NotesFAQContact Us
Collection
Advanced
Search Tips
Publication Date
In 20250
Since 20240
Since 2021 (last 5 years)0
Since 2016 (last 10 years)2
Since 2006 (last 20 years)8
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing all 11 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Petscher, Yaacov; Koon, Sharon – Assessment for Effective Intervention, 2020
The assessment of screening accuracy and setting of cut points for a universal screener have traditionally been evaluated using logistic regression analysis. This analytic technique has been frequently used to evaluate the trade-offs in correct classification with misidentification of individuals who are at risk of performing poorly on a later…
Descriptors: Screening Tests, Accuracy, Regression (Statistics), Classification
Phelps, Richard P. – Online Submission, 2020
This review critiques the highly-praised and influential 2001 study, "Getting Tough? The Impact of High School Graduation Exams," which concluded that "minimum competency," or high school "graduation exams," had no effect on student achievement. The review compares the test classifications of "Getting…
Descriptors: High School Students, Exit Examinations, Academic Achievement, Minimum Competencies
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Koon, Sharon; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2015
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…
Descriptors: Classification, Regression (Statistics), Models, At Risk Students
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Koon, Sharon; Petscher, Yaacov; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
This study examines whether the classification and regression tree (CART) model improves the early identification of students at risk for reading comprehension difficulties compared with the more difficult to interpret logistic regression model. CART is a type of predictive modeling that relies on nonparametric techniques. It presents results in…
Descriptors: At Risk Students, Reading Difficulties, Identification, Reading Comprehension
Peer reviewed Peer reviewed
Direct linkDirect link
Johnson, Evelyn S.; Jenkins, Joseph R.; Petscher, Yaacov – Assessment for Effective Intervention, 2010
In a response-to-intervention framework, schools typically employ a direct route approach to screening, in which students identified as at risk by a screening process are directly placed into intervention. Direct route approaches require screening decisions to be highly accurate, but few studies examining the predictive validity of reading…
Descriptors: Screening Tests, Reading Tests, At Risk Students, Predictive Validity
Guam Department of Education, 2013
This report addresses the reporting requirements of Public Law 26-26 and the provisions of "No Child Left Behind" ("NCLB") as described in the Guam Department of Education Board adopted "District Action Plan" ("DAP"). Public Law 26-26, Section 3106 states that "No later than thirty (30) days following…
Descriptors: Public Education, Student Characteristics, Academic Achievement, Graduation Rate
Peer reviewed Peer reviewed
Direct linkDirect link
VanDerHeyden, Amanda M.; Burns, Matthew K. – Assessment for Effective Intervention, 2008
This article investigates the utility of various estimates of mathematics proficiency. The participants were 432 students in Grades 2 through 5. The delayed alternate form reliability of multiskill probes, retention probes, slopes of student growth, and trials to criterion were computed. The fluency probes were found to be both sufficiently…
Descriptors: Grades (Scholastic), Scores, Grade 5, Grade 2
Wang, Jia; Wang, Haiwen – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2007
This study evaluates a large urban district's standards-based promotion policy decisions against a model-driven classification. Hierarchical logistic regression was used to explore factors related to grade retention at both the student and school level. Statistical results indicate that using students' next year achievement test scores as…
Descriptors: Grade Repetition, Urban Schools, Board of Education Policy, Decision Making
Schmidt, William H. – 1978
A taxonomy was developed for classifying instructional materials in fourth grade mathematics, as well as the content of actual classroom instruction and of achievement tests. The taxonomy is defined by the intersection of three factors and results in 468 cells: (1) mode of presentation (for example, use of graphs, tables, story problems)--3…
Descriptors: Achievement Tests, Classification, Content Analysis, Curriculum Evaluation
Freeman, Donald; And Others – 1979
To help teachers in comparing the content of their instruction with the content of standardized tests, a taxonomy of elementary school mathematics was developed. The taxonomy consisted of matrix with three dimensions; (1) mode of presentation (how questions are asked); (2) nature of material (type of numbers or mathematical terms); and (3)…
Descriptors: Achievement Tests, Classification, Content Analysis, Curriculum Development
Rubin, Rosalyn A.; And Others – 1976
Relationships between performance on two preschool administrations of the Metropolitan Readiness Tests (MRT) and Stanford Achievement Test (SAT) scores at age nine were examined for a sample of 732 children. Significant correlations, ranging from .50 to .71, were obtained between readiness scores and SAT scores on reading, spelling and arithmetic.…
Descriptors: Academic Achievement, Achievement Tests, Classification, Correlation