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Heritability studies attempt to estimate the contribution of genes (vs. environments) to variation in phenotypes (or outcomes of interest) in a given population at a given time. This chapter scrutinizes heritability studies of adverse health phenotypes, emphasizing flaws that have become more glaring in light of recent advances in the life sciences and manifest most visibly in epigenetics.

Drawing on a diverse body of research and critical scholarship, this chapter examines the veracity of methodological and conceptual assumptions of heritability studies.

The chapter argues that heritability studies are futile for two reasons: (1) heritability studies suffer from serious methodological flaws with the overall effect of making estimates inaccurate and likely biased toward inflated heritability, and, more importantly (2) the conceptual (biological) model on which heritability studies depend – that of identifiably separate effects of genes versus the environment on phenotype variance – is unsound. As discussed, contemporary bioscientific work indicates that genes and environments are enmeshed in a complex (bidirectional, interactional), dynamic relationship that defies any attempt to demarcate separate contributions to phenotype variance. Thus, heritability studies attempt the biologically impossible. The emerging research on the importance of microbiota is also discussed, including how the commensal relationship between microbial and human cells further stymies heritability studies.

Understandably, few sociologists have the time or interest to be informed about the methodological and theoretical underpinnings of heritability studies or to keep pace with the incredible advances in genetics and epigenetics over the last several years. The present chapter aims to provide interested scholars with information about heritability and heritability estimates of adverse health outcomes in light of recent advances in the biosciences.

This chapter aims at bringing a sport-centric approach to the study of digital marketing. This is important as most of the existing research on digital marketing takes an industry-neutral perspective that generalises theory to all contexts. The sport industry has been a major recipient of digital marketing so it is important to recognise its impact on the evolution of digital marketing practices. To do so requires an analysis of what digital marketing is and how it has changed as a result of technological innovations. This enables a better understanding about the growth trajectory of digital sport marketing that integrates a sport-specific theory to digital marketing practices.

Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys. Covariate data associated with the judges are also often recorded. Such covariate data should be used in conjunction with preference data when drawing inferences about judges.

To cluster a population of judges, the population is modeled as a collection of homogeneous groups. The Plackett-Luce model for ranked data is employed to model a judge's ranked preferences within a group. A mixture of Plackett- Luce models is employed to model the population of judges, where each component in the mixture represents a group of judges.

Mixture of experts models provide a framework in which covariates are included in mixture models. Covariates are included through the mixing proportions and the component density parameters. A mixture of experts model for ranked preference data is developed by combining a mixture of experts model and a mixture of Plackett-Luce models. Particular attention is given to the manner in which covariates enter the model. The mixing proportions and group specific parameters are potentially dependent on covariates. Model selection procedures are employed to choose optimal models.

Model parameters are estimated via the ‘EMM algorithm’, a hybrid of the expectation–maximization and the minorization–maximization algorithms. Examples are provided through a menu survey and through Irish election data. Results indicate mixture modeling using covariates is insightful when examining a population of judges who express preferences.

The authors use laboratory experiments to test two self-assessment tax mechanisms for facilitating land assembly. One mechanism is incentive compatible with a complex tax function, while the other uses a flat tax rate to mitigate implementation concerns. Sellers publicly declare a price for their land. Overstating its true value is penalized by using the declared price to assess a property tax; understating its value is penalized by allowing developers to buy the property at the declared price. The authors find that both mechanisms increase the rate of land assembly and gains from trade relative to a control in which sellers’ price declarations have no effect on their taxes. However, these effects are statistically insignificant or transitory. The assembly rates in our self-assessment treatments are markedly higher than those of prior experimental studies in which the buyer faces bargaining frictions, such as costly delay or capital constraints.