Modeling Motive Activation in the Operant Motive Test: A Psychometric Analysis Using Dynamic Thurstonian Item Response Theory

The Operant Motive Test (OMT) is a picture-based procedure that asks respondents to generate imaginative verbal behavior that is later coded for the presence of affiliation, power, and achievement-related motive content by trained coders. The OMT uses a larger number of pictures and asks respondents...

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Bibliographic Details
Published in:Motivation science 2016-12, Vol.2 (4), p.268-286
Main Authors: Runge, J. Malte, Lang, Jonas W. B, Engeser, Stefan, Schüler, Julia, den Hartog, Sophie C, Zettler, Ingo
Format: Article
Language:English
Subjects:
Female
Human
Item Response Theory
Male
Measurement
Motivation
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Summary:The Operant Motive Test (OMT) is a picture-based procedure that asks respondents to generate imaginative verbal behavior that is later coded for the presence of affiliation, power, and achievement-related motive content by trained coders. The OMT uses a larger number of pictures and asks respondents to provide more brief answers than earlier and more traditional picture-based implicit motive measures and has therefore become a frequently used measurement instrument in both research and practice. This article focuses on the psychometric response mechanism in the OMT and builds on recent advancements in the psychometric modeling of the response process in implicit motive measures through the use of Thurstonian item-response theory. The contribution of the article is twofold. First, the article builds on a recently developed dynamic Thurstonian model for more traditional implicit motive measures (Lang, 2014) and reports the first analysis of which we are aware that applies this model to OMT data (N = 633) and studies dynamic motive activation in the OMT. Results of this analysis yielded evidence for dynamic motive activation in the OMT and showed that simulated IRT reliabilities based on the dynamic model were .52, .62, and .73 for the affiliation, achievement, and power motive in the OMT, respectively. The second contribution of this article is a tutorial and R code that allows researchers to directly apply the dynamic Thurstonian IRT model to their data. The future use of the OMT in research and potential ways to improve the OMT are discussed.
ISSN:2333-8113
2333-8121
DOI:10.1037/mot0000041