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. 2025 May;1547(1):220-232.
doi: 10.1111/nyas.15350. Epub 2025 Apr 27.

A network analysis of statistics anxiety symptoms and their antecedents in UK higher education students

Joshua J March  1 , David Hamilton  1 , Dawn McCormack  1 , Ross Brisco  2 , Amy Grech  2
Affiliations

A network analysis of statistics anxiety symptoms and their antecedents in UK higher education students

Joshua J March et al. Ann N Y Acad Sci. 2025 May.

Abstract

Statistics anxiety is a widespread, multifaceted phenomenon affecting many students in higher education. Feelings of excessive worry when exposed to statistical content impact student performance and heighten negative perceptions of statistics. While many factors have been identified as relevant antecedents of statistics anxiety, it is unclear how they relate to different components of this phenomenon, and which factors are most influential. Additionally, no research has investigated the impact of peer attitudes toward statistics anxiety. The current study describes a preregistered network analysis of statistics anxiety, peer attitudes, and related variables with a sample of 279 UK higher education students. After performing reliability checks, results support the distinction made in previous literature between attitudes toward statistics and statistics anxiety per se. The former were influenced by feelings of statistics self-efficacy, age, and peer attitudes toward statistics, and the latter was influenced by negative problem orientation and intolerance of uncertainty. The most influential nodes were the negative problem orientation variables, inhibitory anxiety, and interpretation anxiety. The findings are discussed in relation to addressing statistics anxiety from multiple angles.

Keywords: network analysis; peer attitudes; statistics anxiety.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Network estimated from the dataset.
FIGURE 2
FIGURE 2
Bootstrapping (n = 1000) to estimate the stability of the edge‐weights for the estimated network.
FIGURE 3
FIGURE 3
Estimates of strength, closeness, and expected influence calculated for all nodes in the dataset. Sorted in descending order for strength.
FIGURE 4
FIGURE 4
Case‐dropping bootstrapping to estimate the stability of the centrality estimates across different subsets of the data.
See this image and copyright information in PMC

References

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