Intentional harms are worse, even when they're not.

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Abstract

People and societies seek to combat harmful events. However, because resources are limited, every wrong righted leaves another wrong left unchecked. Responses must therefore be calibrated to the magnitude of the harm. One underappreciated factor that affects this calibration may be people's oversensitivity to intent. Across a series of studies, people saw intended harms as worse than unintended harms, even though the two harms were identical. This harm-magnification effect occurred for both subjective and monetary estimates of harm, and it remained when participants were given incentives to be accurate. The effect was fully mediated by blame motivation. People may therefore focus on intentional harms to the neglect of unintentional (but equally damaging) harms.

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Winter Mason, Siddharth Suri (2012)

Amazon's Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this article is to demonstrate how to use this Web site for conducting behavioral research and to lower the barrier to entry for researchers who could benefit from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments, and faster iteration between developing theory and executing experiments. While other methods of conducting behavioral research may be comparable to or even better than Mechanical Turk on one or more of the axes outlined above, we will show that when taken as a whole Mechanical Turk can be a useful tool for many researchers. We will discuss how the behavior of workers compares with that of experts and laboratory subjects. Then we will illustrate the mechanics of putting a task on Mechanical Turk, including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform, including techniques for conducting synchronous experiments, methods for ensuring high-quality work, how to keep data private, and how to maintain code security.

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Hart Blanton, James Jaccard (2005)

Many psychological tests have arbitrary metrics but are appropriate for testing psychological theories. Metric arbitrariness is a concern, however, when researchers wish to draw inferences about the true, absolute standing of a group or individual on the latent psychological dimension being measured. The authors illustrate this in the context of 2 case studies in which psychologists need to develop inventories with nonarbitrary metrics. One example comes from social psychology, where researchers have begun using the Implicit Association Test to provide the lay public with feedback about their "hidden biases" via popular Internet Web pages. The other example comes from clinical psychology, where researchers often wish to evaluate the real-world importance of interventions. As the authors show, both pursuits require researchers to conduct formal research that makes their metrics nonarbitrary by linking test scores to meaningful real-world events. 2006 APA, all rights reserved.