Kelvyn Jones

Academic controversies

• 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' of country income that is inequality. A 2025 meta analysis of 168 studies using multilevel data found no effect of economic inequality on well-being or mental health.
• He has argued against Growth in a Time of Debt thesis and 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. 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" and are further developed and exemplified with colleagues in "Mutual misunderstanding and avoidance, misrepresentations and disciplinary politics: spatial science and quantitative analysis in geographical curricula" and a subsequent extended reply to critics in "One step forward but two steps back to the proper appreciation of spatial science". 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".
• He 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 offers all that fixed effects can provide and more. They also challenge the Fixed Effects Vector Decomposition 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". 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.". 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 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, report that “The most elaborate and animated treatment of the connection can be found in the recent paper of Bell and Jones. 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 ”
• 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.
• He contends that even with population data, 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 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.; the full list of papers that Bell and Jones have written are available for download from Research Gate. A review of the debate is given by Barker, KM et al Cross-classified multilevel models 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 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

• 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.

• Geography of health
• 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 prices
• Multilevel modelling of sporting outcomes

• 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
• Modelling segregation: methodological developments; this includes work on the modifiable areal unit problem;
• Modelling interactions: analysis of large tables of counts using a Poisson random effects model
• Age period cohort analysis A recent review Cross-classified multilevel models in health research: A systematic review of published empirical studies and recommendations for best practices, the article says "Bell and Jones 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. "

  Access to publications and citations

• A Google Scholar profile gives up-to-date citation of his work; as of January 2025 his H-index score is 70.
• He makes available much of his academic output on ResearchGate, where he frequently answers questions on statistical modeling. He has also explained his reasons for doing so in answer to a question on the site. As of February 2022, he has over 1.4 million 'reads' on ResearchGate and this is accruing at a rate of around 1,000 per week.
• There are also a ResearcherID site, an ORCID site and a database of publications at the University of Bristol.