Kelvyn Jones, (born ) is a British professor (Emeritus) of human quantitative geography at the University of Bristol.Kelvyn Jones website at the University of Bristol University of Bristol. Retrieved 9 February 2021. He focuses on the quantitative modelling of social science data with complex structure through the application of multilevel models; especially in relation to change and health outcomes. Uniquely he is an elected Fellow of the British Academy, the Academy of the Social Sciences and the Learned Society of Wales.
Academic controversies He has been involved in a number of academic controversies, and these debates have been of a methodological and substantive nature. They include:
He has disagreed with the Wilkinson inequality hypothesis that within country differences in health and mortality are driven by invidious comparison; instead arguing that there is a materialist argument based on poverty even in advanced economies. The argument is based on critique of Wilkinson's use of aggregate data and supports the ideas of Hugh Gravelle that if there is a non-linear individual relationship between income and ill-health then the aggregate relationship will necessarily involve the 'spread' (standard deviation) of country income that is inequality. A 2025 meta analysis of 168 studies using multilevel data (11,389,871 participants from 38,335 geographical units) found no effect of economic inequality on well-being or mental health.Sommet, N., Fillon, A.A., Rudmann, O. et al. No meta-analytical effect of economic inequality on well-being or mental health. Nature (2025). https://doi.org/10.1038/s41586-025-09797-z He has argued against Growth in a Time of Debt thesis and (with Andy Bell) re-analyzed the Reinhart and Rogoff data to show that the evidence for many counties is that the relationship is around the other way - the lack of growth produces debt, and that the relationship between debt and growth varies significantly between countries, meaning that an average "rule", such as that suggested by Reinhart and Rogoff, has little meaning or policy relevance. With colleagues, he has argued against Trevor Phillips that the UK is 'sleep walking to segregation', finding that ethnic residential segregation in London for example is decreasing. They dispute that Muslim ghettoes are developing in British cities, and that Australian suburbs are being 'swamped' by Asians and Muslims. He has argued that quantitative analysis in the form of quantitative geography has an important role in emancipatory human geography (see critical geography). He has argued that this involves adopting a realist philosophy of science distinguished as critical realism and not positivism. The arguments are made in "The Practice of Quantitative Methods"Jones, Kelvyn (2011)The Practice of Quantitative Methods in Somekh, Bridget and Lewin, Cathy (eds.) Theory and methods in social research Sage Publications Ltd, 201-211, download from https://www.researchgate.net/publication/256801634_The_practice_of_quantitative_methods and are further developed and exemplified with colleagues in "Mutual misunderstanding and avoidance, misrepresentations and disciplinary politics: spatial science and quantitative analysis in (United Kingdom) geographical curricula"Johnston R, Harris R, Jones K, et al (2014) Mutual mis-understanding and avoidance, mis-representations, and disciplinary politics: spatial science and quantitative analysis in (UK) geographical curricula. Dialogues in Human Geography 4(1): 3–25 https://www.researchgate.net/publication/261054447 and a subsequent extended reply to critics in "One step forward but two steps back to the proper appreciation of spatial science".Johnston, R, et al. (2014) "One step forward but two steps back to the proper appreciation of spatial science." Dialogues in Human Geography 4.1 (2014): 59-69. https://www.researchgate.net/publication/261062153 One commentator described this as "an extraordinary contribution. This is a panoramic survey of the legacy of half a century of innovation in spatial science—put into a critical, constructive engagement with half a century of innovation in critical social theory".Wyly, E. 2014. The new quantitative revolution. Dialogues in Human Geography 4(1): 26–38. http://journals.sagepub.com/doi/pdf/10.1177/2043820614525732 He (with colleagues) has challenged the 'gold standard' that fixed effects should be the standard approach to the analysis of Panel data and that a Hausman test is an appropriate way of choosing between a Fixed effects model and a Random effects model. Somewhat controversially they argue that a particular form of the random effects model (the within-between model or the similar Mundlak model) offers all that fixed effects can provide and more. They also challenge the Fixed Effects Vector Decomposition (FEVD) model of Plumper and Troeger. One reaction was: "This paper and the instructive controversial over FEVD have shown me that my econometrics training had not - as I once assumed - taught me all that there is to know about fixed effects estimation. In particular, the authors' treatment of 'heterogeneity bias' clarifies the importance of addressing both 'within' and 'between' variation in the data and they make a compelling case for considering both 'individual' and 'ecological' influences".Geoffrey Thomas Pugh's page on Research Gate https://www.researchgate.net/profile/Geoffrey_Pugh/answers Another was: "Bizarre and often incorrect paper by two political scientists on the virtues of random-effects over fixed-effects". to "You can and should use a well-specified random effects model. Always.".Panel data within group estimator on StackExchange demonstrates results with R (programming language) code, see These models shown algebraically in the table for a two-level panel model are discussed and illustrated with snippets of R code by Daniel Lüdecke, and there is a R package (panelr) for panel data analysis by Jacob Long that facilitates their implementation. An extensive review of the potential of this approach in economics concluded that it has been "unreasonably ignored" due in part to "disciplinary isolation" of the subject. In the psychological literature, Hamaker and Muthén, (2020) Hamaker, E. L., & Muthén, B. (2020). The fixed versus random effects debate and how it relates to centering in multilevel modelling. Psychological methods, 25(3), 365–379. https://doi.org/10.1037/met0000239 report that “The most elaborate and animated treatment of the connection [between FE versus RE models and centering in multilevel models] can be found in the recent paper of Bell and Jones (2015). They build a compelling case for multilevel modelling, arguing that, while the problem of endogeneity is very real, the point is that we should simply use the right multilevel model to tackle it (i.e., based on person mean centering the time-varying covariate and/or including these means as a predictor at the between-level)” He and colleagues argue that group-mean centering in multilevel models can be a useful procedure in random coefficient models, thereby disagreeing that it is a 'dangerous' procedure. Reactions to this critique include "may the Saints & Angels protect us from ever having a paper this thoroughly dismantled" and "Seriously though, if you are interested in multilevel modelling I highly recommend this short, instructive and frankly rather sassy paper." The essence of the argument is that in a two-level model, the slope parameter associated with level-1 variable is a potentially uninterpretable mixture of within and between effects. The solution is to decentre the level-1 variable by subtracting the level 2 cluster mean and including these level 2 means in the model. The argument is made in terms of continuous variables and is extended to multicategory predictors by Yaremych et al (2021).Yaremych, H. E., Preacher, K. J., & Hedeker, D. (2021) Centering categorical predictors in multilevel models: Best practices and interpretation. Psychological Methods. https://doi.org/10.1037/met0000434 He contends that even with population data (e.g. a full enumeration of all pupils in all schools in a country), a statistical inference approach is required to deal with stochastic or natural variation. Observed outcomes are seen as a result of a stochastic process which could produce different results under the same circumstances. It is this underlying process that is of interest and the actual observed values give only an imprecise estimate of this. Working with Andy Bell, he has argued that the multilevel model (in the form of the hierarchical-age–period–cohort (HAPC) model) is not an automatic solution to the identification problem of the age period cohort model. This third-party site considers some earlier papers in the exchange between Bell and Jones and Yang and Land, while this most recent paper gives in Table 1 the key papers (and arguments made).; the full list of papers that Bell and Jones have written are available for download from Research Gate.Age-period- cohort project site on Research Gate: https://www.researchgate.net/project/Age-period-cohort A review of the debate is given by Barker, KM et al (2020) Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices, SSM - Population Health, Volume 12. They conclude "Bell and Jones (2018) have done much to explicate the debate, the ‘identification problem,’ and the methodological concerns. Despite this, the vast majority of researchers continue to employ CCMM for APC analysis without reference to the identification problem, the controversy itself, or any of the latest recommendations for best practices. Those that do refer to the identification problem often note this only within the limitations section of the manuscript. In light of the ongoing debate surrounding these methods, however, we urge substantial caution when conducting APC analysis and recommend a more meaningful engagement with the logic underlying the controversy. "
Academic work and projects He researches in three main areas:These are listed on his University of Bristol website: http://www.bristol.ac.uk/geography/people/kelvyn-jones/research.html
Geography of health: particularly geographical inequalities in mortality in advanced economies; Research design: especially to develop evidence-based research in non-experimental, observational studies; Realistically complex modelling: this research work focuses on the quantitative analysis of social-science data with complex structure, particularly when there are many levels of analysis such as panels, spatial series, and space-time series.
His substantive and methodological work is wide-ranging and includes the following bodies of work:
Substantive research Geography of health Macro determinants of health;Project site on Research Gate: https://www.researchgate.net/project/Macro-determinants-of-health Multilevel modelling of health-related behaviors and outcomes Multilevel modelling of mental health outcomes Multilevel modelling of social capital, trust and volunteering Multilevel modelling of voting behaviors and electoral outcomes Forecasting geographical variations in the EU referendum Multilevel modelling of socio-demographic variation in China Modelling segregation: applying the new methodologies Multilevel modelling of property(house) prices Multilevel modelling of sporting outcomes
Methodological research Quantitative geography Statistical data analysis in the social sciences Multilevel modelling: scope, models and issues Multilevel analysis, software, manuals and data Fixed and Random effects analysis Modelling nationally predicting locally (multilevel regression with post stratification)