Dr Andrew Bell
School of Education
Senior Lecturer in Quantitative Social Sciences
+44 114 222 6065
Full contact details
School of Education
The Wave
2 Whitham Road
Sheffield
S10 2AH
- Profile
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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
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Multilevel modelling, longitudinal modelling, mental health and wellbeing, life course research, political science, social epidemiology
- Publications
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Books
Edited books
- Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem. Abingdon: Routledge.
Journal articles
- Commentary on: “age period cohort analysis – a review of what we should and shouldn’t do”. Annals of Human Biology, 51(1).
- Extending intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK. Social Science and Medicine.
- Clarifications on the Intersectional MAIHDA Approach: A conceptual guide and response to Wilkes and Karimi (2024). Social Science and Medicine, 350.
- A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). SSM - Population Health, 101664-101664.
- Preferences for work and leisure: is labour supply a function of what workers prefer?. Momentum Quarterly, 11(4), 204-269.
- The role of socioeconomic deprivation in explaining neighborhood and clinic effects in the effectiveness of psychological interventions. Journal of Consulting and Clinical Psychology, 91(2), 82-94.
- Methods for disentangling period and cohort changes in mortality risk over the twentieth century: comparing graphical and modelling approaches. Quality and Quantity, 57(4), 3219-3239.
- Understanding the effect of universal credit on housing insecurity in England: a difference-in-differences approach. Housing Studies.
- Neighbourhood deprivation and intersectional inequalities in biomarkers of healthy ageing in England. Health and Place, 77.
- Revisiting the Effects of Organized Mammography Programs on Inequalities in Breast Screening Uptake: A Multilevel Analysis of Nationwide Data From 1997 to 2017. Frontiers in Public Health, 10.
- 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.
- Age period cohort analysis: a review of what we should and shouldn’t do. Annals of Human Biology, 47(2), 208-217.
- Research culture : a survey of new PIs in the UK. eLife, 2019(8).
- Using shrinkage in multilevel models to understand intersectionality: a simulation study and a guide for best practice. Methodology, 15(2), 88-96.
- Fixed and random effects models: making an informed choice. Quality & Quantity, 53(2), 1051-1074.
- Cross‐Classified Multilevel Modelling of the Effectiveness of Similarity‐Based Virtual Screening. ChemMedChem, 13(6), 582-587.
- Understanding and misunderstanding group mean centering: a commentary on Kelley et al.’s dangerous practice. Quality and Quantity, 52(5), 2031-2036.
- The hierarchical age–period–cohort model: Why does it find the results that it finds?. Quality and Quantity, 52(2), 783-799.
- Urban geography and protest mobilization in Africa. Political Geography, 53, 54-64.
- Formula for success: Multilevel modelling of Formula One Driver and Constructor performance, 1950-2014. Journal of Quantitative Analysis in Sports, 12(2), 99-112.
- 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.
- Bayesian Informative Priors with Yang and Land’s Hierarchical Age-Period-Cohort model. Quality and Quantity, 49(1), 255-266.
- Stylised fact or situated messiness? The diverse effects of increasing debt on national economic growth. Journal of Economic Geography, 15(2), 449-472.
- Explaining Fixed Effects: Random Effects modelling of Time-Series Cross-Sectional and Panel Data. Political Science Research and Methods, 3(1), 133-153.
- 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.
- 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 and Quantity, 48(4), 2089-2095.
- Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model. Demographic Research, 30, 333-360.
- 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.
- The impossibility of separating age, period and cohort effects. Social science & medicine, 93, 163-165.
Chapters
- Age-Period-Cohort Analysis of Attitudes towards Foreigners in Germany, 1980–2016, Blickpunkt Gesellschaft (pp. 141-178). Springer Fachmedien Wiesbaden
- Introducing age, period and cohort effects In Bell A (Ed.), Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem Abingdon: Routledge.
- Multilevel models for age–period–cohort analysis In Bell A (Ed.), Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem (pp. 23-40). 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 (pp. 72-75). 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 International Publishing
Website content
- https://digitalmedia.sheffield.ac.uk/id/1_y55wuyu9 Age Period Cohort models: the identification problem and what to do about it. Retrieved from
- https://www.youtube.com/watch?v=rwqnC1fy_zc Intersectionality and health explained. Youtube. Retrieved from
- 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://www.futurelearn.com/info/blog/blue-monday-and-the-problem-of-junk-science?category=learning 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. 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
Preprints
- A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), Center for Open Science.
- Extending intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) for longitudinal data, with application to mental health trajectories in the UK, Center for Open Science.
- PhD Supervision
- Christie Butcher: The characteristics and experiences of carers in the UK trends and variations 2001-2021 (with Prof Matt Bennett and Professor Sue Yeandle) ESRC-funded Data Analytics and Society CDT, in partnership with CarersUK
- Harriet Ann Patrick: The financial costs of unpaid care in a geographical context (with Prof Matt Bennett and Professor Sue Yeandle) ESRC-funded Data Analytics and Society CDT, in partnership with Office for National Statistics
- Rhiannon Williams: Tackling homelessness in the UK: a data analytics approach (with Prof Gwilym Price and Dr Beth Garratt). ESRC-funded Data Analytics and Society CDT, in partnership with Shelter