Christopher M J Charlton

Ethnic Residential Segregation: A Multilevel, Multigroup, Multiscale Approach Exemplified by London in 2011.

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.

Modeling heterogeneous variance–Covariance components in two-level models

Applications of multilevel models to continuous outcomes nearly always assume constant residual variance and constant random effects variances and covariances. However, modeling heterogeneity of variance can prove a useful indicator of model misspecification, and in some educational and behavioral studies, it may even be of direct substantive interest. The purpose of this article is to review, describe, and illustrate a set of recent extensions to two-level models that allow the residual and random effects variance–covariance components to be specified as functions of predictors. These predictors can then be entered with random coefficients to allow the Level-1 heteroscedastic relationships to vary across Level-2 units. We demonstrate by simulation that ignoring Level-2 variability in residual variances leads the Level-1 variance function regression coefficients to be estimated with spurious precision. We discuss software options for fitting these extensions, and we illustrate them by reanalyzing the classic High School and Beyond data and two-level school effects models presented by Raudenbush and Bryk.

DEEP: a provenance-aware executable document system

The concept of executable documents is attracting growing interest from both academics and publishers since it is a promising technology for the the dissemination of scientific results. Provenance is a kind of metadata that provides a rich description of the derivation history of data products starting from their original sources. It has been used in many different e-Science domains and has shown great potential in enabling reproducibility of scientific results. However, while both executable documents and provenance are aimed at enhancing the dissemination of scientific results, little has been done to explore the integration of both techniques. In this paper, we introduce the design and development of Deep, an executable document environment that generates scientific results dynamically and interactively, and also records the provenance for these results in the document. In this system, provenance is exposed to users via an interface that provides them with an alternative way of navigating the executable document. In addition, we make use of the provenance to offer a document rollback facility to users and help to manage the systems dynamic resources.

Ethnic and class residential segregation: exploring their intersection – a multilevel analysis of ancestry and occupational class in Sydney:

Most studies of ethnic residential segregation recognise that occupational class is an important influence on the intensity of segregation of members of different ethnic groups, but are unable to explore variations in that intensity because of the lack of relevant data. Australian census data allow the class structure of different ancestry groups to be identified in small areas within cities. Such data for 17 ancestry groups in Sydney are used here to explore variations in segregation levels between classes within ancestry groups at three separate scales. To do this, a major extension to a recently-developed methodology for exploring multi-scale segregation patterns is introduced. The results show that for some groups class is more important than ancestry as an influence on segregation levels, whereas for others there is relatively little class segregation.

Multilevel growth curve models that incorporate a random coefficient model for the level 1 variance function

Aim To present a flexible model for repeated measures longitudinal growth data within individuals that allows trends over time to incorporate individual-specific random effects. These may reflect the timing of growth events and characterise within-individual variability which can be modelled as a function of age. Subjects and methods A Bayesian model is developed that includes random effects for the mean growth function, an individual age-alignment random effect and random effects for the within-individual variance function. This model is applied to data on boys’ heights from the Edinburgh longitudinal growth study and to repeated weight measurements of a sample of pregnant women in the Avon Longitudinal Study of Parents and Children cohort. Results The mean age at which the growth curves for individual boys are aligned is 11.4 years, corresponding to the mean ‘take off’ age for pubertal growth. The within-individual variance (standard deviation) is found to decrease from 0.24 cm2 (0.50 cm) at 9 years for the ‘average’ boy to 0.07 cm2 (0.25 cm) at 16 years. Change in weight during pregnancy can be characterised by regression splines with random effects that include a large woman-specific random effect for the within-individual variation, which is also correlated with overall weight and weight gain. Conclusions The proposed model provides a useful extension to existing approaches, allowing considerable flexibility in describing within- and between-individual differences in growth patterns.

A Bayesian model for measurement and misclassification errors alongside missing data, with an application to higher education participation in Australia

ABSTRACT In this paper we consider the impact of both missing data and measurement errors on a longitudinal analysis of participation in higher education in Australia. We develop a general method for handling both discrete and continuous measurement errors that also allows for the incorporation of missing values and random effects in both binary and continuous response multilevel models. Measurement errors are allowed to be mutually dependent and their distribution may depend on further covariates. We show that our methodology works via two simple simulation studies. We then consider the impact of our measurement error assumptions on the analysis of the real data set.

Intermediate Notation for Provenance and Workflow Reproducibility

We present a technique to capture retrospective provenance across a number of tools in a statistical software suite. Our goal is to facilitate portability of processes between the tools to enhance usability and to support reproducibility. We describe an intermediate notation to aid runtime capture of provenance and demonstrate conversion to an executable and editable workflow. The notation is amenable to conversion to PROV via a template expansion mechanism. We discuss the impact on our system of recording this intermediate notation in terms of runtime performance and also the benefits it brings.

A Longitudinal Mixed Logit Model for Estimation of Push and Pull Effects in Residential Location Choice

Abstract We develop a random effects discrete choice model for the analysis of households’ choice of neighborhood over time. The model is parameterized in a way that exploits longitudinal data to separate the influence of neighborhood characteristics on the decision to move out of the current area (“push” effects) and on the choice of one destination over another (“pull” effects). Random effects are included to allow for unobserved heterogeneity between households in their propensity to move, and in the importance placed on area characteristics. The model also includes area-level random effects. The combination of a large choice set, large sample size, and repeated observations mean that existing estimation approaches are often infeasible. We, therefore, propose an efficient MCMC algorithm for the analysis of large-scale datasets. The model is applied in an analysis of residential choice in England using data from the British Household Panel Survey linked to neighborhood-level census data. We consider how effects of area deprivation and distance from the current area depend on household characteristics and life course transitions in the previous year. We find substantial differences between households in the effects of deprivation on out-mobility and selection of destination, with evidence of severely constrained choices among less-advantaged households. Supplementary materials for this article are available online.