Kelvyn Jones | University of Bristol (original) (raw)

Papers by Kelvyn Jones

This paper: (a) finds rankings of who are the best formula 1 (F1) drivers of all time, conditiona... more This paper: (a) finds rankings of who are the best formula 1 (F1) drivers of all time, conditional on team performance; (b) quantifies how much teams and drivers matter; and (c) quantifies how team and driver effects vary over time and under different racing conditions. The finishing position of drivers is used as the response variable in a cross-classified multilevel model that partitions variance into team, team-year and driver levels. These effects are then allowed to vary by year, track type and weather conditions. Juan Manuel Fangio is found to be the greatest driver of all time. Team effects are shown to be more important than driver effects (and increasingly so over time), although their importance may be reduced in wet weather and on street tracks.

Quality and Quantity, 2018

It is claimed the hierarchical-age–period–cohort (HAPC) model solves the age–period–cohort (APC) ... more It is claimed the hierarchical-age–period–cohort (HAPC) model solves the age–period–cohort (APC) identification problem. However, this is debateable; simulations show situations where the model produces incorrect results, countered by proponents of the model arguing those simulations are not relevant to real-life scenarios. This paper moves beyond questioning whether the HAPC model works, to why it produces the results it does. We argue HAPC estimates are the result not of the distinctive substantive APC processes occurring in the dataset, but are primarily an artefact of the data structure—that is, the way the data has been collected. Were the data collected differently, the results produced would be different. This is illustrated both with simulations and real data, the latter by taking a variety of samples from the National Health Interview Survey (NHIS) data used by Reither et al. (Soc Sci Med 69(10):1439–1448, 2009) in their HAPC study of obesity. When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al. in Soc Sci Med 145:125–128, 2015a). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends.

The British Journal of …, Jan 1, 2005

Background Geographical variation in the prevalence of common mental disorders has not been expla... more Background Geographical variation in the prevalence of common mental disorders has not been explained adequately.

Aims To investigate whether regional mental health differences in Wales would persist after having taken into account the characteristics of individuals and regional social deprivation.

Method Data from the 1998 Welsh Health Survey were used. Common mental disorders were assessed with the mental health index included in the Short-Form 36 health survey (SF–36).The data were analysed using a multi-level linear regression model.

Results Of the total variance in the mental health index, 1.47% occurred at regional level (95% CI 0.56–2.38). Adjustment for individual characteristics did not explain the between-region variation. A higher area deprivation score was associated with a higher score on the mental health index.

Social Science Medicine, 2010

According to a critical rationalist view of science, a hypothesis can never be proven true, but i... more According to a critical rationalist view of science, a hypothesis can never be proven true, but it can be falsified. Experiments/tests that produce results consistent with the hypothesis can only enhance the circumstantial evidence supporting the argument; a single critical test is sufficient to refute it. Of course, one such test will rarely -as Barford, Dorling, and Pickett express it in this issue -'put a final nail in the [hypothesis's] coffin', but it necessarily raises queries that have to be addressed; why the negative findings, especially if they follow a lot of studies that have sustained the hypothesis?

Health and Place, 2010

This study analyses the relationships between self-rated health and both individual and mean nati... more This study analyses the relationships between self-rated health and both individual and mean national social trust, focusing on a variant of Wilkinson's hypothesis that individuals will be less healthy the greater the lack of social cohesion in a country. It employs multilevel modelling on World Values Survey data across 69 countries with a total sample of 160,436 individuals. The results show that self-rated health are positively linked to social trust at both country and individual levels after controlling for individual socio-demographic and income variables plus individual social trust; increased trust is associated with better health. Moreover, this analysis of social trust gives some insight into distinctive results for the former Soviet Bloc countries, which have high reported levels of poor health, alongside the Scandinavian countries which have high levels of trust and better health situations. Our results support and extend the Wilkinson hypothesis that the level of trust, an indicator of social cohesion, is predictive of individuals' health.

Progress in Human Geography, 2015

Since the 1990s, multilevel models have become popular tools for looking at contextual effects up... more Since the 1990s, multilevel models have become popular tools for looking at contextual effects upon health. However, the way that geography is incorporated into these models has received criticism due to somewhat arbitrary definitions of what counts as context, the models' discrete and, arguably, aspatial view of geographical effects, and the lack of any clear theoretical specification of the processes involved. This review draws together and extends these criticisms, arguing that while currently there are problems with how geography is conceived within multilevel models, there are ways of addressing them, and indeed that it is important to do so.

Health Education Research, 2000

We develop and apply a multilevel modeling approach that is simultaneously capable of assessing m... more We develop and apply a multilevel modeling approach that is simultaneously capable of assessing multigroup and multiscale segregation in the presence of substantial stochastic variation that accompanies ethnicity rates based on small absolute counts. Bayesian MCMC estimation of a log-normal Poisson model allows the calculation of the variance estimates of the degree of segregation in a single overall model, and credible intervals are obtained to provide a measure of uncertainty around those estimates. The procedure partitions the variance at different levels and implicitly models the dependency (or autocorrelation) at each spatial scale below the topmost one. Substantively, we apply the model to 2011 census data for London, one of the world's most ethnically diverse cities. We find that the degree of segregation depends both on scale and group.

Geographical Analysis, 2001

Within the last fm years, geographers and researchers in other cognate disciplines with geographi... more Within the last fm years, geographers and researchers in other cognate disciplines with geographic concern have begun to use multilevel models. While there are several useful existing introducto y accounts of these models in the geographical literature, this paper seeks to extend them in three main ways to clarify and emphusim further the substantial opportunities they aflord. First, it focuses on how multilevel models are centrally concerned with modeling population heterogeneity as a&nction of predictor variables. Second, it considers and illustrates a number of specijic interpretive issues that can arise when conducting multilevel analyses of place effects.

Environment and Planning a Abstract, 2013

Spatial Analysis Modelling in a Gis Environment, 1996

Social Science Medicine, 1998

This paper: (a) finds rankings of who are the best formula 1 (F1) drivers of all time, conditiona... more This paper: (a) finds rankings of who are the best formula 1 (F1) drivers of all time, conditional on team performance; (b) quantifies how much teams and drivers matter; and (c) quantifies how team and driver effects vary over time and under different racing conditions. The finishing position of drivers is used as the response variable in a cross-classified multilevel model that partitions variance into team, team-year and driver levels. These effects are then allowed to vary by year, track type and weather conditions. Juan Manuel Fangio is found to be the greatest driver of all time. Team effects are shown to be more important than driver effects (and increasingly so over time), although their importance may be reduced in wet weather and on street tracks.

Quality and Quantity, 2018

It is claimed the hierarchical-age–period–cohort (HAPC) model solves the age–period–cohort (APC) ... more It is claimed the hierarchical-age–period–cohort (HAPC) model solves the age–period–cohort (APC) identification problem. However, this is debateable; simulations show situations where the model produces incorrect results, countered by proponents of the model arguing those simulations are not relevant to real-life scenarios. This paper moves beyond questioning whether the HAPC model works, to why it produces the results it does. We argue HAPC estimates are the result not of the distinctive substantive APC processes occurring in the dataset, but are primarily an artefact of the data structure—that is, the way the data has been collected. Were the data collected differently, the results produced would be different. This is illustrated both with simulations and real data, the latter by taking a variety of samples from the National Health Interview Survey (NHIS) data used by Reither et al. (Soc Sci Med 69(10):1439–1448, 2009) in their HAPC study of obesity. When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al. in Soc Sci Med 145:125–128, 2015a). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends.

The British Journal of …, Jan 1, 2005

Background Geographical variation in the prevalence of common mental disorders has not been expla... more Background Geographical variation in the prevalence of common mental disorders has not been explained adequately.

Aims To investigate whether regional mental health differences in Wales would persist after having taken into account the characteristics of individuals and regional social deprivation.

Method Data from the 1998 Welsh Health Survey were used. Common mental disorders were assessed with the mental health index included in the Short-Form 36 health survey (SF–36).The data were analysed using a multi-level linear regression model.

Results Of the total variance in the mental health index, 1.47% occurred at regional level (95% CI 0.56–2.38). Adjustment for individual characteristics did not explain the between-region variation. A higher area deprivation score was associated with a higher score on the mental health index.

Social Science Medicine, 2010

According to a critical rationalist view of science, a hypothesis can never be proven true, but i... more According to a critical rationalist view of science, a hypothesis can never be proven true, but it can be falsified. Experiments/tests that produce results consistent with the hypothesis can only enhance the circumstantial evidence supporting the argument; a single critical test is sufficient to refute it. Of course, one such test will rarely -as Barford, Dorling, and Pickett express it in this issue -'put a final nail in the [hypothesis's] coffin', but it necessarily raises queries that have to be addressed; why the negative findings, especially if they follow a lot of studies that have sustained the hypothesis?

Health and Place, 2010

This study analyses the relationships between self-rated health and both individual and mean nati... more This study analyses the relationships between self-rated health and both individual and mean national social trust, focusing on a variant of Wilkinson's hypothesis that individuals will be less healthy the greater the lack of social cohesion in a country. It employs multilevel modelling on World Values Survey data across 69 countries with a total sample of 160,436 individuals. The results show that self-rated health are positively linked to social trust at both country and individual levels after controlling for individual socio-demographic and income variables plus individual social trust; increased trust is associated with better health. Moreover, this analysis of social trust gives some insight into distinctive results for the former Soviet Bloc countries, which have high reported levels of poor health, alongside the Scandinavian countries which have high levels of trust and better health situations. Our results support and extend the Wilkinson hypothesis that the level of trust, an indicator of social cohesion, is predictive of individuals' health.

Progress in Human Geography, 2015

Since the 1990s, multilevel models have become popular tools for looking at contextual effects up... more Since the 1990s, multilevel models have become popular tools for looking at contextual effects upon health. However, the way that geography is incorporated into these models has received criticism due to somewhat arbitrary definitions of what counts as context, the models' discrete and, arguably, aspatial view of geographical effects, and the lack of any clear theoretical specification of the processes involved. This review draws together and extends these criticisms, arguing that while currently there are problems with how geography is conceived within multilevel models, there are ways of addressing them, and indeed that it is important to do so.

Health Education Research, 2000

We develop and apply a multilevel modeling approach that is simultaneously capable of assessing m... more We develop and apply a multilevel modeling approach that is simultaneously capable of assessing multigroup and multiscale segregation in the presence of substantial stochastic variation that accompanies ethnicity rates based on small absolute counts. Bayesian MCMC estimation of a log-normal Poisson model allows the calculation of the variance estimates of the degree of segregation in a single overall model, and credible intervals are obtained to provide a measure of uncertainty around those estimates. The procedure partitions the variance at different levels and implicitly models the dependency (or autocorrelation) at each spatial scale below the topmost one. Substantively, we apply the model to 2011 census data for London, one of the world's most ethnically diverse cities. We find that the degree of segregation depends both on scale and group.

Geographical Analysis, 2001

Within the last fm years, geographers and researchers in other cognate disciplines with geographi... more Within the last fm years, geographers and researchers in other cognate disciplines with geographic concern have begun to use multilevel models. While there are several useful existing introducto y accounts of these models in the geographical literature, this paper seeks to extend them in three main ways to clarify and emphusim further the substantial opportunities they aflord. First, it focuses on how multilevel models are centrally concerned with modeling population heterogeneity as a&nction of predictor variables. Second, it considers and illustrates a number of specijic interpretive issues that can arise when conducting multilevel analyses of place effects.

Environment and Planning a Abstract, 2013

Spatial Analysis Modelling in a Gis Environment, 1996

Social Science Medicine, 1998

Quality & Quantity, 2018

This paper considers the modelling choices available to researchers using multilevel data, includ... more This paper considers the modelling choices available to researchers using multilevel data, including longitudinal data of various types. Specifically, we consider fixed effects (FE) and random effects (RE) models, including the within-between RE model, often misleadingly termed the ‘hybrid’ model. We argue that the latter is unambiguously a RE model, and that it is the most general of the three models, and as such a sensible starting point, given its flexibility to incorporate the positive aspects of both FE and RE models, and its ability to allow extensions (such as random slopes) that are often important. We present simulations that reveal the extent to which these models cope with mis-specification, finding that failing to include random slopes (e.g. in a FE or standard RE model) can lead to anti-conservative standard errors, and that mis-specifying non-Normal random effects as Normally distributed can introduce some small biases to variance and random effect estimates, but not fixed-part parameter estimates. We conclude with advice for applied researchers, and present a glossary, that gives clear definitions to terms that are confusing or have more than one meaning. Overall, we hope the paper gives practical advice to researchers in many different social science disciplines and beyond, looking to understand and use multilevel and longitudinal data.