Reducing Sexual-Orientation Discrimination: Experimental Evidence from Basic Information Treatments

We study basic information treatments regarding sexual orientation using randomized experiments in three countries with strong and widespread anti-gay attitudes: Serbia, Turkey, and Ukraine. Participants who received information about the economic costs to society of sexual-orientation discrimination were significantly more likely than those in a control group to support equal employment opportunities based on sexual orientation. Information that the World Health Organization (WHO) does not regard homosexuality as a mental illness increased social acceptance of sexual minorities, but only for those who reported trust in the WHO. Our results have important implications for policy makers aiming to expand the rights of lesbian, gay, and bisexual people worldwide.

Situational Peer Dynamics and Crime Decisions

Objectives To investigate how peer dynamics, specifically interpersonal conversations between a potential offender and a peer, contemporaneous with a crime opportunity, influence perceptions of sanction certainty and social costs. Methods Data are analyzed from randomized experiments and hypothetical vignettes embedded within a nationwide, online survey ( n = 1,275). Vignettes were presented for three distinct crime opportunities, drunk driving, fighting, and insurance fraud. Results The findings suggest that respondents adjust two core decision-making perceptions—the perceived certainty of being legally sanctioned and perceived social costs such as stigma or embarrassment—in accord with the content of verbal communications from peers. There is evidence for this both between and within subjects. Conclusions The study underscores the importance of accounting for both physical and social features of the situational context for crime in models of offender decision making. Implications are drawn regarding the social milieu for offender decision making, and the broader criminological relevance of choice principles.

Mediation: Causal Mechanisms in Business and Management

Mediation refers to causation. Tests for mediation are common in business, management, and related fields. In the simplest mediation model, a researcher asserts that a treatment causes a mediator and that the mediator causes an outcome. For example, a practitioner might examine whether diversity training increases awareness of stereotypes, which, in turn, improves inclusive climate perceptions. Because mediation inferences are causal inferences, it is important to demonstrate that the cause actually precedes the effect, the cause and effect covary, and rival explanations for the causal effect can be ruled out. Although various experimental designs for testing mediation hypotheses are available, single randomized experiments and two randomized experiments provide the strongest evidence for inferring mediation compared with nonexperimental designs, where selection bias and a multitude of confounding variables can make causal interpretations difficult. In addition to experimental designs, traditional statistical approaches for testing mediation include causal steps, difference in coefficients, and product of coefficients. Of the traditional approaches, the causal steps method tends to have low statistical power; the product of coefficients method tends to provide adequate power. Bootstrapping can improve the performance of these tests for mediation. The general causal mediation framework offers a modern approach to testing for causal mechanisms. The general causal mediation framework is flexible. The treatment, mediator, and outcome can be categorical or continuous. The general framework not only incorporates experimental designs (e.g., single randomized experiments, two randomized experiments) but also allows for a variety of statistical models and complex functional forms.

Protocol for Development of a Reporting Guideline for Causal and Counterfactual Prediction Models

[Introduction] While there are protocols for reporting on observational studies (e.g., STROBE, RECORD), estimation of causal effects from both observational data and randomized experiments (e.g., AGREMA, CONSORT), and on prediction modelling(e.g., TRIPOD), none is purposely made for assessing the ability and reliability of models to predict counterfactuals for individuals upon one or more possible interventions, on the basis of given (or inferred) causal structures. This paper describes methods and processes that will be used to develop a reporting guideline for causal and counterfactual prediction models(tentative acronym: PRECOG). [Materials and Methods] PRECOG will be developed following published guidance from the EQUATOR network, and will comprise five stages. Stage 1 will be bi-weekly meetings of a working group with external advisors (active until stage 5). Stage 2 will comprise a scoping/systematic review of literature on counterfactual prediction modelling for biomedical sciences(registered in PROSPERO). In stage 3, we will perform a computer-based, real-time Delphi survey to consolidate the PRECOGchecklist, involving experts in causal inference, statistics, machine learning, prediction modelling and protocols/standards. Stage 4 will involve the write-up of the PRECOG guideline (including its checklist) based on the results from the prior stages. In stage 5, we will work on the publication of the guideline and of the scoping/systematic review as peer-reviewed, open-access papers, and on their dissemination through conferences, websites, and social media. [Conclusions] PRECOG can help researchers and policymakers to carry out and critically appraise causal and counterfactual prediction model studies. PRECOG will also be useful for designing interventions, and we anticipate further expansion of the guideline for specific areas, e.g., pharmaceutical interventions.

How Communicating about Discrimination Influences Attributions of Blame and Condemnation

Across two randomized experiments, we examine how communication about discriminatory acts can influence judgments of blame and condemnation. Specifically, we consider whether attributing discrimination to implicit or explicit bias affects how people evaluate online reports of discrimination. In Study 1 (N = 947), we explore this question in the context of an online news environment, and in Study 2 (N = 121) we replicate our results on a social media site (i.e., Twitter). Across both studies, we document how viewers respond differently to reports of discrimination due to variation in agent motives, the type of bias that purportedly caused the discriminatory behavior, and the extent to which agents are reported to have completed implicit bias training. We discuss our theoretical contribution to perspectives of blame attribution and the communication of bias as well as the practical implications of our findings.

Pengembangan Aplikasi Asisten Pintar Pembuka Al Qur’an 30 Juz dengan Perintah Voice Command

Many developers of digital Qur'an applications today still use tap to scrolling to run applications, although the features are interesting. This makes it less effective and efficient in opening the Qur'an. As is the case during the taklim assembly, some da'i are very interactive with jama'ah, asking to open certain surahs and verses so that there are some who have difficulty in searching. Therefore, the need for the Qur'anic application with voice command command to facilitate users. This research is the development of the Qur'an application with voice recognition feature. Using the waterfall method in development, voice command with google speech API as a voice command of surah and verse calling in the Qur'an application 30 juz. Conducted 10 randomized experiments with calls in the form of play or open surahs and certain verses give a 90% accuracy result. Commands can be given when online or offline. Then the use of google speech API can be very useful for use in the development of other applications.