The Corpus Status of Literature in Teaching Sociology: Novels as “Sociological Reconstruction”

Using fiction in teaching sociology involves what Harvey Sacks calls “sociological reconstruction”. Numerous comments on teaching sociology provide advice and suggestions on the use of literature and “what counts” as “sociological” literature, including specific titles. This paper goes further: while the use of literature is a routine feature of sociological accounts, discerning the relevance of a novel, or a passage within a novel, to sociological themes is an analyst’s achievement. It requires work both by the teacher and the student to recognize the relevance of fiction to sociology. Previous studies on fiction in sociology focus on the pedagogic aspects of using novels but fail to acknowledge the key problem of “sociological reconstruction” attempted through the use of novels. The paper explicates the crucial and generic issue of “corpus status”, which is fore-grounded by the use of non-sociological materials in sociology.     

Time for a change? Predictors of child care changes by low-income families

Instability in child care arrangements can negatively affect children's development, especially in low-income families. However, few studies have examined what predicts changes over time in child care arrangements. This paper presents findings from a unique multiyear study tracking child care use in low-income families. We estimate rich quantitative models to analyze the relationships among child, household, and care provider characteristics and four different types of changes. We find turnover in child care arrangements to be common in this low-income population. Over a period of six months, half of the children changed primary provider. Child care changes were frequently related to job loss, changes in family composition, or the changing availability of caregivers. While concerns have been raised that short spells of child care subsidy receipt cause child care instability, we found that subsidy use was not associated with higher rates of change. In addition, we found that the lower a parent's assessment of the child's experience in a particular arrangement in the prior time period, the higher the likelihood of changing providers by the next survey wave. These results indicate that low-income parents recognize quality factors and change arrangements to improve the quality of care.     

On MCMC sampling in hierarchical longitudinal models

Markov chain Monte Carlo (MCMC) algorithms have revolutionized Bayesian practice. In their simplest form (i.e., when parameters are updated one at a time) they are, however, often slow to converge when applied to high-dimensional statistical models. A remedy for this problem is to block the parameters into groups, which are then updated simultaneously using either a Gibbs or Metropolis-Hastings step. In this paper we construct several (partially and fully blocked) MCMC algorithms for minimizing the autocorrelation in MCMC samples arising from important classes of longitudinal data models. We exploit an identity used by Chib (1995) in the context of Bayes factor computation to show how the parameters in a general linear mixed model may be updated in a single block, improving convergence and producing essentially independent draws from the posterior of the parameters of interest. We also investigate the value of blocking in non-Gaussian mixed models, as well as in a class of binary response data longitudinal models. We illustrate the approaches in detail with three real-data examples.     

Some Bibliographic Practices in Interdisciplinary Work: Accounting for Citations in Library and Information Sciences

Interdisciplinary studies involve the use of concepts, methods and theories developed in other disciplines. Using sociology and library and information science as researchable fields, this paper examines how interdisciplinary presentations appropriate discipline-specific concepts. Itemizing materials in a bibliography is a claim to familiarity with the content of those materials. Bibliographic anomalies are apparent in papers by Elfreda Chatman, published in library and information science journals. Misspellings and disjunctive uses of material suggests that Chatman is unfamiliar with the literature bases she invokes. Working through texts and accompanying bibliographies, this article shows how the entailments of research programs that Chatman claims to use can be reflected back onto her own work. The article provides suggestions on how to make interdisciplinary studies more accountable to academic communities.     

Order-free co-regionalized areal data models with application to multiple-disease mapping

With the ready availability of spatial databases and geographical information system software, statisticians are increasingly encountering multivariate modelling settings featuring associations of more than one type: spatial associations between data locations and associations between the variables within the locations. Although flexible modelling of multivariate point-referenced data has recently been addressed by using a linear model of co-regionalization, existing methods for multivariate areal data typically suffer from unnecessary restrictions on the covariance structure or undesirable dependence on the conditioning order of the variables. We propose a class of Bayesian hierarchical models for multivariate areal data that avoids these restrictions, permitting flexible and order-free modelling of correlations both between variables and across areal units. Our framework encompasses a rich class of multivariate conditionally autoregressive models that are computationally feasible via modern Markov chain Monte Carlo methods. We illustrate the strengths of our approach over existing models by using simulation studies and also offer a real data application involving annual lung, larynx and oesophageal cancer death-rates in Minnesota counties between 1990 and 2000.     

Bayesian measures of model complexity and fit

Summary. We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure p _D for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general p _D approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the 'hat' matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding p _D to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.     

Flexible Cure Rate Modeling Under Latent Activation Schemes

With rapid improvements in medical treatment and health care, many datasets dealing with time to relapse or death now reveal a substantial portion of patients who are cured (i.e., who never experience the event). Extended survival models called cure rate models account for the probability of a subject being cured and can be broadly classified into the classical mixture models of Berkson and Gage (BG type) or the stochastic tumor models pioneered by Yakovlev and extended to a hierarchical framework by Chen, Ibrahim, and Sinha (YCIS type). Recent developments in Bayesian hierarchical cure models have evoked significant interest regarding relationships and preferences between these two classes of models. Our present work proposes a unifying class of cure rate models that facilitates flexible hierarchical model-building while including both existing cure model classes as special cases. This unifying class enables robust modeling by accounting for uncertainty in underlying mechanisms leading to cure. Issues such as regressing on the cure fraction and propriety of the associated posterior distributions under different modeling assumptions are also discussed. Finally, we offer a simulation study and also illustrate with two datasets (on melanoma and breast cancer) that reveal our framework's ability to distinguish among underlying mechanisms that lead to relapse and cure.     

Inference for nonconjugate Bayesian Models using the Gibbs sampler

A Bayesian approach to modeling a rich class of nonconjugate problems is presented. An adaptive Monte Carlo integration technique known as the Gibbs sampler is proposed as a mechanism for implementing a conceptually and computationally simple solution in such a framework. The result is a general strategy for obtaining marginal posterior densities under changing specification of the model error densities and related prior densities. We illustrate the approach in a nonlinear regression setting, comparing the merits of three candidate error distributions.     

Multiple imputation for missing data in a longitudinal cohort study: a tutorial based on a detailed case study involving imputation of missing outcome data

Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple times and the resulting estimates of the parameter(s) of interest are combined across the completed datasets, is becoming increasingly popular for handling missing data. However, MI can result in biased inference if not carried out appropriately or if the underlying assumptions are not justifiable. Despite this, there remains a scarcity of guidelines for carrying out MI. In this paper we provide a tutorial on the main issues involved in employing MI, as well as highlighting some common pitfalls and misconceptions, and areas requiring further development. When contemplating using MI we must first consider whether it is likely to offer gains (reduced bias or increased precision) over alternative methods of analysis. Once it has been decided to use MI, there are a number of decisions that must be made during the imputation process; we discuss the extent to which these decisions can be guided by the current literature. Finally we highlight the importance of checking the fit of the imputation model. This process is illustrated using a case study in which we impute missing outcome data in a five-wave longitudinal study that compared extremely preterm individuals with term-born controls.