This paper reports the statistical and probabilistic reasoning of young children in terms of randomness, variability, and data representations in the context of informal inferential reasoning (IIR). Using the IIR approach, a task was designed and conducted one-on-one with 28 children aged 5 to 6 years old, in a case study setting. The researcher…

A linear model is often used to find the effect of a binary treatment D on a noncontinuous outcome Y with covariates X. Particularly, a binary Y gives the popular "linear probability model (LPM)," but the linear model is untenable if X contains a continuous regressor. This raises the question: what kind of treatment effect does the…

A commonly-held belief is that many university students are 'assessment-driven'; that is, students engage more with activities that are assessed compared with those that are not. 'Incentivised engagement' includes the practice of providing incentives (such as marks or otherwise) for students to engage in particular activities. Perusall is a social…

Global digital advancements provide postsecondary students with ample digital job opportunities. Thus, it is crucial to understand the psychological determinants that affect postsecondary students' choices of a digital career. Self-efficacy and interest are two widely used psychological determinants in the choice behavior of individuals. However,…

Background: During the COVID-19 pandemic, stay-at-home (SAH) orders were instituted to limit geographic movement of the population and decrease the spread of the virus. Parents made decisions about how to keep themselves and their children safe which may have led to differing compliance with SAH orders and affected parenting. Objective: We…

Experiments in psychology or education often use logistic regression models (LRMs) when analyzing binary outcomes. However, a challenge with LRMs is that results are generally difficult to understand. We present alternatives to LRMs in the analysis of experiments and discuss the linear probability model, the log-binomial model, and the modified…

This paper reports on a study that investigated young children's responses to a range of probabilistic tasks. A central aspect of the study was our examination of the children's use of subjective thinking. Most research that has been conducted in relation to young children's probabilistic thinking has focused on the extent to which young children…

Artificial neural networks (NN) can help researchers estimate propensity scores for quasi-experimental estimation of treatment effects because they can automatically detect complex interactions involving many covariates. However, NN is difficult to implement due to the complexity of choosing an algorithm for various treatment levels and monitoring…

This study explores the epistemic considerations that seemed to govern thirty-one Indian teachers' beliefs while teaching certain concepts of probability, especially using random generators. It focuses on knowing how these teachers make transitions between the different interpretations of probability, namely, Logical (sometimes also known as…

To promote the development of young children's probabilistic intuitions through experience, we focus on the dual nature of probability related to both belief (subjective notion) and frequency (objective notion). This paper reports on the responses of 7-8-year-olds on two tasks used to bridge subjective and objective notions of probability. We…

Background: Meta-analysis refers to the statistical methods employed to combine results of several empirical studies in a topic of interest (Hedges & Olkin, 1985). Meta-analysis is often included in literature review studies to quantitatively analyze data from a collection of studies (Valentine et al., 2010). The statistical power of a…

Procedural learning is key to optimal skill learning and is essential for functioning in everyday life. The findings of previous studies are contradictory regarding whether procedural learning can be modified by prioritizing speed or accuracy during learning. The conflicting results may be due to the fact that procedural learning is a multifaceted…

How do we deal with unlikely witness testimonies? Whether in legal or everyday reasoning, corroborative evidence is generally considered a strong marker of support for the reported hypothesis. However, questions remain regarding how the prior probability, or base rate, of that hypothesis interacts with corroboration. Using a Bayesian network…

Network meta-analysis (NMA) is a statistical technique for the comparison of treatment options. Outcomes of Bayesian NMA include estimates of treatment effects, and the probabilities that each treatment is ranked best, second best and so on. How exactly network topology affects the accuracy and precision of these outcomes is not fully understood.…

Ionization of amino acids (AA) is very important concept in biochemistry. We integrate the mathematical concept of probability with biochemically relevant process of AA ionization. We visualize the ionization process with Mathematica software discussing intramolecular interactions between weakly acidic/basic functional groups and charge--pH…