TRACKING PERFORMANCE UNDER THREAT-INDUCED STRESS

Operator tracking performance in 3 simulated guided missile systems was studied under short-term psychological stress induced by threatening with and also delivering unpleasant electric shocks. The initial part of the stress period was characterized by a moderate decrement of performance and a heightened arousal level, which is regarded as a partial confirmation of the activation theory. The results may also be interpreted in terms of a simple stress-over-time model, based on a hypothetical interaction between arousal and habituation.     

Appraisal and coping processes in acute time-limited stressful situations: A study of police officers

This study focuses on the appraisal and coping process in acute, time-limited stressful situations in a stress experienced group: 54 Swedish police officers. Each police officer retrospectively reported their thoughts, emotions, and actions during five recent stressful job events. The relations among appraisals of threat and of coping options, seven forms of problem- and emotion-focused coping strategies, and selfrated performance were examined. Compared with previous studies of ordinary people in stressful situations, the police officers felt less threatened, appraised the situations as more solvable, and used considerably more problem-focused forms of coping. Age had a significant effect on appraisal, while organizational position had a significant effect on coping. Using latent profile analysis, four appraisal styles were obtained. Consistency of cross-situational appraisal style amongst individuals was high. The functional relationships among variables, i.e. appraisal, coping, and performance, are discussed.     

Sex differences in conformity: Instrumental or communal response?

Male and female introductory psychology students (N=88) were asked to read a clinical case history and the opinion consensus of either (a) their peers, (b) advanced psychology students, or (c) Ph.D. clinical psychologists that the patient should be hospitalized for at least 90 days (an extreme treatment). Subjects then indicated on a scale of 10 to 90 days, the length of hospitalization they thought best. In a significant interaction effect (sex of subject × expertise of source) males were least influenced by the opinions of their peers and increasingly influenced as the source became more expert. Females, however, seemed most influenced by their peer group, recommending longest hospitalization terms in that condition. Results are interpreted in terms of sex-role identification and the emergence of an instrumental (task) or communal set.     

Growth modeling with nonignorable dropout: alternative analyses of the STAR*D antidepressant trial

This article uses a general latent variable framework to study a series of models for nonignorable missingness due to dropout. Nonignorable missing data modeling acknowledges that missingness may depend not only on covariates and observed outcomes at previous time points as with the standard missing at random assumption, but also on latent variables such as values that would have been observed (missing outcomes), developmental trends (growth factors), and qualitatively different types of development (latent trajectory classes). These alternative predictors of missing data can be explored in a general latent variable framework with the Mplus program. A flexible new model uses an extended pattern-mixture approach where missingness is a function of latent dropout classes in combination with growth mixture modeling. A new selection model not only allows an influence of the outcomes on missingness but allows this influence to vary across classes. Model selection is discussed. The missing data models are applied to longitudinal data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, the largest antidepressant clinical trial in the United States to date. Despite the importance of this trial, STAR*D growth model analyses using nonignorable missing data techniques have not been explored until now. The STAR*D data are shown to feature distinct trajectory classes, including a low class corresponding to substantial improvement in depression, a minority class with a U-shaped curve corresponding to transient improvement, and a high class corresponding to no improvement. The analyses provide a new way to assess drug efficiency in the presence of dropout.     

Do we Need a Special Ethics for Research?

Research is subject to more stringent ethical requirements than most other human activities, and a procedure that is otherwise allowed may be forbidden in research. Hence, risk-taking is more restricted in scientific research than in most non-research contexts, and privacy is better protected in scientific questionnaires than in marketing surveys. Potential arguments for this difference are scrutinized. The case in its favour appears to be weak. A stronger case can be made in favour of a difference in the opposite direction: If perilous or otherwise problematic activities have to be performed it is usually better to perform them in a research context where they are properly evaluated so that guidance is obtained for the future. However, retreating from current ethical demands on research is not a desirable direction to go. Instead, research ethics can serve to inspire the introduction of more stringent ethical principles in other social sectors.     

Beyond the intellectual

Pastoral Psychology -     

Genetic and environmental influences on mothering of adolescents: a comparison of two samples

This study examined 2 samples of adolescents and mothers using a child-based design (Nonshared Environment in Adolescent Development [NEAD] project, N = 395 families) and a parent-based design (Twin Moms [TM] project, N = 236 twin family pairs) to compare genetic and environmental influences on mothering. For both samples, the same measures of positivity, negativity, control, and monitoring were used. The use of matched child-based and parent-based samples enabled passive and nonpassive genotype-environment (GE) correlations to be approximated, providing information about process. Passive GE correlations were suggested for mother's positivity and monitoring. For mother's negativity and control, primarily nonpassive GE correlations were suggested. In several cases, both types of GE correlation were indicated. Finally, observer ratings of negativity and monitoring were influenced only by environmental factors.     

Order-Independent Transformative Decision Rules

A transformative decision rule alters the representation of a decision problem, either by changing the set of alternative acts or the set of states of the world taken into consideration, or by modifying the probability or value assignments. A set of transformative decision rules is order-independent in case the order in which the rules are applied is irrelevant. The main result of this paper is an axiomatic characterization of order-independent transformative decision rules, based on a single axiom. It is shown that the proposed axiomatization resolves a problem observed by Teddy Seidenfeld in a previous axiomatization by Peterson.     

Investigating population heterogeneity with factor mixture models

Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved population heterogeneity. Observed sources of heterogeneity can be included as covariates. The different ways to incorporate covariates correspond to different conceptual interpretations. These are discussed in detail. Characteristics of factor mixture modeling are described in comparison to other methods designed for data stemming from heterogeneous populations. A step-by-step analysis of a subset of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied in an exploratory fashion to data collected at a single time point.     

Selective Revision

We introduce a constructive model of selective belief revision in which it is possible to accept only a part of the input information. A selective revision operator ο is defined by the equality K ο α = K * f(α), where * is an AGM revision operator and f a function, typically with the property ⊢ α → f(α). Axiomatic characterizations are provided for three variants of selective revision. This revised version was published online in June 2006 with corrections to the Cover Date.