Automated coding of implicit motives : a machine-learning approach

Implicit motives are key drivers of individual differences but are time-consuming to assess, requiring many hours of work by trained human coders. In this paper we report on the use of machine learning to automate the coding of implicit motives. We assess the performance of three neural network mode...

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Main Authors:	Pang, Joyce S., Ring, Hiram
Other Authors:	School of Social Sciences
Format:	Article
Language:	English
Published:	2022
Subjects:	Social sciences::Psychology Implicit Motive Assessment Picture Story Exercise
Online Access:	https://hdl.handle.net/10356/154800
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1548002022-01-10T01:42:41Z Automated coding of implicit motives : a machine-learning approach Pang, Joyce S. Ring, Hiram School of Social Sciences Social sciences::Psychology Implicit Motive Assessment Picture Story Exercise Implicit motives are key drivers of individual differences but are time-consuming to assess, requiring many hours of work by trained human coders. In this paper we report on the use of machine learning to automate the coding of implicit motives. We assess the performance of three neural network models on three unseen datasets in order to establish baselines for convergent, divergent, causal, and criterion validity. Results suggest that this is a promising direction to pursue in developing an automatic procedure for coding implicit motives. 2022-01-10T01:42:41Z 2022-01-10T01:42:41Z 2020 Journal Article Pang, J. S. & Ring, H. (2020). Automated coding of implicit motives : a machine-learning approach. Motivation and Emotion, 44(4), 549-566. https://dx.doi.org/10.1007/s11031-020-09832-8 0146-7239 https://hdl.handle.net/10356/154800 10.1007/s11031-020-09832-8 2-s2.0-85084668867 4 44 549 566 en Motivation and Emotion © Springer Science+Business Media, LLC, part of Springer Nature 2020
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Social sciences::Psychology Implicit Motive Assessment Picture Story Exercise
spellingShingle	Social sciences::Psychology Implicit Motive Assessment Picture Story Exercise Pang, Joyce S. Ring, Hiram Automated coding of implicit motives : a machine-learning approach
description	Implicit motives are key drivers of individual differences but are time-consuming to assess, requiring many hours of work by trained human coders. In this paper we report on the use of machine learning to automate the coding of implicit motives. We assess the performance of three neural network models on three unseen datasets in order to establish baselines for convergent, divergent, causal, and criterion validity. Results suggest that this is a promising direction to pursue in developing an automatic procedure for coding implicit motives.
author2	School of Social Sciences
author_facet	School of Social Sciences Pang, Joyce S. Ring, Hiram
format	Article
author	Pang, Joyce S. Ring, Hiram
author_sort	Pang, Joyce S.
title	Automated coding of implicit motives : a machine-learning approach
title_short	Automated coding of implicit motives : a machine-learning approach
title_full	Automated coding of implicit motives : a machine-learning approach
title_fullStr	Automated coding of implicit motives : a machine-learning approach
title_full_unstemmed	Automated coding of implicit motives : a machine-learning approach
title_sort	automated coding of implicit motives : a machine-learning approach
publishDate	2022
url	https://hdl.handle.net/10356/154800
_version_	1722355394463924224

Automated coding of implicit motives : a machine-learning approach

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