Identifying high impact school improvements using conditional mean independent correlations and growth functions

Nicolette van Halem*, Ilja Cornelisz, Alan Daly, Chris van Klaveren

*Corresponding author for this work

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1 Citation (Scopus)
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In educational contexts where many domains subject to improvement are interdependent and causal evidence is frequently lacking it is difficult, if not impossible, for policymakers and educational practitioners to decide which domain should be invested in. This paper proposes a new method that uses Conditional Mean Independent Correlations (CMIC) and normative growth functions to inform such decision-making processes. In this paper, CMIC and growth functions are applied to data from a research-practice partnership to identify high impact improvements among domains that are considered important to the district’s mission and vision around student learning. The results point to improvement domains that administrators did not consider to be high impact improvements initially, suggesting that this method brings leaders food for thought around strategies for improvement efforts. The CMIC and growth functions moreover accommodate opportunities for policymakers and practitioners to base their decisions on theory and data, providing them with a stronger degree of decision-making authority for use of resources for improvement. Simultaneously, CMIC and growth functions enable researchers to test and further develop theoretical models on improvement efforts. Limitations and suggestions for further research are discussed.

Original languageEnglish
Pages (from-to)211-228
Number of pages18
JournalInternational Journal of Research and Method in Education
Issue number2
Publication statusE-pub ahead of print - 27 Jul 2023
Externally publishedYes

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Publisher Copyright:
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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