Dr Andrew Bell

Books

• Bell A, Hartman T, Piekut A, Rae A & Taylor M (2020) Making Sense of Data in the Media. Sage.

Edited books

• Bell A (Ed.) (2021) Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem. Abingdon: Routledge.

Journal articles

• Bright S, Buckley C, Holman D, Leckie G, Bell A, Mulia N, Kilian C & Purshouse R (2024) Corrigendum to “An analysis of intersectional disparities in alcohol consumption in the US” [Soc. Sci. Med. Volume 363, December 2024, 117514]. Social Science & Medicine, 117577-117577.
• Bright S, Buckley C, Holman D, Leckie G, Bell A, Mulia N, Kilian C & Purshouse R (2024) An analysis of intersectional disparities in alcohol consumption in the US. Social Science and Medicine.
• Bell A (2024) Commentary on: “age period cohort analysis – a review of what we should and shouldn’t do”. Annals of Human Biology, 51(1).
• Bell A, Evans C, Holman D & Leckie G (2024) 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.
• Evans C, Borrell LN, Bell A, Holman D, Subramanian SV & Leckie G (2024) Clarifications on the Intersectional MAIHDA Approach: A conceptual guide and response to Wilkes and Karimi (2024). Social Science and Medicine, 350.
• Evans CR, Leckie G, Subramanian SV, Bell A & Merlo J (2024) A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). SSM - Population Health, 101664-101664.
• Kaczmarczyk P & Bell A (2023) Preferences for work and leisure: is labour supply a function of what workers prefer?. Momentum Quarterly, 11(4), 204-269.
• Firth N, Barkham M, Delgadillo J, Bell A & O'Cathain A (2023) 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.
• Jones PM, Minton J & Bell A (2023) 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.
• Williams R, Bell A, Garratt E & Pryce G (2022) Understanding the effect of universal credit on housing insecurity in England: a difference-in-differences approach. Housing Studies.
• Holman D, Bell A, Green M & Salway S (2022) Neighbourhood deprivation and intersectional inequalities in biomarkers of healthy ageing in England. Health and Place, 77.
• Jolidon V, De Prez V, Bracke P, Bell A, Burton-Jeangros C & Cullati S (2022) 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.
• Holman D, Salway S, Bell A, Beach B, Adebajo A, Ali N & Butt J (2021) Can intersectionality help with understanding and tackling health inequalities? Perspectives of professional stakeholders. Health Research Policy and Systems, 19(1).
• Holman D, Salway S & Bell A (2020) Mapping intersectional inequalities in biomarkers of healthy ageing and chronic disease in older English adults. Scientific Reports, 10.
• Bell A (2020) Age period cohort analysis: a review of what we should and shouldn’t do. Annals of Human Biology, 47(2), 208-217.
• Acton S, Bell A, Toseland C & Twelvetrees A (2019) Research culture : a survey of new PIs in the UK. eLife, 2019(8).
• Bell AJ, Holman D & Jones K (2019) Using shrinkage in multilevel models to understand intersectionality: a simulation study and a guide for best practice. Methodology, 15(2), 88-96.
• Bell A, Fairbrother M & Jones K (2019) Fixed and random effects models: making an informed choice. Quality & Quantity, 53(2), 1051-1074.
• Mazalan L, Bell AJ, Sbaffi L & Willett P (2018) Cross‐Classified Multilevel Modelling of the Effectiveness of Similarity‐Based Virtual Screening. ChemMedChem, 13(6), 582-587.
• Bell A, Jones K & Fairbrother M (2018) Understanding and misunderstanding group mean centering: a commentary on Kelley et al.’s dangerous practice. Quality and Quantity, 52(5), 2031-2036.
• Bell A & Jones K (2018) The hierarchical age–period–cohort model: Why does it find the results that it finds?. Quality and Quantity, 52(2), 783-799.
• Fox S & Bell A (2016) Urban geography and protest mobilization in Africa. Political Geography, 53, 54-64.
• Bell AJ, Smith J, Sabel CE & Jones K (2016) Formula for success: Multilevel modelling of Formula One Driver and Constructor performance, 1950-2014. Journal of Quantitative Analysis in Sports, 12(2), 99-112.
• Bell AJ & Jones K (2015) 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.
• Bell AJ & Jones K (2015) Bayesian Informative Priors with Yang and Land’s Hierarchical Age-Period-Cohort model. Quality and Quantity, 49(1), 255-266.
• Bell AJ, Johnston R & Jones K (2014) Stylised fact or situated messiness? The diverse effects of increasing debt on national economic growth. Journal of Economic Geography, 15(2), 449-472.
• Bell A & Jones K (2014) Explaining Fixed Effects: Random Effects modelling of Time-Series Cross-Sectional and Panel Data. Political Science Research and Methods, 3(1), 133-153.
• Bell AJ (2014) 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.
• Bell AJ & Jones K (2014) 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.
• Bell AJ & Jones K (2014) Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model. Demographic Research, 30, 333-360.
• Bell AJ & Jones K (2014) 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.
• Bell AJ & Jones K (2013) The impossibility of separating age, period and cohort effects. Social science & medicine, 93, 163-165.

Chapters

• Bell A, Diehl Y, Hochman O & Schmidt P (2024) Age-period-cohort analysis of attitudes towards foreigners in Germany, 1980–2016 In Hochman O, Stanciu A & Hadjar A (Ed.), 40 Jahre ALLBUS - Die deutsche Gesellschaft im Wandel (pp. 141-178). Springer VS Wiesbaden View this article in WRRO
• Bell A (2021) Introducing age, period and cohort effects In Bell A (Ed.), Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem Abingdon: Routledge.
• Bell A & Jones K (2020) 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.
• Bell AJ (2020) 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.
• Bell A & Jones K (2015) 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

• Bell A (2022) Age Period Cohort models: the identification problem and what to do about it. Retrieved from https://digitalmedia.sheffield.ac.uk/id/1_y55wuyu9
• Holman D, Salway S & Bell A (2020) Intersectionality and health explained. Youtube. Retrieved from https://www.youtube.com/watch?v=rwqnC1fy_zc
• Bell A (2020) Making Sense Of Data In The 2019 General Election. Social Science Space. Retrieved from https://www.socialsciencespace.com/2020/01/making-sense-of-data-in-the-2019-general-election/
• Else H (2019) Female scientists get less money and staff for their first labs. Nature News. Retrieved from https://www.nature.com/articles/d41586-019-00933-0
• Bell AJ & Fox S (2019) 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
• Burns J (2017) Fake news: Universities offer tips on how to spot it. BBC News. Retrieved from http://www.bbc.co.uk/news/education-41902914
• Bell AJ (2017) The Age Period Cohort Identification Problem. YouTube video. Retrieved from https://www.youtube.com/watch?v=t0GuikebSNw
• Bell AJ (2017) Who is the Greatest Formula 1 Driver of All Time? - Why Numbers Matter, Episode 5. Retrieved from https://www.youtube.com/watch?v=rzzSEMNmxmI&list=PLfcfWl4oIvSRzF_bE8Snz2jqdYZX1CRKN&index=5
• Bell AJ (2017) Chocolate Helps You Lose Weight - Why Numbers Matter, Episode 4. Retrieved from https://www.youtube.com/watch?v=S3aLo_rYBgQ&list=PLfcfWl4oIvSRzF_bE8Snz2jqdYZX1CRKN&index=4
• Bell AJ (2017) Are You Above Average? - Why Numbers Matter, Episode 3. Retrieved from https://www.youtube.com/watch?v=hQLCWHww9OQ&list=PLfcfWl4oIvSRzF_bE8Snz2jqdYZX1CRKN&index=3
• Bell AJ (2016) Blue Monday and the problem of junk science. Futurelearn blog. Retrieved from https://www.futurelearn.com/info/blog/blue-monday-and-the-problem-of-junk-science?category=learning
• Bell AJ (2015) The impossibility of separating age, period and cohort effects. Conference presentation at NCRM Research Methods Festival, 2014. Retrieved from https://www.youtube.com/watch?v=j0Cb5g-lx9g
• Bell AJ (2014) The varying relationship between economic growth and national debt. NCRM MethodsNews. Retrieved from http://eprints.ncrm.ac.uk/3699/4/MethodsNewsAutumn2014.pdf
• Bell AJ (2013) 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://blogs.lse.ac.uk/politicsandpolicy/debt-and-economic-growth-but-no-geography-a-cautionary-tale/
• Leckie G & Bell AJ (2013) Module 12: Cross-Classified Multilevel Models - MLwiN practical. Retrieved from http://www.bristol.ac.uk/media-library/sites/cmm/migrated/documents/12-mlwin-example.pdf
• Bell A & Leckie G Multilevel models to study intersectionality.

Preprints

• Leckie G, Bell A, Merlo J, Subramanian SV & Evans C (2025) The Statistical Advantages of MAIHDA for Estimating Intersectional Inequalities, Center for Open Science.
• Evans C, Leckie G, Subramanian SV, Bell A, Merlo J, Borrell LN & Webb CJR (2024) A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), Center for Open Science.
• Evans C, Leckie G, Subramanian SV, Bell A & Merlo J (2024) A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), Center for Open Science.
• Bell A, Evans C, Holman D & Leckie G (2023) 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.
• Jones PM, Minton J & Bell A (2017) Period and cohort changes in mortality risk over the twentieth century in the UK: an exploratory analysis, Center for Open Science.