Data Collection in a Flat World: Accelerating Behavioral Research by Using Mechanical Turk

2010 ◽  
Author(s):  
Joseph K. Goodman ◽  
Cynthia E. Cryder ◽  
Amar Cheema
Keyword(s):  
Data Collection ◽  
Behavioral Research ◽  
Mechanical Turk

Effect of Incentive Amount on U.S. Adolescents’ Participation in an Accelerometer Data Collection Component of a National Survey

Field Methods ◽  
2021 ◽  
pp. 1525822X2198984
Author(s):  
April Y. Oh ◽  
Andrew Caporaso ◽  
Terisa Davis ◽  
Laura A. Dwyer ◽  
Linda C. Nebeling ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
National Survey ◽  
Monetary Incentive ◽  
Behavioral Research ◽  
Accelerometer Data ◽  
Web Based ◽  
Demographic Subgroups

Behavioral research increasingly uses accelerometers to provide objective estimates of physical activity. This study extends research on methods for collecting accelerometer data among youth by examining whether the amount of a monetary incentive affects enrollment and compliance in a mail-based accelerometer study of adolescents. We invited a subset of adolescents in a national web-based study to wear an accelerometer for seven days and return it by mail; participants received either $20 or $40 for participating. Enrollment did not significantly differ by incentive amount. However, adolescents receiving the $40 incentive had significantly higher compliance (accelerometer wear and return). This difference was largely consistent across demographic subgroups. Those in the $40 group also wore the accelerometer for more time than the $20 group on the first two days of the study. Compared to $20, a $40 incentive may promote youth completion of mail-based accelerometer studies.

scholarly journals Behavioral Correlates of the Muzak Experience

2021 ◽  
Author(s):  
Ellie Abrams ◽  
Pablo Ripolles ◽  
David Poeppel
Keyword(s):  
Data Collection ◽  
Computational Models ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Practical Significance ◽  
Music Listening ◽  
Crowd Sourcing ◽  
Behavioral Correlates ◽  
Potential Control ◽  
Musical Stimulus

The current work seeks to characterize a unique genre of music, elevator music (Muzak), using behavioral crowd-sourcing data from Amazon Mechanical Turk. Participants rated excerpts of elevator music along with more rewarding genres of music for pleasure, valence, familiarity, and recognition. Our results demonstrate that elevator music is rated as neutrally pleasurable, with high valence, and highly familiar. Data collection is ongoing, and future experiments use computational models of music to tease apart the neutral effects of elevator music listening. Our results have practical significance in that they may provide a potential control musical stimulus to be used with self-selected rewarding music.

scholarly journals Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research

PLoS ONE ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. e57410 ◽  
Author(s):  
Matthew J. C. Crump ◽  
John V. McDonnell ◽  
Todd M. Gureckis
Keyword(s):  
Behavioral Research ◽  
Mechanical Turk ◽  
Amazon's Mechanical Turk

scholarly journals Ok Google: Using virtual assistants for data collection in psychological and behavioral research

2021 ◽  
Author(s):  
Philipp Sprengholz ◽  
Cornelia Betsch
Keyword(s):  
Data Collection ◽  
Data Privacy ◽  
Reliability And Validity ◽  
Behavioral Research ◽  
Social Psychological ◽  
Online Questionnaire ◽  
Privacy Concerns ◽  
Wide Availability ◽  
Lower Education ◽  
The Many

AbstractBecause of the increasing popularity of voice-controlled virtual assistants, such as Amazon’s Alexa and Google Assistant, they should be considered a new medium for psychological and behavioral research. We developed Survey Mate, an extension of Google Assistant, and conducted two studies to analyze the reliability and validity of data collected through this medium. In the first study, we assessed validated procrastination and shyness scales as well as social desirability indicators for both the virtual assistant and an online questionnaire. The results revealed comparable internal consistency and construct and criterion validity. In the second study, five social psychological experiments, which have been successfully replicated by the Many Labs projects, were successfully reproduced using a virtual assistant for data collection. Comparable effects were observed for users of both smartphones and smart speakers. Our findings point to the applicability of virtual assistants in data collection independent of the device used. While we identify some limitations, including data privacy concerns and a tendency toward more socially desirable responses, we found that virtual assistants could allow the recruitment of participants who are hard to reach with established data collection techniques, such as people with visual impairment, dyslexia, or lower education. This new medium could also be suitable for recruiting samples from non-Western countries because of its wide availability and easily adaptable language settings. It could also support an increase in the generalizability of theories in the future.

Does hotel cleanliness correlate with surfaces guests contact?

2019 ◽  
Vol 31 (7) ◽  
pp. 2933-2950 ◽  
Author(s):  
Haeik Park ◽  
Sheryl Fried Kline ◽  
Jooho Kim ◽  
Barbara Almanza ◽  
Jing Ma
Keyword(s):  
Data Collection ◽  
Empirical Data ◽  
Design Methodology ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Data Sets ◽  
Holistic View ◽  
Content Type ◽  
Public Areas ◽  
Collection Methods

Purpose This study aims to strengthen implications about hotel cleaning outcomes by comparing guests’ perception of the amount of contact they have with cleanliness of hotel surfaces. Design/methodology/approach This study used two data-collection methods, a survey and an adenosine triphosphate (ATP) test. Data were collected from recent hotel guests using Amazon Mechanical Turk. Guests were asked to identify hotel surfaces that they touch most frequently. Actual hotel cleanliness was measured using empirical data collected with ATP meters. The two data sets were used to compare guests’ perceptions about the amount of contact they have with actual cleanliness measurements of those hotel surfaces. Findings This study found that amount of guest contact was related to cleanliness of surfaces in guestrooms. Significant differences were found in guest perception between high- and low-touch areas and between guestrooms and hotel public areas. More high-touch areas and higher ATP readings were found in guestrooms than in hotel public areas. Originality/value To the best of the authors’ knowledge this study is the first to compare guest contact with hotel surfaces to a scientific measure of hotel cleanliness. In addition, this study is unique because it assesses guest contact and cleanliness of public areas to provide a holistic view of hotel-cleaning needs. The study offers industry empirically based results from guest perception and scientifically based data that can be used to improve hotel housekeeping programs.

scholarly journals Mechanical Turk data collection in addiction research: utility, concerns and best practices

Addiction ◽  
2020 ◽  
Vol 115 (10) ◽  
pp. 1960-1968 ◽  
Author(s):  
Alexandra M. Mellis ◽  
Warren K. Bickel
Keyword(s):  
Data Collection ◽  
Best Practices ◽  
Mechanical Turk

Results from the Mechanical Turk Online Written Questionnaires

2019 ◽  
pp. 113-138
Author(s):  
James N. Stanford
Keyword(s):  
Data Collection ◽  
New England ◽  
Mechanical Turk ◽  
Free Response ◽  
Online Data ◽  
Regional Features ◽  
Current State ◽  
Phonological Features ◽  
Online Data Collection ◽  
Different Parts

This is the second of the two chapters (Chapters 4 and 5) that present the results of the author’s online data collection project using Mechanical Turk. This chapter analyzes the results of the online written questionnaires; 534 people responded to online questions about New England dialect features, including phonological features and lexical items. The author maps the results in terms of regional features in different parts of New England, comparing them to prior surveys and to the acoustic analyses of the prior chapter. The chapter also analyzes 100 free-response answers where New Englanders gave further insights into the current state of New England English.

scholarly journals Poverty in behavioral research: Different operationalizations, different results

2020 ◽  
Author(s):  
Matus Adamkovic ◽  
Marcel Martončik ◽  
Martin Lačný ◽  
Monika Kačmárová
Keyword(s):  
Social Sciences ◽  
Data Collection ◽  
Behavioral Research ◽  
Sensitivity Analyses ◽  
Outcome Variable ◽  
Complex Phenomenon ◽  
Objective Indicators ◽  
Substantial Heterogeneity

Poverty is a complex phenomenon involving objective as well as subjective aspects. In reality, 9 out of 10 flagship studies from social sciences assess only objective indicators reducing poverty's multidimensional nature into solely economic characteristics. Comparing the effects of several distinct poverty operationalizations on the same outcome variable, we found substantial heterogeneity in the estimates. Neglecting the fact that different poverty operationalizations produce different results can generate misleading narratives when interpreting the findings. A researcher should be well-aware which poverty operationalization is the most suitable for their research purposes prior to the data collection. In case this is hard to determine, we aim to encourage researchers to perform sensitivity analyses based on different poverty operationalizations in order to inspect how these choices shape their outcomes.

Data collection and measurement assessment in behavioral research: 1958–2013.

2016 ◽  
Vol 16 (1) ◽  
pp. 19-33 ◽  
Author(s):  
Douglas E. Kostewicz ◽  
Seth A. King ◽  
Shawn M. Datchuk ◽  
Kaitlyn M. Brennan ◽  
Sean D. Casey
Keyword(s):  
Data Collection ◽  
Behavioral Research
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