Use of Hierarchical Linear Modeling and Curriculum-Based Measurement for Assessing Academic Growth and Instructional Factors for Students with Learning Difficulties

Oct 15, 2026

Uploaded by Grenda

The paper demonstrates applying Hierarchical Linear Modeling (HLM) to multi-wave Curriculum-Based Measurement (CBM) to model academic growth and evaluate relations to student- and instruction-related variables. HLM is presented as advantageous for capturing growth rates using flexible designs and multiple data points, while CBM’s psychometric qualities support longitudinal assessment. The discussion emphasizes detecting small change, establishing validity and reliability, and using repeated measurement to guide instructional decisions and improve educational practices for students with learning difficulties.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Measuring Academic Growth for Students with Learning Difficulties

Curriculum-Based Measurement (CBM) and Repeated Measurement

Modeling Approach

Applying HLM to Multi-Wave CBM

Psychometric Features of CBM

Comparing Statistical Methods for Growth Assessment

Applications and Discussion

Assessing Effects of Student and Instruction Variables

Research Questions and Improving Instruction

Asia Pacific Education Review Copyright 2004 by Education Research Institute 2004, Vol. 5, No. 2, 136-148. Use of Hierarchical Linear Modeling and Curriculum-Based Measurement for Assessing Academic Growth and Instructional Factors for Students with Learning Difficulties Jongho Shin Christine A. Espin Stanley L. Deno Scott McConnell Seoul National University University of Minnesota University of Minnesota University of Minnesota Korea U. S.A U. S.A U. S.A The main purpose of this paper is to demonstrate how to apply the Hierarchical Linear Modeling (HLM) technique to multi-wave Curriculum-Based Measurement (CBM) measures in modeling academic growth and assessing its relations to student- and instruction-related variables. HLM has advantages over other statistical methods (e.g., repeated measures ANOVA, Structural Equation Modeling) in modeling academic growth. The advantages include allowing more flexible research designs in collecting multiple data points and estimating growth rates and their relations to correlates in more reliable, accurate ways. CBM, as a multi-wave progressmonitoring system, also has distinctive psychometric features that facilitate longitudinal research on academic skill development. These features include provision of multiple data points within short time periods, good validity and reliability, and sensitivity for detecting small degrees of change. Finally, research questions related to assessing the academic growth of students with learning difficulties and using assessment results to improve educational practices for them are discussed Key Words: growth modeling, Hierarchical Linear Modeling (HLM), Curriculum Based Measurement (CBM) A major goal of education has been to produce changes in the knowledge and skill levels of students. Although measuring change over time is important for all educators, it is especially important for teachers who are in charge of students with learning difficulties and must monitor individual students’ progress over time and evaluate the effects of instructional programs for those students. The academic growth of students with learning difficulties, unfortunately, is often so minimal as to go undetected by typical published, standardized achievement tests, especially over short periods of time. In addition, it is not easy to find Jongho Shin, Assistant Professor at the Department of Education of the Seoul National University. Christine Espin, Stanley Deno, and Scott McConnell, Professors atthe Department of Educational Psychology of the University of Minnesota. Correspondence concerning this article should be addressed to Jongho Shin, Department of Education, Seoul National University, San 56-1 Shinrim-Dong Kwanak-Gu, Seoul 151-748, Korea. Electronic mail may be sent [to jshin21@snu.ac.kr](to jshin21@snu.ac.kr). testing instruments that generate reliable and valid repeated measures of student performance over time, especially within a short time period (e.g., one year) . For example, published standardized achievement tests are designed to be given at approximately one-year time intervals; they are not designed for multiple administrations within a one-year time frame. Over the past 20 years, a system of measurement referred to as Curriculum-Based Measurement (CBM) has been developed that can be used to measure student performance reliably, validly, and repeatedly to represent academic growth over time (Deno, 1985; Good & Jefferson, 1998; Marston, 1989; Shin, Deno, & Espin, 2000) . CBM has been used primarily as a method for teachers to evaluate the progress of individual students over time so that teachers can evaluate the effectiveness of their instruction. However, CBM also has great potential for enabling researchers to examine the academic growth patterns and rates for groups of students, and further, to examine the relationship between student growth and relevant correlates (i.e., variables Growth Modeling assumed to be associated with st

FAQs

What is the main objective of this paper?

To show how to apply Hierarchical Linear Modeling (HLM) with multi-wave Curriculum-Based Measurement (CBM) for modeling academic growth and examining related student and instructional variables.

Why is HLM considered useful for academic growth modeling?

HLM is described as providing advantages over other methods by allowing flexible designs, estimating growth rates, and relating them to correlates using multiple data points more reliably and accurately.

What role does CBM play in assessing students with learning difficulties?

CBM is a multi-wave progress monitoring system with psychometric strengths, enabling repeated measurement that helps detect slow development and small degrees of change over time.

Oct 15, 2026

Uploaded by Grenda