Time-of-day and day-of-week variations in Amazon Mechanical Turk survey responses

2021 ◽  
pp. 103378
Author(s):  
Carola Conces Binder
Keyword(s):  
Time Of Day ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Survey Responses

scholarly journals Intertemporal Differences Among MTurk Workers

2016 ◽  
Author(s):  
Logan Samuel Casey ◽  
Jesse J. Chandler
Keyword(s):  
Serial Position ◽  
Time Of Day ◽  
Demographic Characteristics ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Psychological Processes ◽  
Original Dataset ◽  
Sample Composition ◽  
Growing Body ◽  
Online Labor

The online labor market Amazon Mechanical Turk (MTurk) is an increasinglypopular platform for generating samples of respondents for social scienceresearch. A growing body of research has examined the demographiccomposition of MTurk workers, typically comparing samples of MTurk workersto samples of respondents drawn from other populations. While thesecomparisons have revealed important information about the ways in whichMTurk workers are and are not representative of the general population,variations among samples drawn from MTurk have received less attention.This paper focuses on whether MTurk sample composition varies as a functionof time. Using an original dataset of nearly 10,000 MTurk workers, weexamine whether demographic characteristics vary by (1) time of day, (2)day of week, and (3) serial position (i.e., earlier or later in datacollection). We find that day of week differences are minimal, but thattime of day and serial position are associated with small but importantvariations in demographic composition, including characteristics known toimpact political attitudes and psychological processes. This demonstratesthat MTurk samples cannot be presumed identical across different studies,and we suggest several forms of variation to which researchers using MTurkought to be attentive.

scholarly journals Evaluating extinction, renewal, and resurgence of operant behavior in humans with Amazon Mechanical Turk

2021 ◽  
Vol 74 ◽  
pp. 101728
Author(s):  
Carolyn M. Ritchey ◽  
Toshikazu Kuroda ◽  
Jillian M. Rung ◽  
Christopher A. Podlesnik
Keyword(s):  
Operant Behavior ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk

scholarly journals Amazon Mechanical Turk: Gold Mine or Coal Mine?

2011 ◽  
Vol 37 (2) ◽  
pp. 413-420 ◽  
Author(s):  
Karën Fort ◽  
Gilles Adda ◽  
K. Bretonnel Cohen
Keyword(s):  
Coal Mine ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Gold Mine

scholarly journals Developing and validating an isotrigon texture discrimination task using Amazon Mechanical Turk

2015 ◽  
Vol 16 (S1) ◽  
Author(s):  
John WG Seamons ◽  
Marconi S Barbosa ◽  
Jonathan D Victor ◽  
Dominique Coy ◽  
Ted Maddess
Keyword(s):  
Discrimination Task ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Texture Discrimination

Real user evaluation of spoken dialogue systems using Amazon Mechanical Turk

2011 ◽  
Author(s):  
F. Jurčíček ◽  
S. Keizer ◽  
Milica Gašić ◽  
François Mairesse ◽  
B. Thomson ◽  
...  
Keyword(s):  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
User Evaluation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

Exploring Biases Between Human and Machine Generated Designs

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Christian E. Lopez ◽  
Scarlett R. Miller ◽  
Conrad S. Tucker
Keyword(s):  
Computer Simulation ◽  
Deep Learning ◽  
Domain Knowledge ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Design Tools ◽  
Generative Design ◽  
Generate Functional ◽  
Intended Function ◽  
2D Sketches

The objective of this work is to explore the possible biases that individuals may have toward the perceived functionality of machine generated designs, compared to human created designs. Toward this end, 1187 participants were recruited via Amazon mechanical Turk (AMT) to analyze the perceived functional characteristics of both human created two-dimensional (2D) sketches and sketches generated by a deep learning generative model. In addition, a computer simulation was used to test the capability of the sketched ideas to perform their intended function and explore the validity of participants' responses. The results reveal that both participants and computer simulation evaluations were in agreement, indicating that sketches generated via the deep generative design model were more likely to perform their intended function, compared to human created sketches used to train the model. The results also reveal that participants were subject to biases while evaluating the sketches, and their age and domain knowledge were positively correlated with their perceived functionality of sketches. The results provide evidence that supports the capabilities of deep learning generative design tools to generate functional ideas and their potential to assist designers in creative tasks such as ideation.

Consumer perception of portable ladder hazards and warnings

2018 ◽  
Vol 62 (1) ◽  
pp. 411-415
Author(s):  
Kenneth Nemire
Keyword(s):  
Significant Relationship ◽  
Online Survey ◽  
Consumer Perceptions ◽  
Consumer Perception ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Future Research ◽  
Warning Labels ◽  
Preliminary Assessment ◽  
Survey Tool

This article describes the results of a survey intended as a preliminary assessment of consumer perceptions of the hazardousness of portable ladders and the warning labels provided on portable ladders. One hundred ten participants responded to an online survey tool called Amazon Mechanical Turk. The survey collected information about participants’ use of ladders, their ratings of familiarity with ladders, perceived hazardousness of portable ladders, and perception of warning labels on portable ladders. Results indicated a small but significant relationship between familiarity with ladders and their perceived hazardousness, and that participants thought that people should be warned about the hazards associated with ladder use. Implications for future research about consumer perception of portable ladder hazards and warnings are described.

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