scholarly journals The Early Parenting Attitudes Questionnaire: Measuring Intuitive Theories of Parenting and Child Development

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Emily Hembacher ◽  
Michael C. Frank
Keyword(s):  
Child Development ◽  
Parenting Behaviors ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Parenting Attitudes ◽  
Early Environment ◽  
Early Parenting ◽  
Learning Factor ◽  
Exploratory Factor Analyses ◽  
Intuitive Theory

Parenting behaviors and decisions play an important role in determining children’s early environment. Are these behaviors driven by an intuitive theory of parenting – a coherent set of beliefs about child development and parent-child relationships? In exploratory work, we asked adults on Amazon Mechanical Turk to endorse a set of propositions about parenting and conducted exploratory factor analyses of their responses. Three distinct factors appeared in responses: an Affection and Attachment factor, an Early Learning factor, and a Rules and Respect factor. In an iterative process, we created a scale of items with subscales designed to measure these factors, which we call the Early Parenting Attitudes Questionnaire (EPAQ). We next conducted two studies with groups of parents (on Mechanical Turk and from the membership of a local museum) to estimate the validity of the new scale. We asked whether agreement with each subscale varied based on demographic factors and whether intuitive theories predicted self-reported parenting behaviors. The EPAQ provides an instrument to assess attitudes about parenting and child development, facilitating investigation of and intervention on parenting behaviors.

scholarly journals The Early Parenting Attitudes Questionnaire: Measuring Intuitive Theories of Parenting and Child Development

2018 ◽  
Author(s):  
Emily Hembacher ◽  
Michael C. Frank
Keyword(s):  
Child Development ◽  
Parenting Behaviors ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Parenting Attitudes ◽  
Early Environment ◽  
Early Parenting ◽  
Learning Factor ◽  
Exploratory Factor Analyses ◽  
Intuitive Theory

Parenting behaviors and decisions play an important role in determining children’s early environment. Are these behaviors driven by an intuitive theory of parenting – a coherent set of beliefs about child development and parent-child relationships? In exploratory work, we asked adults on Amazon Mechanical Turk to endorse a set of propositions about parenting and conducted exploratory factor analyses of their responses. Three distinct factors appeared in responses: an Affection and Attachment factor, an Early Learning factor, and a Rules and Respect factor. In an iterative process, we created a scale of items with subscales designed to measure these factors, which we call the Early Parenting Attitudes Questionnaire (EPAQ). We next conducted a series of studies with groups of parents (on Mechanical Turk and from the membership of a local museum) to estimate the validity of the new scale. We asked whether the predicted factor structure emerged from subsequent confirmatory factor analysis on a new sample, whether agreement with each subscale varied based on demographic factors, and whether intuitive theories predicted self-reported parenting behaviors. The present scale provides an instrument to assess attitudes about parenting and child development, facilitating of investigation and intervention on parenting behaviors.

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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