📏psychological measurement topic overview

I believe robust psychological measurement is fundamental to reliable psychological research. So, although my work spans diverse areas including neurodiversity, statistics education, and meta-science, many of the questions I ask centre on how psychological constructs are defined, measured, and interpreted.

As such, this page brings together the measurement-focused elements of my work, but some of the projects also appear elsewhere on the website.

My interest in measurement grew from repeatedly encountering situations where the conclusions that could be drawn from research depended on the quality of the measures being used and finding insufficient evidence of their quality. Accordingly, much of my research focuses on evaluating the validity evidence for existing measures, investigating whether scales function equivalently across different groups, and examining the extent to which commonly used instruments capture distinct constructs.

This work has included my PhD research on the distinctiveness of statistics anxiety and mathematics anxiety, as well as the co-development of a measure of academic learning experiences, factor structure re-evaluations, and ongoing studies measurement invariance across languages and populations. I am also interested in broader, meta-scientific questions about measurement practice within psychology, including how scales are developed, validated, and reported.

Across all of this work, my goal is to contribute to more robust, transparent, and meaningful measurement, ensuring that the conclusions drawn from psychological research are built on strong foundations.

✨Research highlights 

📝 A Fault in our STARS? Re-examining the overlap between Statistics Anxiety & mathematics anxiety

Jenny Terry, the SMARVUS Team, and Andy Field (2026)

This programme of research examined whether the Statistics Anxiety Rating Scale (STARS) captures anything distinct from the Revised Mathematics Anxiety Rating Scale (R-MARS). Across two large-scale, multi-sample, pre-registered studies, multiple psychometric and other quantitative analyses suggested remarkably little separation between the constructs. The findings raise important questions about how statistics anxiety has been conceptualised and measured in higher education research. 

Read the pre-print of the first and second studies →

📝 New autism-inclusive Questionnaire for exploring academic learning experiences

Sophie Anns, Clare Davis, Jessica Millington, & Jenny Terry (2026)

The Academic Learning Experiences Questionnaire (ALEQ) was developed to better understand how autistic students experience learning and assessment in higher education. The study identified seven distinct dimensions of learning experience and found differing response patterns between autistic and non-autistic students across several domains. The ALEQ offers a structured tool for future research exploring neurodiversity and learning in higher education.

Coming soon to Autism in Adulthood. 

Read the pre-print →

🚧current Projects & PRe-PRINTS

Lead/Senior Researcher:

• Are self-reported statistics grades accurate (enough)? [PRE-PRINT]

• Which construct(s) is the Statistics Anxiety Rating Scale (STARS) actually measuring?

• Does measurement invariance hold for the Statistics Anxiety Rating Scale (STARS) across languages?

• How can we best capture AuDHD with self-report scales?

• Re-examining the LSAS-SR factor structure in a large international sample.

• Examining the validity evidence for a revised version of the CRT using IRT.

Contributing Researcher:

• Are anxiety scales measurement-invariant across neurotypes?

📑publications

Sophie Anns, Clare Davis, Jessica Millington, & Jenny Terry. (2026; in press). Validating the Academic Learning Experiences Questionnaire: Microtransitions, Sensory Reactivity and Cognitive-Attentional Dimensions in Autistic University Students. Autism in Adulthood.

Janos Salamon, Jenny Terry, András Zsidó, Balazs Aczel, & Tamás Nagy. (2026). Statistics anxiety: validity, measurement invariance, and educational correlates in the Hungarian STARS. Journal of Statistics and Data Science Education, 1–13. https://doi.org/10.1080/26939169.2026.2646167 

🗂️posters, presentations, & RESOURCES