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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Pang, Joyce S., Ring, Hiram
مؤلفون آخرون: School of Social Sciences
التنسيق: مقال
اللغة:English
منشور في: 2022
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/154800
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص: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.