Internet use in the contemporary media environment

Although a significant amount of research has focused on traditional media choice and use, and even on some ‘new’ media, these studies have either neglected the Internet and World Wide Web or were conducted prior to their recent popularity. This study offers a novel exploration of individuals' use of three Internet functions (information retrieval, information giving, and conversation capabilities) in the context of the use of other communication media. Data from 684 individuals demonstrate that the Internet is a multidimensional technology used in a manner similar to other, more traditional media. Specifically, conversation features of the Internet align with mediated interpersonal technologies (the telephone and electronic mail), whereas the Internet's information‐retrieval and information‐giving features are used in ways similar to mass media channels (newspapers, television, and books and magazines). In addition, needs fulfilled by these channels cluster in ways consistent with past research, regardless of the technologies employed to meet them.     

In Vivo Hippocampal Acetylcholine Release During Exposure to Acute Stress

Hippocampal extracellular acetylcholine (ACh) and choline levels were evaluated using in vivo microdialysis in male Fischer 344 rats before, during, and following an 80-min exposure to two different stress conditions. Measurements were taken in rats restrained and immersed in a water bath containing either 37 degreesC (normothermic-restraint) or 20 degreesC (cold-restraint) water. Results were compared to normothermic-freely-moving rats. Cold-restrained rats displayed decreased ACh levels during cold exposure relative to both normothermic-restrained and normothermic-freely-moving rats. By the end of the cold exposure period and following removal from cold, ACh levels had returned to near-baseline values. Normothermic-restrained rats had levels similar to those of normothermic-freely-moving rats, except for a marked increase in ACh following removal from restraint. Cold-restrained rats displayed a gradual elevation in choline levels during cold stress, followed by a gradual decline after stress termination, whereas both normothermic-restrained and normothermic-freely-moving rats displayed gradual decreases during the microdialysis session. These findings demonstrate that central cholinergic neurotransmission can be altered by the application of, and removal from, acute stressors. In addition, the results suggest a possible relationship between the magnitudes of both the stressor and its cholinergic consequences.     

Detecting Clusters/Communities in Social Networks

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.     

Cautionary Remarks on the Use of Clusterwise Regression

Clusterwise linear regression is a multivariate statistical procedure that attempts to cluster objects with the objective of minimizing the sum of the error sums of squares for the within-cluster regression models. In this article, we show that the minimization of this criterion makes no effort to distinguish the error explained by the within-cluster regression models from the error explained by the clustering process. In some cases, most of the variation in the response variable is explained by clustering the objects, with little additional benefit provided by the within-cluster regression models. Accordingly, there is tremendous potential for overfitting with clusterwise regression, which is demonstrated with numerical examples and simulation experiments. To guard against the misuse of clusterwise regression, we recommend a benchmarking procedure that compares the results for the observed empirical data with those obtained across a set of random permutations of the response measures. We also demonstrate the potential for overfitting via an empirical application related to the prediction of reflective judgment using high school and college performance measures.     

An implicit enumeration method for an exact test of weighted kappa

The kappa coefficient is one of the most widely used measures for evaluating the agreement between two raters asked to assign N objects to one of K nominal categories. Weighted versions of kappa enable partial credit to be awarded for near agreement, most notably in the case of ordinal categories. An exact significance test for weighted kappa can be conducted by enumerating all rater agreement tables with the same fixed marginal frequencies as the observed table, and accumulating the probabilities for all tables that produce a weighted kappa index that is greater than or equal to the observed measure. Unfortunately, complete enumeration of all tables is computationally unwieldy for modest values of N and K. We present an implicit enumeration algorithm for conducting an exact test of weighted kappa, which can be applied to tables of non‐trivial size. The algorithm is particularly efficient for ‘good’ to ‘excellent’ values of weighted kappa that typically have very small p‐values. Therefore, our method is beneficial for situations where resampling tests are of limited value because the number of trials needed to estimate the p‐value tends to be large.     

Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the performance of nearly all of the variable selection procedures. Overall, a variable selection technique based on a variance-to-range weighting procedure coupled with the largest decreases in within-cluster sums of squares error performed the best. On the other hand, variable selection methods used in conjunction with finite mixture models performed the worst.     

Cross validation issues in multiobjective clustering

The implementation of multiobjective programming methods in combinatorial data analysis is an emergent area of study with a variety of pragmatic applications in the behavioural sciences. Most notably, multiobjective programming provides a tool for analysts to model trade offs among competing criteria in clustering, seriation, and unidimensional scaling tasks. Although multiobjective programming has considerable promise, the technique can produce numerically appealing results that lack empirical validity. With this issue in mind, the purpose of this paper is to briefly review viable areas of application for multiobjective programming and, more importantly, to outline the importance of cross‐validation when using this method in cluster analysis.     

Clustering Qualitative Data Based on Binary Equivalence Relations: Neighborhood Search Heuristics for the Clique Partitioning Problem

The clique partitioning problem (CPP) requires the establishment of an equivalence relation for the vertices of a graph such that the sum of the edge costs associated with the relation is minimized. The CPP has important applications for the social sciences because it provides a framework for clustering objects measured on a collection of nominal or ordinal attributes. In such instances, the CPP incorporates edge costs obtained from an aggregation of binary equivalence relations among the attributes. We review existing theory and methods for the CPP and propose two versions of a new neighborhood search algorithm for efficient solution. The first version (NS-R) uses a relocation algorithm in the search for improved solutions, whereas the second (NS-TS) uses an embedded tabu search routine. The new algorithms are compared to simulated annealing (SA) and tabu search (TS) algorithms from the CPP literature. Although the heuristics yielded comparable results for some test problems, the neighborhood search algorithms generally yielded the best performances for large and difficult instances of the CPP.     

A variable-selection heuristic for K-means clustering

One of the most vexing problems in cluster analysis is the selection and/or weighting of variables in order to include those that truly define cluster structure, while eliminating those that might mask such structure. This paper presents a variable-selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. The heuristic was subjected to Monte Carlo testing across more than 2200 datasets with known cluster structure. The results indicate the heuristic is extremely effective at eliminating masking variables. A cluster analysis of real-world financial services data revealed that using the variable-selection heuristic prior to the K-means algorithm resulted in greater cluster stability.     

Clamped Corticosterone (B) Reveals the Effect of Endogenous B on Both Facilitated Responsivity to Acute Restraint and Metabolic Responses to Chronic Stress

To determine the effects of both corticosterone (B) and chronic stressors on acute ACTH responses to restraint, young male rats were exposed to streptozotocin-induced diabetes, cold (5-7 degreesC) or intracerebroventricular (icv) neuropeptide Y (NPY) for 5 d and then exposed to restraint within 2 h after lights on. Two groups of rats were studied: intact and adrenalectomized replaced with B pellets that maintained plasma B in the normal mean 24-h range of intact rats. In addition to ACTH and B responses to restraint on d 5, body weight, food intake, fat depots, glucose and other hormones were measured to determine the role of stress-induced elevations in B on energy balance. ACTH responses to restraint were normal in intact rats subjected to diabetes or cold. By contrast, there was no ACTH or B response to restraint in NPY-infused intact rats. All 3 groups of chronically stimulated adrenalectomized rats with clamped B had facilitated ACTH responses to restraint compared to their treatment controls. Overall food intake increased in all groups of stressed rats; however, augmented intake occurred only during the light in intact rats and equally in the light and dark in B-clamped rats. White adipose depot weights were decreased by both diabetes and cold and increased by NPY in intact rats; the decreases with cold and increases with NPY were both blunted and changes in fat stores were not significant in adrenalectomized, B-clamped rats. We conclude that: 1. diabetes- and cold-induced facilitation of restraint-induced afferent input to hypothalamic control of the hypothalamo-pituitary-adrenal (HPA) axis is opposed in intact rats by the elevated feedback signal of B secretion; 2. NPY does not induce facilitation of afferent stress pathways; 3. chronic stimulation of the HPA axis induces acute hyperresponsiveness of hypothalamic neurons to restraint provided that the afferent input of this acute stimulus is not prevented by B feedback; 4. stimulus-induced elevations in B secretion result in day-time feeding; 5. insensitivity of both caloric efficiency and white fat stores to chronic stress in adrenalectomized, B-clamped rats results from loss of normally variable B levels.