Essay: The Role of Participatory Listening in Collaborative Discourse: Insights from Studies of Engineering Design Teams

In this article, we summarize evidence from observations of engineering teams for the importance of active listening in collaborative design problem solving. We focus on the way speakers use freehand sketching to convey complex design ideas, and how listeners support them whilst they do this. We argue that listeners temporarily redistribute the shared cognitive load of discourse production and comprehension within design teams: whilst speakers are engaged in sketch construction they are unable to take account of listener understanding, and listeners respond intuitively by anticipating and avoiding difficulties in this process. We conclude that this cooperativeness emerges naturally from the operation of effort conserving principles underlying collaborative social interaction, and show that by focusing on the contribution of the listener to the production of creative engineering discourse, this principle can be thrown into sharp relief.     

Sexual fantasy experiences and guilt among conservative Christians: An exploratory study

This study examines the sexual fantasy experiences and cognitive‐affective evaluations in a sample of conservative Christians. The study also operationalizes and documents fantasy guilt as an entity distinct from the more broadly defined category of sex guilt. Respondents reported normative fantasy categories and frequencies of fantasy occurrences. Concomitantly, fantasy episodes were experienced along with substantial to high levels of guilt over fantasizing. Findings show a complex interrelationship between a subject's cognitions, emotions, and fantasy guilt. These point to the import this sample gives to learned cognitions in furthering guilt, over and above the content or frequency of the fantasy experience itself.     

The Persistence of Neighboring as a Determinant of Community Attachment: A Community Field Perspective*

We examined the community field perspective as a complement to the linear‐development and systemic models of community attachment, wherein community attachment is defined as a social bond to the community of place. We empirically evaluated indicators of the actor's interaction within the social field, such as the perceived quality of neighboring and density of friendships, using social survey and census data from 99 communities in 1994 and 2004 and evaluated as well the percentage change in these indicators from 1994 to 2004. Results show that the community field perspective can complement previous approaches by highlighting the importance of perceived neighboring and friendship density and the persistence of perceived neighboring over time. We suggest implications for community development efforts aimed at enhancing community attachment.     

Hierarchische Planung für industrielle Logistikdienstleister

In this paper a planning and control system for logistics service providers (called LPS system) is developed under consideration of their specific flexibility potentials. Due to the complexity of such planning decisions (different planning levels and data quality of these levels as well as time horizons etc.), a multi-stage, hierarchical planning system is proposed which minimizes the relevant costs under consideration of the flexibilities. The advantage of a hierarchical planning concept consists in a limited planning complexity because otherwise data collection and solution development would lead to unsolvable problems in practice. The results of using LPS systems for a real-life case show a significant pay-off for logistics service providers due to the remarkable cost savings by applying a hierarchical planning concept.     

A study of Bayesian local robustness with applications in actuarial statistics

Local or infinitesimal Bayesian robustness is a powerful tool to study the sensitivity of posterior magnitudes, which cannot be expressed in a simple manner. For these expressions, the global Bayesian robustness methodology does not seem adequate since the practitioner cannot avoid using inappropriate classes of prior distributions in order to make the model mathematically tractable. This situation occurs, for example, when we compute some types of premiums in actuarial statistics in order to fix the premium to be charged to an insurance policy. In this paper, analytical and simple expressions that allow us to study the sensitivity of premiums, which are usually used in automobile insurance are provided by using the local Bayesian robustness methodology. Some examples are examined by using real automobile claim insurance data.     

Inference on effect size indices from several two-armed experiments

The statistical inference problem on effect size indices is addressed using a series of independent two-armed experiments from k arbitrary populations. The effect size parameter simply quantifies the difference between two groups. It is a meaningful index to be used when data are measured on different scales. In the context of bivariate statistical models, we define estimators of the effect size indices and propose large sample testing procedures to test the homogeneity of these indices. The null and non-null distributions of the proposed testing procedures are derived and their performance is evaluated via Monte Carlo simulation. Further, three types of interval estimation of the proposed indices are considered for both combined and uncombined data. Lower and upper confidence limits for the actual effect size indices are obtained and compared via bootstrapping. It is found that the length of the intervals based on the combined effect size estimator are almost half the length of the intervals based on the uncombined effect size estimators. Finally, we illustrate the proposed procedures for hypothesis testing and interval estimation using a real data set.     

A perspective of composite sampling

Composite samples are formed by physically mixing samples. Usually, composite samples are used to reduce the overall cost associated with analytical procedures that must be performed on each sample, but they can also be used to protect the privacy of individuals.

Composite sampling can reduce the cost of identifying individual cases that have a certain trait, such as those with a rare disease or those exceeding pollution-level standards. Not much is lost by applying this method as long as the trait is relatively rare.

Composite sampling can reduce the cost of estimating the mean of some process. When samples are composited, the ability to estimate the variance is lost. In spite of this, the potential savings are so great that composite samples have been used.

Much of this paper deasl with the variance of estimators based on composite sampling when the porportions of hte original samples comprising the composite sample are actually random. Taking repeated samples and measurements on several composite samples complicates the prodcedure, but allows the estimation of between and within variation as well as measurement error.     

Pooled Cross-Sectional and Time Series Data: A Survey of Current Statistical Methodology

A data base that provides a multivariate statistical history for each of a number of individual entities is called a pooled cross-sectional and time series data base in the econometrics literature. In marketing and survey literature the terms panel data or longitudinal data are often used. In management science a convenient term might be management data base. Such a data base provides a particularly rich environment for statistical analysis. This article reviews methods for estimating multivariate relationships particular to each individual entity and for summarizing these relationships for a number of individuals. Inference to a larger population when the data base is viewed as a sample is also considered.     

Understanding Canonical Correlation through the General Linear Model and Principal Components

Canonical correlation has been little used and little understood, even by otherwise sophisticated analysts. An alternative approach to canonical correlation, based on a general linear multivariate model, is presented. Properties of principal component analysis are used to help explain the method. Standard computational methods for full rank canonical correlation, techniques for canonical correlation on component scores, and canonical correlation with less than full rank are discussed. They are seen to be essentially equivalent when the model equation for canonical correlation on component scores is presented. The two approaches to less than full rank situations are equivalent in some senses, but quite different in usefulness, depending on the application. An example dataset is analyzed in detail to help demonstrate the conclusions.