scholarly journals Using infographics to improve trust in science: a randomized pilot test

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jon Agley ◽  
Yunyu Xiao ◽  
Esi E. Thompson ◽  
Lilian Golzarri-Arroyo
Keyword(s):  
Iterative Process ◽  
Behavioral Intentions ◽  
Research Team ◽  
Qualitative Data ◽  
Mechanical Turk ◽  
Full Study ◽  
Amazon's Mechanical Turk ◽  
Study Participants ◽  
Qualitative Responses ◽  
Large Randomized Trial

Abstract Objective This study describes the iterative process of selecting an infographic for use in a large, randomized trial related to trust in science, COVID-19 misinformation, and behavioral intentions for non-pharmaceutical prevenive behaviors. Five separate concepts were developed based on underlying subcomponents of ‘trust in science and scientists’ and were turned into infographics by media experts and digital artists. Study participants (n = 100) were recruited from Amazon’s Mechanical Turk and randomized to five different arms. Each arm viewed a different infographic and provided both quantitative (narrative believability scale and trust in science and scientists inventory) and qualitative data to assist the research team in identifying the infographic most likely to be successful in a larger study. Results Data indicated that all infographics were perceived to be believable, with means ranging from 5.27 to 5.97 on a scale from one to seven. No iatrogenic outcomes were observed for within-group changes in trust in science. Given equivocal believability outcomes, and after examining confidence intervals for data on trust in science and then the qualitative responses, we selected infographic 3, which addressed issues of credibility and consensus by illustrating changing narratives on butter and margarine, as the best candidate for use in the full study.

Communicating Reform: Testing an Apology for Police History as a Supplement to a Policy Communication

2021 ◽  
pp. 027507402110488
Author(s):  
Mark Benton
Keyword(s):  
African Americans ◽  
Policy Implementation ◽  
Community Policing ◽  
Qualitative Data ◽  
The United States ◽  
Mechanical Turk ◽  
Police Legitimacy ◽  
Policy Communication ◽  
Amazon's Mechanical Turk ◽  
Vignette Experiment

Policing in the United States has a racist history, with negative implications for its legitimacy among African Americans today. Legitimacy is important for policing's effective operations. Community policing may improve policing's legitimacy but is difficult to implement with fidelity and does not address history. An apology for policing's racist history may work as a legitimizing supplement to community policing. On the other hand, an apology may be interpreted as words without changes in practices. Using a survey vignette experiment on Amazon's Mechanical Turk to sample African Americans, this research tests the legitimizing effect of a supplemental apology for historical police racism during a community policing policy announcement. Statistical findings suggest that supplementing the communication with an apology imparted little to no additional legitimacy on policing among respondents. Qualitative data suggested a rationale: Apologies need not indicate future equitable behavior or policy implementation, with implementation itself seeming crucial for police legitimacy improvements.

Screening Amazon’s Mechanical Turk for Adults With ADHD

2015 ◽  
Vol 23 (10) ◽  
pp. 1178-1187 ◽  
Author(s):  
Brian T. Wymbs ◽  
Anne E. Dawson
Keyword(s):  
Anxiety Disorders ◽  
Mechanical Turk ◽  
Comorbid Depression ◽  
College Degrees ◽  
Demographic Correlates ◽  
Amazon's Mechanical Turk ◽  
Study Participants ◽  
Recruitment Tool ◽  
Age Range ◽  
Screening Survey

Objective: Researchers are increasingly using Amazon’s Mechanical Turk (MTurk; www.mturk.com ) to recruit study participants. However, the utility of MTurk for investigations of ADHD in adulthood is unknown. Method: A total of 6,526 MTurk workers (median age range = 26-35 years) completed an online screening survey assessing their diagnostic histories and symptoms of ADHD, as well as relevant demographic correlates and diagnostic comorbidity. Results: The prevalence of MTurk workers who reported being diagnosed as children and as adults, and the percentage of workers whose ADHD appeared to persist from childhood into adulthood, are consistent with those observed in “offline” samples. Relative to MTurk workers diagnosed with ADHD as adults, workers diagnosed with ADHD as children were more likely to be male and without college degrees, as well as less likely to have comorbid depression or anxiety disorders. Conclusion: Amazon’s MTurk holds promise as a recruitment tool for studying adults with ADHD.

A Technical Guide to Using Amazon's Mechanical Turk in Behavioral Accounting Research

2017 ◽  
Vol 30 (1) ◽  
pp. 111-122 ◽  
Author(s):  
Steve Buchheit ◽  
Marcus M. Doxey ◽  
Troy Pollard ◽  
Shane R. Stinson
Keyword(s):  
Third Party ◽  
Mechanical Turk ◽  
Behavioral Accounting ◽  
Online Data ◽  
Research Participants ◽  
Traditional Research ◽  
Potential Benefits ◽  
Technical Guide ◽  
Online Data Collection ◽  
Amazon's Mechanical Turk

ABSTRACT Multiple social science researchers claim that online data collection, mainly via Amazon's Mechanical Turk (MTurk), has revolutionized the behavioral sciences (Gureckis et al. 2016; Litman, Robinson, and Abberbock 2017). While MTurk-based research has grown exponentially in recent years (Chandler and Shapiro 2016), reasonable concerns have been raised about online research participants' ability to proxy for traditional research participants (Chandler, Mueller, and Paolacci 2014). This paper reviews recent MTurk research and provides further guidance for recruiting samples of MTurk participants from populations of interest to behavioral accounting researchers. First, we provide guidance on the logistics of using MTurk and discuss the potential benefits offered by TurkPrime, a third-party service provider. Second, we discuss ways to overcome challenges related to targeted participant recruiting in an online environment. Finally, we offer suggestions for disclosures that authors may provide about their efforts to attract participants and analyze responses.

scholarly journals MCR: Open-Source Software to Automate Compilation of Health Study Report-Back

2021 ◽  
Vol 18 (11) ◽  
pp. 6104
Author(s):  
Erin Polka ◽  
Ellen Childs ◽  
Alexa Friedman ◽  
Kathryn S. Tomsho ◽  
Birgit Claus Henn ◽  
...  
Keyword(s):  
Quality Control ◽  
Open Source ◽  
Open Source Software ◽  
Research Team ◽  
Self Determination ◽  
Health Study ◽  
Equitable Access ◽  
Team Members ◽  
Study Participants ◽  
The Impact

Sharing individualized results with health study participants, a practice we and others refer to as “report-back,” ensures participant access to exposure and health information and may promote health equity. However, the practice of report-back and the content shared is often limited by the time-intensive process of personalizing reports. Software tools that automate creation of individualized reports have been built for specific studies, but are largely not open-source or broadly modifiable. We created an open-source and generalizable tool, called the Macro for the Compilation of Report-backs (MCR), to automate compilation of health study reports. We piloted MCR in two environmental exposure studies in Massachusetts, USA, and interviewed research team members (n = 7) about the impact of MCR on the report-back process. Researchers using MCR created more detailed reports than during manual report-back, including more individualized numerical, text, and graphical results. Using MCR, researchers saved time producing draft and final reports. Researchers also reported feeling more creative in the design process and more confident in report-back quality control. While MCR does not expedite the entire report-back process, we hope that this open-source tool reduces the barriers to personalizing health study reports, promotes more equitable access to individualized data, and advances self-determination among participants.

Development and Validation of the Ableist Microaggression Impact Questionnaire

2021 ◽  
pp. 003435522110142
Author(s):  
Deniz Aydemir-Döke ◽  
James T. Herbert
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Internal Consistency ◽  
Internal Consistency Reliability ◽  
Mechanical Turk ◽  
Rehabilitation Counselor ◽  
Research And Practice ◽  
Amazon's Mechanical Turk ◽  
Development And Validation ◽  
Three Factor

Microaggressions are daily insults to minority individuals such as people with disabilities (PWD) that communicate messages of exclusion, inferiority, and abnormality. In this study, we developed a new scale, the Ableist Microaggressions Impact Questionnaire (AMIQ), which assesses ableist microaggression experiences of PWD. Data from 245 PWD were collected using Amazon’s Mechanical Turk (MTurk) platform. An exploratory factor analysis of the 25-item AMIQ revealed a three-factor structure with internal consistency reliability ranging between .87 and .92. As a more economical and psychometrically sound instrument assessing microaggression impact as it pertains to disability, the AMIQ offers promise for rehabilitation counselor research and practice.

scholarly journals Engaging bioscientists in science communication: Evidence from an international survey

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 209
Author(s):  
Andrea Boggio ◽  
Giorgio Scita ◽  
Carmen Sorrentino ◽  
David Hemenway ◽  
Andrea Ballabeni
Keyword(s):  
Science Communication ◽  
Qualitative Data ◽  
Basic Research ◽  
Social Value ◽  
Science Outreach ◽  
The Public ◽  
The Social ◽  
Negative Feelings ◽  
Study Participants ◽  
Institutional Actors

Background: Exchanges between scientists and nonscientists are critical to realizing the social value of basic research. These exchanges rest in part on the willingness and ability of scientists to engage effectively in science communication activities. In this paper, we discuss the perception and willingness of basic scientists in the biological and biomedical fields to engage in science outreach. Methods: The analysis is based on qualitative data collected as part of a survey on the social value of basic research and is framed by the theory of planned behavior. This is a well-established theory of human behavior that relies on the premise that a person’s intention to engage in a behavior is the single best predictor of whether that person will in fact engage in that behavior. Results: Our data show that, while bioscientists maintain a positive attitude towards science communication, their intentions are influenced by some negative feelings with regard to how nonscientists react to science communication efforts. Interactions with institutional actors, governmental bodies and the public are particularly problematic. On the other hand, interactions with clinicians and patients are framed in positive terms. Finally, some study participants raised concerns as to their ability to communicate science effectively, the availability of time and resources, and the lack of proper rewards, particularly in terms of career advancement, for those who engage in science efforts. Conclusions: Our findings suggest that bioscientists' intentions to engage in science communication efforts must be better studied to develop empirically-informed interventions to increase scientists’ participation in science outreach efforts.

scholarly journals Predictors of accent-based prejudice

2021 ◽  
Author(s):  
David Matthew Sumantry
Keyword(s):  
Latin America ◽  
Theoretical Models ◽  
Mechanical Turk ◽  
Right Wing ◽  
Negative Stereotypes ◽  
Suitable Alternative ◽  
Content Model ◽  
Stereotype Content ◽  
Amazon's Mechanical Turk ◽  
Direct Measures

This thesis investigated accent-based stereotyping and prejudice – a line of research originating in Lambert et al. (1960) – by studying perceptions of four accented groups. Participants recruited from Amazon’s Mechanical Turk listened to audio clips where the speakers had native accents from either Toronto, Latin America, Arabic countries, or India. They then evaluated the speakers on several dimensions based on the Stereotype Content Model (SCM) and the solidarity-status-dynamism model (SSD), and completed direct measures of prejudice. Speakers were not evaluated differently on measures of prejudice but were stereotyped differently. Participants higher in right-wing ideologies held more negative stereotypes of speakers and demonstrated greater prejudice. Comparing theoretical models indicated that the more commonly-used SCM provides a suitable alternative to the SSD model. Implications for research on accent-based prejudice are discussed.

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