation algorithms were used to classify the movements in relation to the children’s dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children’s complexity of movements when interacting with toys as well as their posture. Such classifications and the movement features could assist practitioners to accurately diagnose impaired or delayed movement development in a timely fashion as well as facilitating treatment monitoring