PREDICTING MARITAL SUCCESS FOR PREMARITAL COUPLE TYPES BASED ON PREPARE

Recent studies have shown that group differences in marital stability and satisfaction can be predicted based on premarital relationship quality. There is also a growing literature indicating that there are distinct types of relationships, both premaritally and developmentally over time. This study examined the relationship between the four premarital types (Vitalized, Harmonious, Traditional, and Conflicted) identified by Fowers and Olson (1992) and relationship outcome over a 3-year period with 393 couples. A substantial relationship was found, with conflicted couples being the most likely to separate or divorce. Vitalized couples had the highest levels of satisfaction, followed by Harmonious, Traditional, and Conflicted couples. Traditional couples were less likely to have divorced than Harmonious couples, even though Harmonious couples had higher premarital relationship satisfaction scores.     

Scheduling Updates of Probabilistic Risk Assessments: The Arkansas Nuclear One-Unit 1 Experience

This paper presents the results of a study that identified how often a probabilistic risk assessment (PRA)should be updated to accommodate the changes that take place at nuclear power plants. Based on a 7-year analysis of design and procedural changes at one plant, we consider 5 years to be the maximum interval for updating PRAs. This conclusion is preliminary because it is based on the review of changes that occurred at a single plant, and it addresses only PRAs that involve a Level 1 analysis (i.e., a PRA including calculation of core damage frequency only). Nevertheless, this conclusion indicates that maintaining a useful PRA requires periodic updating efforts. However, the need for this periodic update stems only partly from the number of changes that can be expected to take place at nuclear power plants–changes that individually have only a moderate to minor impact on the PRA, but whose combined impact is substantial and necessitates a PRA update. Additionally, a comparison of two generations of PRAs performed about 5 years apart indicates that PRAs must be periodically updated to reflect the evolution of PRA methods. The most desirable updating interval depends on these two technical considerations as well as the cost of updating the PRA. (Cost considerations, however, were beyond the scope of this study.)     

Toward a formal theory of interactive resocialization: The case of adoptive parenthood

Resocialization is pulled out of its structuralist roots and recast into an interactionist framework. Building on a substantive theory of the resocialization experience of becoming an adoptive parent, three theoretical properties of interactionist resocialization are presented. These can be summarized as involving unlearning and relearning some aspects of a core identity; the interpretation and active reconstruction of that identity which has reciprocal effects on all actors involved; and finally, the effects of both situational and trans-situational norms that give shape to the resocialization experience. Suggestions are made for other substantive areas of research that could be used for comparative analysis in order to further refine a formal theory of interactionist resocialization.     

The analysis of incomplete data using stochastic covariates

In the development of many diseases there are often associated random variables which continuously reflect the progress of a subject towards the final expression of the disease (failure). At any given time these processes, which we call stochastic covariates, may provide information about the current hazard and the remaining time to failure. Likewise, in situations when the specific times of key prior events are not known, such as the time of onset of an occult tumour or the time of infection with HIV-1, it may be possible to identify a stochastic covariate which reveals, indirectly, when the event of interest occurred. The analysis of carcinogenicity trials which involve occult tumours is usually based on the time of death or sacrifice and an indicator of tumour presence for each animal in the experiment. However, the size of an occult tumour observed at the endpoint represents data concerning tumour development which may convey additional information concerning both the tumour incidence rate and the rate of death to which tumour-bearing animals are subject. We develop a stochastic model for tumour growth and suggest different ways in which the effect of this growth on the hazard of failure might be modelled. Using a combined model for tumour growth and additive competing risks of death, we show that if this tumour size information is used, assumptions concerning tumour lethality, the context of observation or multiple sacrifice times are no longer necessary in order to estimate the tumour incidence rate. Parametric estimation based on the method of maximum likelihood is outlined and is applied to simulated data from the combined model. The results of this limited study confirm that use of the stochastic covariate tumour size results in more precise estimation of the incidence rate for occult tumours.     

Comparison of three estimators of the number of species

We consider estimation of the number of cells in a multinomial distribution. This is one version of the species problem: there are many applications, such as the estimation of the number of unobserved species of animals; estimation of vocabulary size, etc. We describe the results of a simulation comparison of three principal frequent-ist' procedures for estimating the number of cells (or species). The first procedure postulates a functional form for the cell probabilities; the second procedure approxi mates the distribution of the probabilities by a parametric probability density function; and the third procedure is based on an estimate of the sample coverage, i.e. the sum of the probabilities of the observed cells. Among the procedures studied, we find that the third (non-parametric) method is globally preferable; the second (functional parametric) method cannot be recommended; and that, when based on the inverse Gaussian density, the first method is competitive in some cases with the third method. We also discuss Sichel's recent generalized inverse Gaussian-based procedure which, with some refine ment, promises to perform at least as well as the non-parametric method in all cases.     

HIV-infected woman. Illness cognition assessment.

1. Special attention is particularly important in addressing the mental health needs of HIV-infected women. These needs may involve issues such as concerns about present or future pregnancy; child-rearing, often as single parents; poverty, illicit drug use or addiction; and the potential lack of support services. 2. Diagnosis of HIV infection in women may be overlooked, as the clinical signs that routinely trigger the suspicion of HIV illness in men are often interpreted to reflect depression or psychological factors, such as stress or overwork, rather than raise the suspicion of HIV infection in women. 3. Understanding a woman's illness cognition is an important component of designing strategies that can enhance coping. The picture a woman generates regarding her illness is a composite of her experiences and responses, and determines to a large extent what she will do in response to her illness.     

THE INFLUENCE OF ECONOMICS ON ANTITRUST LAW

Economists today play prominent roles in formulating antitrust policy and litigating antitrust cases. This paper explains why economics influences antitrust law and describes how economic theories enter and shape the antitrust system. Antitrust policy and doctrine change over time in response to developments in economic theory, and the decentralization of the antitrust adjudication system and the wide latitude accorded judges in interpreting antitrust statutes ensure that legal rules will reflect advances in the economic literature concerning the appropriate content of standards governing business conduct.     

Implications of temporal aggregation on the relation between two time series

Using a simple theoretical model and giving an empirical example it is investigated if it matters whether we use (monthly) averages or end-of-period (month) data in performing Granger-causality tests. It is shown that no major problems are to be expected if the two series are temporally aggregated in the same way, but non-trivial problems can occur if the two series have different kinds of temporal aggregation.