The impact of cultural similarity and level of acquaintance on personality

The aim of this study was to ascertain whether people’s personality appeared to change depending on how well they knew other people they were interacting with, and whether those persons were from the same culture or not. This is a challenging question for two reasons. Personality is relatively stable by its nature, and the relationship between social context and personality is not at all well understood. Until recently studies in this area have used a relatively static model of personality. Recent research in this area is moving toward a more dynamic model to explain how personality and social context may interact with each other. Ninety-two participants took part in the study. The protocol utilized a within-subject experimental design where participants were asked to rate the personality of someone they knew well, in a number of different social situations. The results indicated that people appeared to be more self-disclosing, displayed more power-seeking behaviour, and were more empathic to others who were culturally similar. People also trusted their friends and family more, and were more self-conscious with strangers. While culture similarity and level of acquaintance did affect personality at least to some degree, they did not appear to interact

Estimation in Dirichlet random effects models

We develop a new Gibbs sampler for a linear mixed model with a Dirichlet process random effect term, which is easily extended to a generalized linear mixed model with a probit link function. Our Gibbs sampler exploits the properties of the multinomial and Dirichlet distributions, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithms. We also investigate methods for the estimation of the precision parameter of the Dirichlet process, finding that maximum likelihood may not be desirable, but a posterior mode is a reasonable approach. Examples are given to show how these models perform on real data. Our results complement both the theoretical basis of the Dirichlet process nonparametric prior and the computational work that has been done to date.Comment: Published in at http://dx.doi.org/10.1214/09-AOS731 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

accuracy: Tools for Accurate and Reliable Statistical Computing

Most empirical social scientists are surprised that low-level numerical issues in software can have deleterious effects on the estimation process. Statistical analyses that appear to be perfectly successful can be invalidated by concealed numerical problems. We have developed a set of tools, contained in accuracy, a package for R and S-PLUS, to diagnose problems stemming from numerical and measurement error and to improve the accuracy of inferences. The tools included in accuracy include a framework for gauging the computational stability of model results, tools for comparing model results, optimization diagnostics, and tools for collecting entropy for true random numbers generation.

What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation

What should a researcher do when statistical analysis software terminates before completion with a message that the Hessian is not invertable? The standard textbook advice is to respecify the model, but this is another way of saying that the researcher should change the question being asked. Obviously, however, computer programs should not be in the business of deciding what questions are worthy of study. Although noninvertable Hessians are sometimes signals of poorly posed questions, nonsensical models, or inappropriate estimators, they also frequently occur when information about the quantities of interest exists in the data, through the likelihood function. We explain the problem in some detail and lay out two preliminary proposals for ways of dealing with noninvertable Hessians without changing the question asked.Governmen

Circular Data in Political Science and How to Handle It

There has been no attention to circular (purely cyclical) data in political science research. We show that such data exist and are mishandled by models that do not take into account the inherently recycling nature of some phenomenon. Clock and calendar effects are the obvious cases, but directional data are observed as well. We describe a standard maximum likelihood regression modeling framework based on the von Mises distribution, then develop a general Bayesian regression procedure for the first time, providing an easy-to-use Metropolis-Hastings sampler for this approach. Applications include a chronographic analysis of U.S. domestic terrorism and directional party preferences in a two-dimensional ideological space for German Bundestag elections. The results demonstrate the importance of circular models to handle periodic and directional data in political scienc

Why does voting get so complicated? : a review of theories for analyzing democratic participation.

The purpose of this article is to present a sample from the panoply of formal theories on voting and elections to Statistical Science readers who have had limited exposure to such work. These abstract ideas provide a framework for understanding the context of the empirical articles that follow in this volume. The primary focus of this theoretical literature is on the use of mathematical formalism to describe electoral systems and outcomes by modeling both voting rules and human behavior. As with empirical models, these constructs are never perfect descriptors of reality, but instead form the basis for understanding fundamental characteristics of the studied system. Our focus is on providing a general, but not overly simplified, review of these theories with practical examples. We end the article with a thought experiment that applies different vote aggregation schemes to the 2000 presidential election count in Florida, and we find that alternative methods provide different results

Atmospheric blocking and mean biases in climate models

Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking

Elicited Priors for Bayesian Model Specifications in Political Science Research

We explain how to use elicited priors in Bayesian political science research. These are a form of prior information produced by previous knowledge from structured interviews with subjective area experts who have little or no concern for the statistical aspects of the project. The purpose is to introduce qualitative and area-specific information into an empirical model in a systematic and organized manner in order to produce parsimonious yet realistic implications. Currently, there is no work in political science that articulates elicited priors in a Bayesian specification. We demonstrate the value of the approach by applying elicited priors to a problem in judicial comparative politics using data and elicitations we collected in Nicaragua

Strategies for Ground Based Testing of Manned Lunar Surface Systems

Integrated testing (such as Multi-Element Integrated Test (MEIT)) is critical to reducing risks and minimizing problems encountered during assembly, activation, and on-orbit operation of large, complex manned spacecraft. Provides the best implementation of "Test Like You Fly:. Planning for integrated testing needs to begin at the earliest stages of Program definition. Program leadership needs to fully understand and buy in to what integrated testing is and why it needs to be performed. As Program evolves and design and schedules mature, continually look for suitable opportunities to perform testing where enough components are together in one place at one time. The benefits to be gained are well worth the costs