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2021 | OriginalPaper | Buchkapitel

Predictive Power of School Motivation Clusters in Secondary Education

verfasst von : Matthijs J. Warrens, W. Miro Ebert

Erschienen in: Data Analysis and Rationality in a Complex World

Verlag: Springer International Publishing

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Abstract

In many applications of cluster analysis in educational research, the solutions found have very limited predictive power for relevant outcomes. In this paper, we explore whether the clusterings found have more predictive power (in terms of explained variance) if relevant outcomes are included in the estimation procedure, using a real-world data set on school motivation. We compare various normal mixture models with different distal outcomes involved such as no outcome variable, a single outcome, all outcomes. All models were estimated using the simultaneous estimation (one-step) procedure for distal outcomes in Latent GOLD. Partial eta squared (\(\eta ^2_p\)) was used to assess predictive power. Including relevant outcomes will in most cases increase the predictive power of the models. Furthermore, the increase in power is more substantial, in the absolute sense, when the correlation between the outcome variable and input variables is higher.

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Titel: Predictive Power of School Motivation Clusters in Secondary Education
verfasst von: Matthijs J. Warrens
W. Miro Ebert
Verlag: Springer International Publishing
Buch: Data Analysis and Rationality in a Complex World
Print ISBN: 978-3-030-60103-4

Electronic ISBN: 978-3-030-60104-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-60104-1_37

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