Early identification of children at risk for academic difficulties using standardized assessment: stability and predictive validity of preschool math and language scores

Niek FransFaculty of Behavioral and Social Sciences, Department of Special Needs Education and Youth Care, University of Groningen, Groningen, the NetherlandsCorrespondence[email protected]
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Wendy J. PostFaculty of Behavioral and Social Sciences, Department of Special Needs Education and Youth Care, University of Groningen, Groningen, the NetherlandsView further author information

Mark HuismanFaculty of Behavioral and Social Sciences, Department of Sociology, University of Groningen, Groningen, the NetherlandsView further author information

Ineke C. E. Oenema-MostertFaculty of Behavioral and Social Sciences, Department of Special Needs Education and Youth Care, University of Groningen, Groningen, the Netherlands;School of Education, Stenden University of Applied Sciences, Leeuwarden, the NetherlandsView further author information

Anne L. KeegstraOtolaryngology Department, University Medical Center Groningen, Groningen, the NetherlandsView further author information

Alexander E. M. G. MinnaertFaculty of Behavioral and Social Sciences, Department of Special Needs Education and Youth Care, University of Groningen, Groningen, the NetherlandsView further author information

Early identification of children at risk for academic difficulties using standardized assessment: stability and predictive validity of preschool math and language scores

Table 1. Sample demographics.

Table 2. Test score descriptives per measurement occasion.

Table 3. Conditions used to cluster children that received at least one ≤25th percentile score on the language (n = 143) or math (n = 101) tests, and percentages in each group.

Table 4. Estimated transition rates and standard errors for language and mathematics.

Table 5. Fixed effects and standard errors for multilevel models on imputed data.

Table 6. Observed correlations of language scores (pairwise deletion, above diagonal) and MI estimated language correlations from multilevel model (below diagonal).

Table 7. Observed correlations of math scores (pairwise deletion, above diagonal) and MI estimated mathematics correlations from multilevel model (below diagonal).

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Early identification of children at risk for academic difficulties using standardized assessment: stability and predictive validity of preschool math and language scores

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Table 1. Sample demographics.

Table 2. Test score descriptives per measurement occasion.

Table 3. Conditions used to cluster children that received at least one ≤25th percentile score on the language (n = 143) or math (n = 101) tests, and percentages in each group.

Table 4. Estimated transition rates and standard errors for language and mathematics.

Table 5. Fixed effects and standard errors for multilevel models on imputed data.

Table 6. Observed correlations of language scores (pairwise deletion, above diagonal) and MI estimated language correlations from multilevel model (below diagonal).

Table 7. Observed correlations of math scores (pairwise deletion, above diagonal) and MI estimated mathematics correlations from multilevel model (below diagonal).

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