Using Student Scores on FAST PM1 Assessment to Estimate Student Performance on PM3 Assessment. Research Note. Volume 2501
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| Title: | Using Student Scores on FAST PM1 Assessment to Estimate Student Performance on PM3 Assessment. Research Note. Volume 2501 |
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| Language: | English |
| Authors: | Nastasia Schreiner, Aleksandr Shneyderman, Miami-Dade County Public Schools, Research Services |
| Source: | Office of Assessment, Research, and Data Analysis, Miami-Dade County Public Schools. 2025. |
| Availability: | Office of Assessment, Research, and Data Analysis, Miami-Dade County Public Schools. 1450 NE Second Avenue, Miami, FL 33132. Tel: 305-995-1000; Fax: 305-995-7521; Web site: https://arda.dadeschools.net/#!/fullWidth/2001 |
| Peer Reviewed: | N |
| Page Count: | 3 |
| Publication Date: | 2025 |
| Document Type: | Reports - Research |
| Education Level: | Elementary Education Secondary Education Elementary Secondary Education |
| Descriptors: | Scores, Standardized Tests, Achievement Tests, Progress Monitoring, Computation, Goodness of Fit, Predictive Validity, Predictor Variables, Academic Achievement, Elementary School Students, Secondary School Students |
| Geographic Terms: | Florida |
| Abstract: | It is possible to use a linear regression to estimate the prediction equation that would provide scale scores on PM3 from those on the PM1 assessment. Such a regression fits the data well with the correlation coefficients varying between 0.73 and 0.81 across all grade levels. On the other hand, estimated scale scores are more difficult to interpret and may provide less actionable information than the achievement levels. In addition, the results of the ordinary linear regression are subject to the regression toward the mean phenomenon, where the expected scores on PM3 for students who score either very low or very high on the PM1 assessment would gravitate toward the mean scale score for a given grade level. Because of these considerations, ordinal logistic regression was selected as an analytical tool to carry out the prediction. This brief explains how the results of the FAST PM1 and PM2 assessments can be used to provide FAST PM3 student performance estimates. Using scores from the same assessment to make such estimates may improve the predictive power and provide teachers and schools with actionable information to improve student performance. |
| Abstractor: | ERIC |
| Entry Date: | 2026 |
| Accession Number: | ED678711 |
| Database: | ERIC |
| Abstract: | It is possible to use a linear regression to estimate the prediction equation that would provide scale scores on PM3 from those on the PM1 assessment. Such a regression fits the data well with the correlation coefficients varying between 0.73 and 0.81 across all grade levels. On the other hand, estimated scale scores are more difficult to interpret and may provide less actionable information than the achievement levels. In addition, the results of the ordinary linear regression are subject to the regression toward the mean phenomenon, where the expected scores on PM3 for students who score either very low or very high on the PM1 assessment would gravitate toward the mean scale score for a given grade level. Because of these considerations, ordinal logistic regression was selected as an analytical tool to carry out the prediction. This brief explains how the results of the FAST PM1 and PM2 assessments can be used to provide FAST PM3 student performance estimates. Using scores from the same assessment to make such estimates may improve the predictive power and provide teachers and schools with actionable information to improve student performance. |
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