Research

Research Interests

  • Casual Inference and Graphical Models in Social and Health Sciences
  • Time Series Methods
  • Big Data, Large Observational Datasets, Routinely Collected NHS Data
  • Modelling Heterogeneous Patterns of Behavior
  • Missing Data and Attrition in Longitudinal Studies
  • Innovations/challenges in the Teaching of Statistics in Social and Applied Sciences

Current research projects

2022, Medical Research Foundation, (~£100,000), Capturing the daily life experiences and difficulties of adolescents with ADHD, (CO-I, Methods Lead), with Murray, A (PI). and Obsuth, I.

2021, North West Cancer Research (~£250,000), IMmunotherapy and PAlliative CAre Trajectories (IMPACT): a mixed methods study (CO-I, Methods Lead) with Brearley, S. (PI), Gadoud, A., Walshe, C.)

Papers

In Submission/Review

Diprossimo, L, Ushakova, A, Zoski, J, Gamble, H, Irey, R, Cain, K, 2021, Vocabulary Scaffolding  Features and Young Readers’ Comprehension of Digital Text: Insights from a Big Observational Dataset.  Preregistration DOI:10.17605/OSF.IO/62C4Q

Murray, A., Ushakova, A. Bianchi, A., Booth, T., Lynn, P., 2021, Dynamic predictors of attrition in Understanding Society: a large 9-wave population-representative study.

Speyer, L. G., Ushakova, A., Blakemore, S. J., Murray, A. L., & Kievit, R. (2022). Testing for Within× Within and Between× Within Moderation using Random Intercept Cross-Lagged Panel Models. preprint DOI: 10.31234/osf.io/wktrb

Best, K. L., Murray, A.L., Speyer, L.G, Ushakova, A. 2021, Prediction of Attrition in Large Longitudinal Studies: Tree-based methods versus Multinomial Logistic Models. preprint DOI: 10.31235/osf.io/tyszr

Ushakova, A., McKenzie, K., Hughes, C., Murray, A.,2021, Measurement Invariance of GHQ-12 Across Student and Non-Student Populations using a large UK Longitudinal Study. 

Ushakova, A., McKenzie, K., Hughes, C., Murray, A., 2021, Analyzing Socio-Economic Risk Factors on Mental Health in Students as measured by GHQ-12 and WEMWBS: UK Understanding Society

Published/Accepted

Ushakova A., Taylor S.A., Killick R. (2022),.Multilevel Changepoint Inference for Periodic Data Sequences. preprint DOI: 10.13140/RG.2.2.35838.51526 Forthcoming in Journal of Computational and Graphical Statistics.

Speyer, L. G., Hall, H. A., Ushakova, A., Luciano, M., Murray, A. L., & Auyeung, B. (2022). Within-Person Relations between Domains of Socio-Emotional Development during Childhood and Adolescence ,  preprint DOI: https://doi.org/10.31234/osf.io/wn6pb  Forthcoming in Research on Child  and Adolescent Psychopathology.

Murray, A. L., Ushakova, A., Speyer, L., Brown, R., Auyeung, B., & Zhu, X. (2021). Sex/gender differences in individual and joint trajectories of common mental health symptoms in early to middle adolescence. JCPP Advances, e12057.

Speyer, L., Ushakova, A., Hall, A., Luciano, M., Auyeung, B., Murray, A. L., 2021, Analyzing Dynamic Change in Children’s Socio-Emotional Development using the Strengths and Difficulties Questionnaire in a large UK Longitudinal Study. Forthcoming in Journal of Abnormal Psychology

Speyer, L., Hall, A., Ushakova, A., Murray, A. L., Luciano, M., Auyeung, B., 2021, Longitudinal Effects of Breastfeeding on Parent- and Teacher-Reported Child Behaviour. Archives of Disease in Childhood  DOI: https://doi.org/10.1136/archdischild-2020-319038

 Speyer, L., Hall, A., Ushakova, A., C., 2020, Links between Perinatal Risk Factors and Maternal Psychological Distress: Network Analysis. Acta Obstetricia et Gynecologica Scandinavica (AOGS) DOI: https://doi.org/10.1111/aogs.14056

Ushakova, A. and Mikhaylov,S.J., 2020, Big Data to the Rescue? Challenges in Analysing Granular Household   Electricity Consumption in the United Kingdom. Energy Research and Social Science, Vol 64 .DOI: https://doi.org/10.1016/j.erss.2020.101428

Ushakova, A. and Murcio, R., 2018, . Interpreting Smart Meter Data of UK Domestic Energy Consumers. In Longley P., Cheshire J., Singleton A, Consumer Data Research (pp. 120-137), London: UCL Press.  ISBN: 9781787353886

Ushakova, A. and Mikhaylov,S.J., 2018, Predicting Energy Customer Vulnerability from Consumption Behavior, Working Paper, Consumer Data Research Centre DOI:10.13140/RG.2.2.20553.19047