Using network science to understand statistics anxiety among college students.

Abstract

Many psychology students experience statistics anxiety, which negatively affects academic performance and necessitates the application of new methods to understand the underlying nature of statistics anxiety. In the present analysis we use the tools of network science, a set of mathematical techniques to examine the relationships between entities in a system, to create networks from the items in the Statistical Anxiety Rating Scale (STARS), a widely used instrument to measure statistics anxiety. Separate networks were constructed and analyzed for students who were high or low in statistics anxiety. The items in the STARS were the nodes in the network, with connections placed between nodes if responses to those 2 items in the STARS were correlated. The network analysis revealed differences in the overall structure of high-and low-anxiety networks and in which nodes were most "important" in the 2 groups, suggesting that different interventions might be required depending on the level of statistics anxiety experienced by the student. Furthermore, our results suggest that the most effective interventions for students with very high levels of statistics anxiety would need to specifically address the (incorrect) perception that strong mathematical ability is a necessary prerequisite for excelling in statistics. Centrally located nodes in the network may help instructors appropriately target classroom interventions to help all students succeed regardless of the level of statistical anxiety experienced by students.

Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School

“…Even in the stance toward school, anxietyrelated associations to "stress" and "anxiety" are present. The emerging picture is a generally anxious perception that students have of the school setting, in particular toward well-known anxiety eliciting STEM disciplines [1,3,5] and, more importantly, toward the assessment system of grading and tests, which has also been documented in other educational systems [2]. Even within this negative aura, students are able to identify the important role played by school in promoting learning and education, which are positive concepts associated with "school" itself.…”

“…Increasingly more studies in Education Science suggest that a large number of students experience anxiety while in educational settings [1,2,3,4,5]. Stress can arise from multiple sources.…”

“…Empirical and theoretical research in the cognitive sciences identified these mental representations of knowledge as components of a way more complex system called mental lexicon, a repository of knowledge apt at information acquisition, processing, and use [21]. The recent adoption of network science tools has shown how the large-scale, associative structure of word knowledge in the mental lexicon is highly informative of a wide variety of cognitive processes such as lexical processing [19,22,23,24], learning and cognitive development [25,26,27], text structuring and writing styles [28,29,30,31], creativity [32,33], and expertise levels in specific domains [34,5]. Analogously, forma mentis networks act as approximated reconstructions on the mental constructs built by individuals in their associative mental lexicon, representing their perceptions of the outer world [16,17].…”

The Effect of Group Polarization on Opposition to Donald Trump

“…First, we identified recurrent themes in the conversations (Krueger & Casey, 2015;Stewart et al, 2007) and present qualitative results and interpretation for these themes. Second, we employed natural language processing, a machine-learning technique used to quantitatively identify sentiment in language (Crossley et al, 2017), and used results from these analyses to construct networks (e.g., Eiler, Al-Kire, Doyle, & Wayment, 2019;Siew, McCartney, & Vitevitch, 2019) to identify underlying psychological processes leading to group polarization using quadratic assignment procedures (see Robins, Lewis, & Wang, 2012 for a review).…”

Studying Statistics Anxiety Requires Sound Statistics: A Comment on Siew, McCartney, and Vitevitch (2019)

“…This effect is also known as Berkson's bias (Berkson, 1946). As such, it is not clear to what extend the results by Siew et al (2019) are biased. The issue of Berkson's bias has only recently been discussed in the literature on psychological network models (De Ron et al, 2019).…”