A Meta-Analysis of the Relations Between Curriculum-Based Measures in Mathematics and Criterion Measures

Abstract

This meta-analysis examined the validity of curriculum-based measures in mathematics (CBM-M) in relation to mathematics outcomes. Peer reviewed journal articles or dissertations were included if participants were in Grades 2 to 8 and the study purpose was examining the criterion validity of MCOMP and MCAP. Studies were identified via electronic databases, table of contents review, and reference and forward citation searches. Twenty-nine studies with 27,907 participants met inclusion criteria. A random-effects three-level meta-analysis of 328 correlations was conducted to estimate the average correlations between scores on the CBM-M tasks and criterion measures and to analyze the effects of potential moderating variables. The average correlation between CBM-M and criterion measures was r =.584 [95% CI: 533, .635]. Both MCAP (r =.654 [95% CI: .599, .703]) and MCOMP (r =.528 [95% CI: .462, .590]) scores were strongly correlated with criterion measures when all grades were included. Moderator analyses were conducted for each CBM-M task, yielding similar findings. Average correlations were stronger for middle compared to elementary grades and for concurrent administration of CBM-M and criterion measures. No moderating effects were found for criterion, CBM-M developer, or time of year the CBM-M task was administered. No evidence of publication bias was found.

Impact Statement

This meta-analysis indicates that MCOMP and MCAP are strong predictors of mathematics achievement and thus useful for universal screening. Stronger correlations were found for middle school students as well as when the CBM-M task and the criterion measure were administered within one month or less of one another.

Keywords:

• Curriculum-based measurement
• math
• computation
• application
• screenings

ASSOCIATE EDITOR:

• Stacy-Ann A. January

Open Scholarship

This article has earned the Center for Open Science badge for Open Materials. The materials are openly accessible at https://osf.io/he7xk/.

Additional information

Notes on contributors

Robin S. Codding

Robin S. Codding, PhD, is a Professor of School Psychology in the Applied Psychology Department at Northeastern University. Her research interests include using assessment data to make instructional decisions, comparing and combining academic intervention strategies and tactics to support student learning, and supporting implementation of school-based assessment and intervention to promote the use of multitiered systems of support, particularly in the area of math.

Gena Nelson

Gena Nelson is an Assistant Research Professor at the University of Oregon in the Center on Teaching and Learning. Her research interests include research synthesis, early mathematics learning, and learning disabilities and mathematics difficulties

Allyson J. Kiss

Allyson J. Kiss, PhD, is a School Psychologist at Anchorage School District (ASD). Her research interests include the efficient use of assessment data to inform learning within a multitiered system of support, identification of students with mathematics difficulties and understanding of how attitudes toward math and math anxiety relate to academic performance. This research was conducted independently of the ASD.

Jaehyun Shin

Jaehyun Shin, PhD, is an Associate Professor in the department of Special (Inclusive) Education at Gyeongin National University of Education, Korea. His research focuses on seeking the best way of identifying and intervening students with severe academic difficulties or disabilities, including the technical adequacy of screening and diagnosis measures, research-based instruction, and supporting teachers’ use of data-based instruction (DBI) to improve students’ academic skills.

Abigail Goodridge

Abigail Goodridge, MS/CAGS, is a doctoral candidate in the school psychology program at Northeastern University. Her research focuses on integrating social-emotional learning strategies and tactics with math interventions to minimize math anxiety and enhance students’ self-efficacy.

Jiyung Hwang

Jiyung Hwang, PhD, is an Assistant Professor of Education in the School of Education at Drake University. The primary focus of her research areas includes evidence-based instructional practices for students with learning disabilities, integrated academic and behavioral intervention within multitiered systems of support, and professional development and instructional coaching for both general and special education teachers in inclusive settings.