Online Instrument Delivery and Participant Recruitment Services: Emerging Opportunities for Behavioral Accounting Research

2013 ◽  
Vol 26 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Duane M. Brandon ◽  
James H. Long ◽  
Tina M. Loraas ◽  
Jennifer Mueller-Phillips ◽  
Brian Vansant
Keyword(s):  
Mechanical Turk ◽  
Professional Organizations ◽  
Behavioral Accounting ◽  
Participant Recruitment ◽  
Accounting Research ◽  
Research Participants ◽  
Delivery Services ◽  
Geographically Distributed ◽  
Amazon's Mechanical Turk

ABSTRACT Behavioral accounting researchers have historically been constrained in their ability to reach externally valid research participants. The purpose of this paper is to familiarize researchers with two relatively new and innovative ways to overcome this issue. First, this paper discusses two online instrument delivery services provided by SurveyMonkey and Qualtrics that can be used to distribute experimental materials to geographically distributed participants quickly and inexpensively. Second, it reviews a number of participant recruitment services that behavioral accounting researchers can use to identify and recruit externally valid research participants. Specifically, this paper discusses commercial participant recruitment services provided by SurveyMonkey Audience, Qualtrics, Amazon's Mechanical Turk, and other commercial firms, as well as several non-commercial participant recruitment services associated with industry and professional organizations. Each service is evaluated against three criteria that are important to behavioral accounting researchers: (1) cost, (2) flexibility, and (3) access to populations of interest.

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.

PsyToolkit

2016 ◽  
Vol 44 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Gijsbert Stoet
Keyword(s):  
Social Media ◽  
Mechanical Turk ◽  
Web Based ◽  
Participant Recruitment ◽  
Social Media Networks ◽  
Extensive Documentation ◽  
Interactive Environment ◽  
Psychological Scales ◽  
Set Up ◽  
Amazon's Mechanical Turk

This article reviews PsyToolkit, a free web-based service designed for setting up, running, and analyzing online questionnaires and reaction-time (RT) experiments. It comes with extensive documentation, videos, lessons, and libraries of free-to-use psychological scales and RT experiments. It provides an elaborate interactive environment to use (or modify) the existing questionnaires and experiments from the PsyToolkit library or to design new studies. Once users have set up their study, they can recruit participants for online participation, and data can be downloaded in spreadsheet format after collection. This article provides examples of how questionnaires and RT experiments can be set up using the website. The PsyToolkit links to online questionnaires and experiments, and these links can easily be embedded in social media networks for participant recruitment, including Amazon's Mechanical Turk. PsyToolkit’s exhaustive documentation enables students to work independently. This article finishes with pedagogical considerations.

Scoundrels or Stars? Theory and Evidence on the Quality of Workers in Online Labor Markets

2016 ◽  
Vol 92 (1) ◽  
pp. 93-114 ◽  
Author(s):  
Anne M. Farrell ◽  
Jonathan H. Grenier ◽  
Justin Leiby
Keyword(s):  
Labor Markets ◽  
Data Availability ◽  
Accounting Research ◽  
Research Participants ◽  
Large Numbers ◽  
Research Designs ◽  
Online Labor Markets ◽  
Online Labor ◽  
Low Pay

ABSTRACT Online labor markets allow rapid recruitment of large numbers of workers for very low pay. Although online workers are often used as research participants, there is little evidence that they are motivated to make costly choices to forgo wealth or leisure that are often central to addressing accounting research questions. Thus, we investigate the validity of using online workers as a proxy for non-experts when accounting research designs use more demanding tasks than these workers typically complete. Three experiments examine the costly choices of online workers relative to student research participants. We find that online workers are at least as willing as students to make costly choices, even at significantly lower wages. We also find that online workers are sensitive to performance-based wages, which are just as effective in inducing high effort as high fixed wages. We discuss implications of our results for conducting accounting research with online workers. Data Availability: Contact the authors.

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.

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