Quality rating and improvement systems: Secondary data analyses of psychometric properties of scale development [Executive summary]
The results of this secondary data analysis simulating a QRIS validation using six large early care and education datasets demonstrate several issues that should be considered when constructing, validating, and making changes to existing quality ratings. First, QRIS are developed from logic models that involve multiple outcome areas such as improving children's outcomes, professionalization of the workforce, family engagement, and ECE systems building. The analyses reported here suggest that separate QRIS rating scales will be needed for each of these dimensions unless they are highly correlated. Second, selection of the quality indicators should be based on the consistency and magnitude of effects in research literature. The QRIS rating is more likely to accurately measure quality when there is good evidence that we know how to measure the included quality indicators in a manner that predicts desired outcomes for the QRIS. Third, use of validated professional guidelines for defining the cut-points in the rating scales can maintain the information in the selected quality measures as they are converted into ratings to form the QRIS score. Results from this secondary data analysis suggest that a QRIS score reflecting classroom quality based on these principles predicts small but significant gains in children's academic outcomes. (author abstract)
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Quality rating and improvement systems: Secondary data analyses of psychometric properties of scale development
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