| Title: | Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model Versus Logistic Regression |
| Description: | The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by which students are identified as at-risk or not at-risk readers. Logistic regression and CART were compared using data on a sample of grades 1 and 2 Florida public school students who participated in both interim assessments and an end-of-the year summative assessment during the 2012/13 academic year. Grade-level analyses were conducted and comparisons between methods were based on traditional measures of diagnostic accuracy, including sensitivity (i.e., proportion of true positives), specificity (proportion of true negatives), positive and negative predictive power, and overall correct classification. Results indicate that CART is comparable to logistic regression, with the results of both methods yielding negative predictive power greater than the recommended standard of .90. Details of each method are provided to assist analysts interested in developing early warning systems using one of the methods. |
| Online Availability: |
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| Cover Date: | February 2015 |
| Web Release: | February 25, 2015 |
| Print Release: | February 25, 2015 |
| Publication #: | REL 2015077 General Ordering Information |
| Center/Program: | REL |
| Associated Centers: | NCEE |
| Authors: | Sharon Koon and Yaacov Petscher: Florida Center for Reading Research at the Florida State University |
| Type of Product: | Applied Research Methods |
| Keywords: |
Achievement (Student)
• reading
ReadingResearch Methodology Risk Statistical Standards and Methodology Students • at-risk
|
| Questions: |
For questions about the content of this Applied Research Methods, please contact: Amy Johnson. |