Dr Andrew Bell
Sheffield Methods Institute
Senior Lecturer in Quantitative Social Sciences
+44 114 222 6065
Full contact details
Sheffield Methods Institute
Interdisciplinary Centre of the Social Sciences (ICOSS)
Before moving to Sheffield, Andy was a lecturer at the University of Bristol, where he also completed his undergraduate degree (in Geography) and PhD (in Advanced Quantitative Methods). His current substantive research focuses on mental health from a life course perspective, but also spans a diverse range of other subject areas, including geography, political science, social epidemiology and economics. Methodologically, Andy’s interests are in the development and application of multilevel models, with work focusing on age-period-cohort analysis and fixed and random effects models.
- Research interests
Multilevel modelling, longitudinal modelling, mental health and wellbeing, life course research, political science, social epidemiology
- Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem. Abingdon: Routledge.
- Can intersectionality help with understanding and tackling health inequalities? Perspectives of professional stakeholders. Health Research Policy and Systems, 19(1).
- Mapping intersectional inequalities in biomarkers of healthy ageing and chronic disease in older English adults. Scientific Reports, 10. View this article in WRRO
- Age period cohort analysis: a review of what we should and shouldn’t do. Annals of Human Biology, 47(2), 208-217. View this article in WRRO
- A survey of new PIs in the UK. eLife, 8, e46827. View this article in WRRO
- Using shrinkage in multilevel models to understand intersectionality: a simulation study and a guide for best practice. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 15(2), 88-96. View this article in WRRO
- Fixed and random effects models: making an informed choice. Quality & Quantity, 53(2), 1051-1074. View this article in WRRO
- Cross‐Classified Multilevel Modelling of the Effectiveness of Similarity‐Based Virtual Screening. ChemMedChem, 13(6), 582-587. View this article in WRRO
- Understanding and misunderstanding group mean centering: a commentary on Kelley et al.’s dangerous practice. Quality and Quantity, 52(5), 2031-2036. View this article in WRRO
- The hierarchical age–period–cohort model: Why does it find the results that it finds?. Quality and Quantity, 52(2), 783-799. View this article in WRRO
- Urban geography and protest mobilization in Africa. Political Geography, 53, 54-64. View this article in WRRO
- Formula for success: Multilevel modelling of Formula One Driver and Constructor performance, 1950-2014. Journal of Quantitative Analysis in Sports, 12(2), 99-112. View this article in WRRO
- Should age-period-cohort analysts accept innovation without scrutiny? A response to Reither, Masters, Yang, Powers, Zheng and Land. Social Science & Medicine, 128, 331-333. View this article in WRRO
- Stylised fact or situated messiness? The diverse effects of increasing debt on national economic growth. Journal of Economic Geography, 15(2), 449-472. View this article in WRRO
- Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model. Quality & Quantity, 49(1), 255-266. View this article in WRRO
- Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data. Political Science Research & Methods, 3(1), 133-153. View this article in WRRO
- Life-course and cohort trajectories of mental health in the UK, 1991–2008 – A multilevel age–period–cohort analysis. Social science & medicine, 120, 21-30. View this article in WRRO
- Current practice in the modelling of age, period and cohort effects with panel data: a commentary on Tawfik et al. (2012), Clarke et al. (2009), and McCulloch (2012). Quality & Quantity, 48(4), 2089-2095. View this article in WRRO
- Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model. Demographic Research, 30, 333-360. View this article in WRRO
- Don't birth cohorts matter? A commentary and simulation exercise on Reither, Hauser, and Yang's (2009) age–period–cohort study of obesity. Social Science & Medicine, 101, 176-180. View this article in WRRO
- The impossibility of separating age, period and cohort effects. Social Science & Medicine, 93, 163-165. View this article in WRRO
- Can intersectionality help with understanding and tackling health inequalities? Perspectives of professional stakeholders.
- View this article in WRRO Multilevel models for age–period–cohort analysis In Bell A (Ed.), Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem Abingdon: Routledge.
- Introducing age, period and cohort effects In Bell A (Ed.), Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem Abingdon: Routledge.
- Cross-Sectional and Longitudinal Studies. In Morin J-F, Olsson C & Atikcan EÖ (Ed.), Research Methods in the Social Sciences: A A-Z of Key Concepts Oxford University Press.
- Age, Period and Cohort Processes in Longitudinal and Life Course Analysis: A Multilevel Perspective In Burton-Jeangros C, Cullati, S, Sacker A & Blane D (Ed.), A Life Course Perspective on Health Trajectories and Transitions (pp. 197-213). Springer View this article in WRRO
- https://www.socialsciencespace.com/2020/01/making-sense-of-data-in-the-2019-general-election/ Making Sense Of Data In The 2019 General Election. Social Science Space. Retrieved from
- https://www.nature.com/articles/d41586-019-00933-0 Female scientists get less money and staff for their first labs. Nature News. Retrieved from
- Using Longitudinal Multilevel Models to Investigate the Relationship Between Urbanization and Protest Mobilization in Africa. Sage Research Methods Case Studies. Retrieved from http://methods.sagepub.com/case/longitudinal-multilevel-models-urbanization-and-protest-mobilization-africa
- http://www.bbc.co.uk/news/education-41902914 Fake news: Universities offer tips on how to spot it. BBC News. Retrieved from
- https://www.youtube.com/watch?v=t0GuikebSNw The Age Period Cohort Identification Problem. YouTube video. Retrieved from
- https://www.youtube.com/watch?v=rzzSEMNmxmI&list=PLfcfWl4oIvSRzF_bE8Snz2jqdYZX1CRKN&index=5 Who is the Greatest Formula 1 Driver of All Time? - Why Numbers Matter, Episode 5. Retrieved from
- https://www.youtube.com/watch?v=S3aLo_rYBgQ&list=PLfcfWl4oIvSRzF_bE8Snz2jqdYZX1CRKN&index=4 Chocolate Helps You Lose Weight - Why Numbers Matter, Episode 4. Retrieved from
- https://www.youtube.com/watch?v=hQLCWHww9OQ&list=PLfcfWl4oIvSRzF_bE8Snz2jqdYZX1CRKN&index=3 Are You Above Average? - Why Numbers Matter, Episode 3. Retrieved from
- https://about.futurelearn.com/blog/blue-monday-and-the-problem-of-junk-science/ Blue Monday and the problem of junk science. Futurelearn blog. Retrieved from
- https://www.youtube.com/watch?v=j0Cb5g-lx9g The impossibility of separating age, period and cohort effects. Conference presentation at NCRM Research Methods Festival, 2014. Retrieved from
- http://eprints.ncrm.ac.uk/3699/4/MethodsNewsAutumn2014.pdf The varying relationship between economic growth and national debt. NCRM MethodsNews. Retrieved from
- http://blogs.lse.ac.uk/politicsandpolicy/debt-and-economic-growth-but-no-geography-a-cautionary-tale/ Significant variation across countries means that simple conclusions regarding growth and debt, like those offered by Reinhart & Rogoff, have no policy relevance. LSE Politics & Policy blog. Retrieved from
- http://www.bristol.ac.uk/media-library/sites/cmm/migrated/documents/12-mlwin-example.pdf Module 12: Cross-Classified Multilevel Models - MLwiN practical. Retrieved from
- Intersectionality and health explained.