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Cited by 1,090

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Academic resilience and its psychological and educational correlates: A construct validity approach

Martin, Andrew J., Marsh, Herbert W.·2006·Psychology in the Schools

Abstract

This study examines educational and psychological correlates of academic resilience using within‐network and between‐network validity approaches. Based on a sample of 402 Australian high‐school students, a newly developed unidimensional academic resilience construct found within‐network validity by way of sound item and factor properties. In terms of between‐network validity, correlation, path analysis, and cluster analysis showed that five factors predict academic resilience: self‐efficacy, control, planning, low anxiety, and persistence. Hence, a 5‐C model of academic resilience is proposed: confidence (self‐efficacy), coordination (planning), control, composure (low anxiety), and commitment (persistence). Path analysis also showed that academic resilience subsequently predicts three educational and psychological “outcomes”: enjoyment of school, class participation, and general self‐esteem. © 2006 Wiley Periodicals, Inc. Psychol Schs 43: 267–281, 2006.

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Metadata

Authors
Martin, A. J., & Marsh, H. W.
Venue
Psychology in the Schools
Publisher
Wiley
Published
2006-02-10
Volume
43
Issue
3
Pages
267-281
ISSN
  • 0033-3085 (ISSN-L)
  • 1520-6807
OpenAlex
W2099952231
Cited by
1,090
References
9
FWCI
14.39
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Academic resilience and its psychological and educational correlates: A construct validity approach. https://app.cochat.ai/papers/10.1002/pits.20149
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Martin, A. J., & Marsh, H. W. (2006). Academic resilience and its psychological and educational correlates: A construct validity approach. Psychology in the Schools. https://doi.org/10.1002/pits.20149

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.