The Psychosocial Effects of Drone Violence: Social Isolation,Self‐Objectification,and Depoliticization

The U.S.‐led drone program has severe and wide‐ranging psychosocial and political effects on people in targeted areas. This article draws upon the author's interviews with people living under drones in Afghanistan to argue that drone surveillance and bombardment causes social isolation and self‐objectification. Interviewees reported that social gatherings, and any activities that involve nighttime travel, are shortened or avoided out of fear that drone surveillance will spot so‐called “nefarious activity” and bombardment will follow. Drawing upon feminist and postcolonial scholarship, the article contends that drone surveillance constitutes a form of psychological colonization. Civilians are frequently made to think of how they look to drone operators and change their behaviors accordingly—therein “self‐objectifying.” Narratives of “precision” warfare allow citizens of Western liberal democracies leading/supporting the drone program to avoid psychologically confronting these effects, while people living under drones face relentless psychological insecurities.     

MINORITY WOMEN IN ACADEMIA Access and Barriers to Professional Participation

The experiences that women, especially minority-status women (African, Asian, Hispanic, and Native American), have in educational advancement in psychology is limited. This limited power was examined in two ways: (a) by reviewing the inclusion of minority women within academic psychology at undergraduate, graduate, job entry, and senior level positions within the profession and (b) by reviewing economic and social-psychological processes that unjustly serve as barriers to women.     

A test of the negative discriminative stimulus as a reinforcer of observing

Five pigeons were used to test the hypothesis that the source of reinforcement for observing behavior is the information that it provides concerning the schedule of primary reinforcement. On a variable-interval schedule, pecking the left-hand key produced a 30-sec display of such information. During this 30-sec period, when pecking the right-hand key was reinforced on a random-interval schedule, both keys were green; when no reinforcement was scheduled (extinction) both keys were red. Later, this baseline procedure, in which both red and green were available, was replaced for blocks of sessions by procedures in which either (a) the red was eliminated and only the green could be produced; or (b) the green was eliminated and only the red could be produced. The results were that green maintained rates of pecking on the left key that were as high or higher than when both colors were available and that red maintained no responding. It was concluded that the reinforcing value of a stimulus depends on the positive or negative direction of its correlation with primary reinforcement, rather than upon the amount of information that it conveys.     

Arguments in the practical reasoning underlying constructed probability responses

Reasoning is an important cognitive activity in probability assessment, and one that has been understudied. This proposition motivates the paper's three general aims. First, based on research in rhetoric, we present a theoretical means of analyzing the arguments constructed during the reasoning that occurs in probability assessment. Second, from verbal protocol data, we establish that subjects constructed arguments in forming beliefs and assessing the associated probabilities. Third, we analyze the data for the structure of subject's arguments, including argument content and form. Subjects used a limited amount of relevant evidence and used a variety of argument forms that could be characterized by the nature of the knowledge that subjects brought to bear in forming the arguments. Subjects predominantly used causal reasoning, but also employed hierarchical category knowledge, resemblance relationships, and arguments from authority. These findings form a basis for expanding our accounts of probability assessment and for improving assessment practice.     

A note on lower bounds for the number of common factors

It is shown that the lower bounds for the number of common factors, established by Guttman [1954] and modified by Kaiser [1961], cannot decrease as the number of observed variates is increased. The result implies that the lower bounds cannot become weaker if the number of observed variates is increased and the number of factors remains constant.This paper is based on a section of a thesis submitted to the Department of Statistics of the University of the Witwatersrand in fulfillment of the requirements for a M.Sc. degree.Presently at the National Institute for Personnel Research, Johannesburg, South Africa.     

On oblique procrustes rotation

The equations involved in the rotation of an arbitrary factor matrix to a least squares fit to a specified factor structure have been known for many years. These equations, in general, cannot be solved by purely algebraic means, and an approximate solution has previously been used in practical applications.In this paper an effective iterative method for obtaining the exact solution is developed. By algebraic manipulation the set of equations is expressed in the form of one polynomial equation in one unknown. Newton's method is suggested for solving this equation. Practical applications of the procedure indicate that convergence within small tolerance limits is generally attained after few iterations.Part of this research was carried out at the National Institute for Personnel Research (South Africa). It was completed while the author was a Visiting Research Psychologist at Educational Testing Service.     

When fit indices and residuals are incompatible

Standard chi-square-based fit indices for factor analysis and related models have a little known property: They are more sensitive to misfit when unique variances are small than when they are large. Consequently, very small correlation residuals indicating excellent fit can be accompanied by indications of bad fit by the fit indices when unique variances are small. An empirical example of this incompatibility between residuals and fit indices is provided. For illustrative purposes, an artificial example is provided that yields exactly the same correlation residuals as the empirical example but has larger unique variances. For this example, the fit indices indicate excellent fit. A theoretical explanation for this phenomenon is provided using relationships between unique variances and eigenvalues of the fitted correlation matrix.