1. On note taking

In this paper the author explores the theoretical and technical issues relating to taking notes of analytic sessions, using an introspective approach. The paper discusses the lack of a consistent approach to note taking amongst analysts and sets out to demonstrate that systematic note taking can be helpful to the analyst. The author describes his discovery that an initial phase where as much data was recorded as possible did not prove to be reliably helpful in clinical work and initially actively interfered with recall in subsequent sessions. The impact of the nature of the analytic session itself and the focus of the analyst's interest on recall is discussed. The author then describes how he modified his note taking technique to classify information from sessions into four categories which enabled the analyst to select which information to record in notes. The characteristics of memory and its constructive nature are discussed in relation to the problems that arise in making accurate notes of analytic sessions.     

Three-Mode Component Analysis with Crisp or Fuzzy Partition of Units

A new methodology is proposed for the simultaneous reduction of units, variables, and occasions of a three-mode data set. Units are partitioned into a reduced number of classes, while, simultaneously, components for variables and occasions accounting for the largest common information for the classification are identified. The model is a constrained three-mode factor analysis and it can be seen as a generalization of the REDKM model proposed by De Soete and Carroll for two-mode data. The least squares fitting problem is mathematically formalized as a constrained problem in continuous and discrete variables. An iterative alternating least squares algorithm is proposed to give an efficient solution to this minimization problem in the crisp and fuzzy classification context. The performances of the proposed methodology are investigated by a simulation study comparing our model with other competing methodologies. Different procedures for starting the proposed algorithm have also been tested. A discussion of some interesting differences in the results follows. Finally, an application to real data illustrates the ability of the proposed model to provide substantive insights into the data complexities.     

An IRT Model with a Parameter-Driven Process for Change

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative interindividual differences and autodependencies are accounted for by assuming within-subject variability with respect to the parameters of the IRT model. In particular, the parameters of the IRT model are governed by an unobserved or “hidden'” homogeneous Markov process. The model includes the mixture linear logistic test model (Mislevy & Verhelst, 1990), the mixture Rasch model (Rost, 1990), and the Saltus model (Wilson, 1989) as specific instances. The model is applied to a longitudinal experiment on discontinuity in conservation acquisition (van der Maas, 1993). Frank Rijmen was supported by the Fund for Scientific Research Flanders (FWO), the GOA/2000/02 granted by the Katholieke Universiteit Leuven to Paul De Boeck and Iven Van Mechelen, and the PDM/02/067 granted by the Katholieke Universiteit Leuven to Paul De Boeck.     

Hierarchical classes models for three-way three-mode binary data: interrelations and model selection

Several hierarchical classes models can be considered for the modeling of three-way three-mode binary data, including the INDCLAS model (Leenen, Van Mechelen, De Boeck, and Rosenberg, 1999), the Tucker3-HICLAS model (Ceulemans, Van Mechelen, and Leenen, 2003), the Tucker2-HICLAS model (Ceulemans and Van Mechelen, 2004), and the Tucker1-HICLAS model that is introduced in this paper. Two questions then may be raised: (1) how are these models interrelated, and (2) given a specific data set, which of these models should be selected, and in which rank? In the present paper, we deal with these questions by (1) showing that the distinct hierarchical classes models for three-way three-mode binary data can be organized into a partially ordered hierarchy, and (2) by presenting model selection strategies based on extensions of the well-known scree test and on the Akaike information criterion. The latter strategies are evaluated by means of an extensive simulation study and are illustrated with an application to interpersonal emotion data. Finally, the presented hierarchy and model selection strategies are related to corresponding work by Kiers (1991) for principal component models for three-way three-mode real-valued data.     

Effects of social proximity on multiple aggressive behaviors

We systematically manipulated social proximity to examine its influence on multiple topographies of aggression. Aggression occurred at high levels during close-proximity sessions and at low levels during distant-proximity sessions even though social contact was presented continuously during both conditions. Topographies of aggression emerged sequentially across the close-proximity sessions, and all topographies were reduced to zero following extended sessions of this condition.     

Tensions of Accountability: Scientists,Technicians and the Ethical Life of Data Production in Astronomy

ABSTRACT

Laboratory ethnographies have emphasized the importance of trust and accountability among scientists and technicians when working in co-presence. But what happens to their relations when data production is outsourced, widely separating scientists and technicians? In contemporary astronomy, observing has largely become a service that observatory staff members do for researchers. This has increased the efficiency of telescope use while also resulting in arrangements prone to some researchers’ dissatisfaction and a raised visibility of technicians’ work. Attending ethnographically to situated evaluations reveals that some data-using researchers and data-producing technicians typify each other as moral figures: staff observers come to be seen as objects of blame and scientists as excessively demanding users. Wherever data travel, the quality and quantity of data can serve as ethical affordances for assigning blame to staff technicians. By contrast, the moral figure of the excessively demanding data user does not travel beyond the observatory, and the social accountability of interactions that visiting astronomers experience in the observatory’s control room is confined to its situated enactment. As scientist–technician interactions have become discontinuous and fraught, moral figures and ethical accounts thus travel unevenly in the landscape of data production, and the importance of social interactions remains often unrecognized.     

Mutual Funds Efficiency Measurement under Financial and Social Responsibility Criteria

Socially responsible investors pursue financial as well as nonfinancial goals. Whereas the role of financial criteria in investment decisions is well understood, much less is known on the influence of social responsibility considerations. This work seeks to integrate both dimensions within a data envelopment analysis framework consistent with second‐order stochastic dominance efficiency. We compare the performance of conventional versus socially responsible mutual funds on an empirical data set. Our data do not support the conjecture that conventional mutual funds exhibit superior overall performance. Copyright © 2013 John Wiley & Sons, Ltd.     

Rethink "Affirmative Agreement," but Abandon "Assent"

Training in ethics and professionalism is a fundamental component of residency education, yet there is little empirical information to guide curricula. The objective of this study is to describe empirically derived ethics objectives for ethics and professionalism training for multiple specialties. Study design is a thematic analysis of documents, semi-structured interviews, and focus groups conducted in a setting of an academic medical center, Veterans Administration, and community hospital training more than 1000 residents. Participants were 84 informants in 13 specialties including residents, program directors, faculty, practicing physicians, and ethics committees. Thematic analysis identified commonalities across informants and specialties. Resident and nonresident informants identified consent, interprofessional relationships, family interactions, communication skills, and end-of-life care as essential components of training. Nonresidents also emphasized formal ethics instruction, resource allocation, and self-monitoring, whereas residents emphasized the learning environment and resident-attending interactions. Conclusions are that empirically derived learning needs for ethics and professionalism included many topics, such as informed consent and resource allocation, relevant for most specialties, providing opportunities for shared curricula and resources.     

Effects of Auditory Feedback on Movement Time in a Fitts Task

The purpose of the present experiment was to investigate the role of auditory feedback and its impact on movement time in a standard Fitts task. Feedback was given at the moment of target acquisition. A 2-way analysis of variance found significant differences between feedback groups at all three indexes of difficulty (F(2, 40) = 156.02, p < .001). Results from a mixed-model multivariate analysis of variance for kinematic factors show significant differences in peak velocity and the location of peak velocity when comparing feedback groups. In general, the addition of auditory feedback decreased the task ID by .5.     

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE)

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participant’s personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual’s data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies.