Values of Participants in Behavioral Accounting Research: A Comparison of the M-Turk Population to a Nationally Representative Sample

2018 ◽  
Vol 31 (1) ◽  
pp. 97-117 ◽  
Author(s):  
William D. Brink ◽  
Lorraine S. Lee ◽  
Jonathan S. Pyzoha

ABSTRACT The external validity of conclusions from behavioral accounting experiments is in part dependent upon the representativeness of the sample compared to the population of interest. Researchers are beginning to leverage the availability of workers via online labor markets, such as Amazon's Mechanical Turk (M-Turk), as proxies for the general population (e.g., investors, jurors, and taxpayers). Using over 200 values-based items from the World Values Survey (WVS), the purpose of the current study is to explore whether U.S. M-Turk workers' values are similar to those of the U.S. population. Results show for the majority of items collected, M-Turk participants' values are significantly different from the WVS participants (e.g., values related to trust, ethics, religious beliefs, and politics). We present select items and themes representing values shown to influence judgments in prior research and discuss how those values may affect inferences of behavioral accounting researchers. Data Availability: Data are available from the authors upon request.

2016 ◽  
Vol 92 (1) ◽  
pp. 93-114 ◽  
Author(s):  
Anne M. Farrell ◽  
Jonathan H. Grenier ◽  
Justin Leiby

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.


2018 ◽  
Vol 33 (1) ◽  
pp. 113-128 ◽  
Author(s):  
Joel Owens ◽  
Erin M. Hawkins

ABSTRACT Recently, researchers have begun using online labor markets to recruit participants for experimental studies examining the judgments and decisions of nonprofessional investors. This study investigates the quality and generalizability of data collected from these sources by replicating an experimental task from Elliott, Hodge, Kennedy, and Pronk (2007) using nonprofessional investor participants from two popular online labor markets—Amazon's Mechanical Turk (MTurk) and Qualtrics Online Sample (Qualtrics). Compared to Qualtrics participants, we find that MTurk participants pay greater attention to the experimental materials and better acquire and recall information. Further, the MTurk sample more closely replicates EHKP's investment club member results on measures of information integration than does the Qualtrics sample. These results provide some evidence that many interesting research questions can be satisfactorily answered using nonprofessional investor participants from MTurk. We believe further investigation is needed before Qualtrics can be endorsed as a high-quality source of nonprofessional investor participants.


2012 ◽  
Vol 20 (3) ◽  
pp. 351-368 ◽  
Author(s):  
Adam J. Berinsky ◽  
Gregory A. Huber ◽  
Gabriel S. Lenz

We examine the trade-offs associated with using Amazon.com's Mechanical Turk (MTurk) interface for subject recruitment. We first describe MTurk and its promise as a vehicle for performing low-cost and easy-to-field experiments. We then assess the internal and external validity of experiments performed using MTurk, employing a framework that can be used to evaluate other subject pools. We first investigate the characteristics of samples drawn from the MTurk population. We show that respondents recruited in this manner are often more representative of the U.S. population than in-person convenience samples—the modal sample in published experimental political science—but less representative than subjects in Internet-based panels or national probability samples. Finally, we replicate important published experimental work using MTurk samples.


2016 ◽  
Author(s):  
Tomer Geva ◽  
Harel Lustiger ◽  
Maytal Saar-Tsechansky

2018 ◽  
Author(s):  
Arindrajit Dube ◽  
Jeff Jacobs ◽  
Suresh Naidu ◽  
Siddharth Suri

Author(s):  
Yili Hong ◽  
Jing Peng ◽  
Gordon Burtch ◽  
Ni Huang

This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exchange of online labor. We propose the effect of workers’ use of the direct messaging system on employers’ hiring decisions and conceptualize the information role of direct messaging. To empirically evaluate the information role of the direct messaging system, we leverage data on the direct messaging activities between workers and employers across more than 470,000 job applications on a leading online labor market. We report evidence that direct messaging with a prospective employer increases a worker’s probability of being hired by 8.9%. However, the degree to which workers benefit from direct messaging is heterogeneous, and the effect amplifies for workers approaching employers from a position of disadvantage (lacking tenure or fit with the job) and attenuates as more workers attempt to message the same prospective employer. The effects also depend on message content. In particular, we find that the benefits of direct messaging for workers depend a great deal on the politeness of the workers, and this “politeness effect” depends on several contextual factors. The beneficial effects are amplified for lower-status workers (i.e., workers lacking tenure and job fit) and workers who share a common language with the employer. At the same time, the beneficial effects weaken in the presence of typographical errors. These findings provide important insights into when and what to message to achieve favorable hiring outcomes in online employment settings.


2019 ◽  
Vol 15 (1) ◽  
pp. 90-116 ◽  
Author(s):  
Patrick S. Forscher ◽  
Nour S. Kteily

The 2016 U.S. presidential election coincided with the rise of the “alternative right,” or alt-right. Alt-right associates have wielded considerable influence on the current administration and on social discourse, but the movement’s loose organizational structure has led to disparate portrayals of its members’ psychology and made it difficult to decipher its aims and reach. To systematically explore the alt-right’s psychology, we recruited two U.S. samples: An exploratory sample through Amazon’s Mechanical Turk ( N = 827, alt-right n = 447) and a larger, nationally representative sample through the National Opinion Research Center’s Amerispeak panel ( N = 1,283, alt-right n = 71–160, depending on the definition). We estimate that 6% of the U.S. population and 10% of Trump voters identify as alt-right. Alt-right adherents reported a psychological profile more reflective of the desire for group-based dominance than economic anxiety. Although both the alt-right and non-alt-right Trump voters differed substantially from non-alt-right, non-Trump voters, the alt-right and Trump voters were quite similar, differing mainly in the alt-right’s especially high enthusiasm for Trump, suspicion of mainstream media, trust in alternative media, and desire for collective action on behalf of Whites. We argue for renewed consideration of overt forms of bias in contemporary intergroup research.


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