Six‐ and 9‐Month‐Old Infants Discriminate Between Goals Despite Similar Action Patterns

Behne, Carpenter, Call, and Tomasello (2005) showed that 9‐ to 18‐month‐olds, but not 6‐month‐olds, differentiated between people who were unwilling and unable to share toys. As the outcome of the two tasks is the same (i.e., the toy is not shared), the infants must respond to the different goals of the actor. However, visual habituation paradigms have shown an earlier onset of goal awareness. The present study reconciles this disparity by replicating the findings of Behne et al. with both 6‐ and 9‐month‐olds, using similar tasks and additional response measures.     

Likelihood Ratio Type Statistics for Repeated Measures Designs with Heterogeneous Covariance Matrices

Because of their simplicity, Wald statistics are typically used in complex experimental designs. Likelihood ratio statistics in factorial designs are more flexible than Wald statistics in the sense of adapting to non-saturated designs by fitting only as many parameters as the model calls for. This leads to a significant gain in power. Here we propose likelihood ratio type statistics for testing hypotheses in repeated measures designs with heterogeneous covariance matrices, and derive their asymptotic distribution in one general theorem that does not require normality or even continuity of the responses. Simulation studies demonstrate their advantages over the Wald statistics.     

Bayesian analysis of mortality data

Congdon argued that the use of parametric modelling of mortality data is necessary in many practical demographical problems. In this paper, we focus on a form of model introduced by Heligman and Pollard in 1980, and we adopt a Bayesian analysis, using Markov chain Monte Carlo simulation, to produce the posterior summaries required. This opens the way to richer, more flexible inference summaries and avoids the numerical problems that are encountered with classical methods. Particular methodologies to cope with incomplete life-tables and a derivation of joint lifetimes, median times to death and related quantities of interest are also presented.     

New ways of specifying data edits

Summary.   Data editing is the process by which data that are collected in some way (a statistical survey for example) are examined for errors and corrected with the help of software. Edits, the logical conditions that should be satisfied by the data, are specified by subject-matter experts with a procedure which could be tedious and could lead to mistakes with practical implications. To render the process of edit specification more efficient we provide a new step—the definition of the so-called abstract data model of a survey—which describes the structure of the phenomenon that is studied in a survey. The existence of this model enables experts to identify all combinations of variables which should be checked by edits and to avoid the definition of conflicting edits. Furthermore, we introduce an automatic data validation strategy—TREEVAL—that consists of fast tree growing to derive automatically the functional form of edits and of a statistical criterion to clean the incoming data. The TREEVAL strategy is cast within a total quality management framework. The application of the methodologies proposed is demonstrated with the help of a real life application.