Evaluating Amazon's Mechanical Turk Workers for Making Profit-Maximizing Decisions with Decision Support

2016 ◽  
Author(s):  
Yun Shin Lee ◽  
Yong Won Seo
Keyword(s):  
Decision Support ◽  
Mechanical Turk ◽  
Profit Maximizing ◽  
Amazon's Mechanical Turk

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.

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 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.

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.

scholarly journals TheChildLab.com A Video Chat Platform for Developmental Research

2018 ◽  
Author(s):  
Mark Sheskin ◽  
Frank Keil
Keyword(s):  
Large Scale ◽  
Longitudinal Research ◽  
Cross Cultural ◽  
Mechanical Turk ◽  
Future Research ◽  
Psychology Research ◽  
Developmental Research ◽  
The Past ◽  
Adult Participants ◽  
Amazon's Mechanical Turk

Over the past decade, the internet has become an important platform for many types of psychology research, especially research with adult participants on Amazon’s Mechanical Turk. More recently, developmental researchers have begun to explore how online studies might be conducted with infants and children. Here, we introduce a new platform for online developmental research that includes live interaction with a researcher, and use it to replicate classic results in the literature. We end by discussing future research, including the potential for large-scale cross-cultural and longitudinal research.

scholarly journals Against Empathy Bias: The Moral Value of Equitable Empathy

2020 ◽  
Author(s):  
Zoe Fowler ◽  
Kyle Fiore Law ◽  
Brendan Gaesser
Keyword(s):  
Moral Life ◽  
Moral Judgments ◽  
Mechanical Turk ◽  
Moral Value ◽  
Moral Beliefs ◽  
Amazon's Mechanical Turk ◽  
The Relationship ◽  
Theoretical Insight ◽  
Insight Into

Empathy has long been considered central in living a moral life. However, mounting evidence has shown that empathy is often biased towards (i.e., felt more strongly for) close and similar others, igniting a debate over whether empathy is inherently morally flawed and should be abandoned in efforts to strive towards greater equity. This debate has focused on whether empathy limits the scope of our morality, with little consideration of whether it may be our moral beliefs limiting our empathy. Across two studies conducted on Amazon’s Mechanical Turk (N= 604), we investigate moral judgments of biased and equitable feelings of empathy. We observed a moral preference for empathy towards socially close over distant others. However, feeling equal empathy for all is seen as the most morally and socially valuable. These findings provide new theoretical insight into the relationship between empathy and morality with implications for navigating towards a more egalitarian future.

scholarly journals Variation among Mechanical Turk Workers Across Time of Day Presents an Opportunity and a Challenge for Research

2019 ◽  
Author(s):  
Nathan Fordsham ◽  
Aaron J Moss ◽  
Sam Krumholtz ◽  
Thomas Roggina ◽  
Jonathan Robinson ◽  
...  
Keyword(s):  
Disordered Eating ◽  
Sampling Methods ◽  
Time Of Day ◽  
Mechanical Turk ◽  
Behavioral Characteristics ◽  
Online Platforms ◽  
Day Of The Week ◽  
Amazon's Mechanical Turk ◽  
Online Sampling ◽  
Anxiety Depression

Conducting behavioral research online allows researchers to gather more data in less time than conducting studies in person. But this efficiency may sometimes have a cost. Specifically, when researchers gather data within just a few hours, their study may be subject to a time of day bias. Because participants in online platforms are generally free to complete studies whenever they want, people who take studies in the morning may be different in important ways than those who take studies at night. We explored this possibility in two studies conducted on Amazon’s Mechanical Turk. In both studies, we sampled participants at different times of the day and examined whether morning and evening active people differed on a variety of psychological and behavioral characteristics known to correlate with a preference for either morningness or eveningness. We found that participants active in the morning and the evening reported different circadian typologies. Additionally, we found that participants active in the morning reported more conscientiousness and less anxiety, depression, procrastination, internet compulsion, disruptive sleep behaviors, disordered eating, and neuroticism than those sampled in the evening. Study 2 demonstrated that many signs of sub-clinical behavior were uniquely high among evening oriented people and that differences between morning and evening oriented people remained robust after controlling for local time zones and day of the week. Overall, our findings have important implications for online sampling methods and indicate that time of day differences in the composition of online samples represent both an opportunity and a challenge for research.

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