Predictive power of school motivation clusters in secondary education

Matthijs J. Warrens*, W. Miro Ebert

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)

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 (휂2푝) 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.
Original languageEnglish
Title of host publicationData Analysis and Rationality in a Complex World
EditorsTheodore Chadjipadelis, Berthold Lausen, Angelos Markos, Tae Rim Lee, Angela Montanari, Rebecca Nugent
PublisherSpringer
Pages341-349
Number of pages9
ISBN (Electronic)978-3-030-60104-1
ISBN (Print)978-3-030-60103-4
DOIs
Publication statusPublished - 2021

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
PublisherSpringer
ISSN (Print)1431-8814

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