Predicting Math Anxiety: A Multidimensional Analysis of Psychological, Attitudinal, and Contextual Factors

Math anxiety is a multidimensional phenomenon shaped by cognitive, attitudinal, and emotional factors. While traditional research has predominantly relied on linear models to explain its predictors, recent work points to non-linear, dynamic, and reciprocal relationships between psychological, attitudinal, and demographic variables. There remains a notable gap in large-scale, cross-cultural research that systematically compares the relative influence of these factors using advanced, comparative modelling techniques. This study addresses that gap by conducting a quantitative analysis of data from the SMARVUS dataset, a large-scale international survey of 12,570 university students across 100 institutions in 35 countries. The dataset includes validated measures of statistics anxiety, test anxiety, trait anxiety, social interaction anxiety, and self-efficacy, as well as demographic information such as gender, age, learning difficulties, and academic background. Using both Ordinary Least Squares and logistic regression models, the study pursues two aims: (1) to examine the relative contribution of individual and contextual factors to math anxiety, and (2) to identify the strongest predictors of elevated math anxiety in university populations. Results show that psychological and attitudinal variables, particularly statistics anxiety, test anxiety, creativity anxiety, and intolerance of uncertainty, outweigh demographic factors in predicting math anxiety. These findings position math anxiety as primarily rooted in students’ emotional and cognitive responses to learning environments. The study concludes that effective interventions should prioritise fostering resilience, self-efficacy, and emotional safety, reframing math anxiety as a modifiable experience rather than an immutable trait.