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

A Primer for Conducting Survey Research using MTurk

2016 ◽  
Vol 7 (2) ◽  
pp. 54-73 ◽  
Author(s):  
Silvana Chambers ◽  
Kim Nimon ◽  
Paula Anthony-McMann
Keyword(s):  
Best Practices ◽  
Survey Research ◽  
Survey Design ◽  
Design Guidelines ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Sample Survey ◽  
Quality Data ◽  
Trade Offs

This paper presents best practices for conducting survey research using Amazon Mechanical Turk (MTurk). Readers will learn the benefits, limitations, and trade-offs of using MTurk as compared to other recruitment services, including SurveyMonkey and Qualtrics. A synthesis of survey design guidelines along with a sample survey are presented to help researchers collect the best quality data. Techniques, including SPSS and R syntax, are provided that demonstrate how users can clean resulting data and identify valid responses for which workers could be paid.

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 Emotional Intelligence a Critical Factor in Organizational Performance

2020 ◽  
Vol 1 (4) ◽  
pp. 10-39
Author(s):  
Neeta Baporikar
Keyword(s):  
Data Collection ◽  
Best Practices ◽  
Organizational Performance ◽  
Case Study Research ◽  
Critical Factor ◽  
Individual Performance ◽  
Qualitative Approach ◽  
Research Paradigm ◽  
Interpretative Research

The objective of this study is to understand how employee EI is a critical factor for improved organizational performance. Adopting a qualitative approach with a case study research design, a sample of 40 employees was selected from the organization; data collection was done through a questionnaire after pilot testing. An analysis is done using SPSS, within an interpretative research paradigm using thematic analyses. Findings reflect that understanding and knowledge on EI and its influence on the performance may add value to the organization as employees become aware of the best practices and contribute better for organizational performance. Further, it was also noted that EI is a significant predictor of job and organizational performance only if it is advocated and spearheaded through individual performance classified into the task and contextual performance.

Best Practices

2013 ◽  
Author(s):  
Edward J. Mullen ◽  
Jennifer L. Bellamy ◽  
Sarah E. Bledsoe
Keyword(s):  
Quality Improvement ◽  
Social Work ◽  
Organizational Learning ◽  
Data Collection ◽  
Best Practices ◽  
Organizational Performance ◽  
Organizational Management ◽  
Quality Data ◽  
Management Literature ◽  
Systems Quality

This entry describes best practices as these are used in social work. The term best practices originated in the organizational management literature in the context of performance measurement and quality improvement where best practices are defined as the preferred technique or approach for achieving a valued outcome. Identification of best practices requires measurement, benchmarking, and identification of processes that result in better outcomes. The identification of best practices requires that organizations put in place quality data collection systems, quality improvement processes, and methods for analyzing and benchmarking pooled provider data. Through this process, organizational learning and organizational performance can be improved.

scholarly journals Is Ubiquitous Technology for Needs Data Management a Game Changer in Humanitarian Arena?

2019 ◽  
Vol 11 (1) ◽  
pp. 83-97
Author(s):  
Punya Prasad Sapkota ◽  
Kashif Siddiqi
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
Best Practices ◽  
Data Entry ◽  
Humanitarian Assistance ◽  
Dynamic Changes ◽  
The World ◽  
Collection And Management ◽  
Over Time ◽  
Game Changer

One in every 70 people around the world is caught up in a crisis (natural disasters, conflict, climate change, etc.) and urgently needs humanitarian assistance and protection according to the OCHA. The humanitarian community assists millions of people every year based on emerging humanitarian needs. Most of the time, the conditions inside the countries, once the humanitarian needs data is collected, are not very conducive and required simple ways to collect data like paper-based data collection with simple questions. This data is later entered into a database or spreadsheet using rigorous and time-consuming data entry efforts. Dynamic changes in needs of people; numbers of partners involved; the complexity of evolving processes; and emerging technologies over time has led to a change in processes for data collection and management. This article is an attempt to capture humanitarian data collection best practices and the use of different technologies in managing data to facilitate humanitarian needs assessment processes for the Syria crisis.

scholarly journals Where do our graduates go? A toolkit for retrospective and ongoing career outcomes data collection for biomedical PhD students and postdoctoral scholars

2019 ◽  
Author(s):  
Elizabeth A. Silva ◽  
Alicia B. Mejía ◽  
Elizabeth S. Watkins
Keyword(s):  
Data Collection ◽  
Best Practices ◽  
Graduate Education ◽  
Life Science ◽  
Retrospective Studies ◽  
Collection Management ◽  
Annual Data ◽  
Career Outcomes ◽  
Call To Action ◽  
Postdoctoral Training

Universities are at long last undertaking efforts to collect and disseminate information about student career outcomes, after decades of calls to action. Organizations such as Rescuing Biomedical Research and Future of Research brought this issue to the forefront of graduate education, and the second Future of Biomedical Graduate and Postdoctoral Training conference (FOBGAPT2) featured the collection of career outcomes data in its final recommendations, published in this journal (Hitchcock et al., 2017). More recently, 26 institutions assembled as the Coalition for Next Generation Life Science, committing to ongoing collection and dissemination of career data for both graduate and postdoc alumni. A few individual institutions have shared snapshots of the data in peer-reviewed publications (Mathur et al., 2018; Silva, des Jarlais, Lindstaedt, Rotman, Watkins, 2016) and on websites. As more and more institutions take up this call to action, they will now be looking for tools, protocols, and best practices for ongoing career outcomes data collection, management, and dissemination. Here, we describe UCSF's experiences in conducting a retrospective study, and in institutionalizing a methodology for annual data collection and dissemination. We describe and share all tools we have developed, and we provide calculations of the time and resources required to accomplish both retrospective studies and annual updates. We also include broader recommendations for implementation at your own institutions, increasing the feasibility of this endeavor.

scholarly journals Standards and Best Practices for Monitoring and Benchmarking Insects

2021 ◽  
Vol 8 ◽  
Author(s):  
Graham A. Montgomery ◽  
Michael W. Belitz ◽  
Rob P. Guralnick ◽  
Morgan W. Tingley
Keyword(s):  
Data Collection ◽  
Best Practices ◽  
National Parks ◽  
Light Trapping ◽  
Rapid Change ◽  
Monitoring Methods ◽  
Spatial And Temporal Scales ◽  
Ecological Responses ◽  
Insect Populations ◽  
Insect Monitoring

Benchmark studies of insect populations are increasingly relevant and needed amid accelerating concern about insect trends in the Anthropocene. The growing recognition that insect populations may be in decline has given rise to a renewed call for insect population monitoring by scientists, and a desire from the broader public to participate in insect surveys. However, due to the immense diversity of insects and a vast assortment of data collection methods, there is a general lack of standardization in insect monitoring methods, such that a sudden and unplanned expansion of data collection may fail to meet its ecological potential or conservation needs without a coordinated focus on standards and best practices. To begin to address this problem, we provide simple guidelines for maximizing return on proven inventory methods that will provide insect benchmarking data suitable for a variety of ecological responses, including occurrence and distribution, phenology, abundance and biomass, and diversity and species composition. To track these responses, we present seven primary insect sampling methods—malaise trapping, light trapping, pan trapping, pitfall trappings, beating sheets, acoustic monitoring, and active visual surveys—and recommend standards while highlighting examples of model programs. For each method, we discuss key topics such as recommended spatial and temporal scales of sampling, important metadata to track, and degree of replication needed to produce rigorous estimates of ecological responses. We additionally suggest protocols for scalable insect monitoring, from backyards to national parks. Overall, we aim to compile a resource that can be used by diverse individuals and organizations seeking to initiate or improve insect monitoring programs in this era of rapid change.

scholarly journals Narratives: fMRI data for evaluating models of naturalistic language comprehension

2020 ◽  
Author(s):  
Samuel A. Nastase ◽  
Yun-Fei Liu ◽  
Hanna Hillman ◽  
Asieh Zadbood ◽  
Liat Hasenfratz ◽  
...  
Keyword(s):  
Data Collection ◽  
Best Practices ◽  
Functional Mri ◽  
Language Comprehension ◽  
Human Subjects ◽  
Narrative Comprehension ◽  
Fmri Data ◽  
Word Level

AbstractThe “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.

Sign in / Sign up

Export Citation Format

Share Document