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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.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/use-of-hierarchical-linear-modeling-and-curriculum-based-measurement-for-assessing-academic-growth-and-instructional-factors-for-students-with-learning-difficulties/592087/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/use-of-hierarchical-linear-modeling-and-curriculum-based-measurement-for-assessing-academic-growth-and-instructional-factors-for-students-with-learning-difficulties/592087.png","ImageObject",300,407,{"name":92,"@type":93},"Grenda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of this paper?","Question",{"text":112,"@type":113},"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.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why is HLM considered useful for academic growth modeling?",{"text":117,"@type":113},"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.",{"name":119,"@type":110,"acceptedAnswer":120},"What role does CBM play in assessing students with learning difficulties?",{"text":121,"@type":113},"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.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},592087,1792190313,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},7971474921005,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Asia Pacific Education Review Copyright 2004 by Education Research Institute  \n2004, Vol. 5, No. 2, 136-148.  \nUse of Hierarchical Linear Modeling and Curriculum-Based Measurement for Assessing Academic Growth and Instructional Factors for Students with Learning Difficulties  \nJongho Shin Christine A. Espin Stanley L. Deno Scott McConnell  \nSeoul National University University of Minnesota University of Minnesota University of Minnesota Korea U. S.A U. S.A U. S.A  \nThe 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  \nKey Words: growth modeling, Hierarchical Linear Modeling (HLM), Curriculum Based Measurement (CBM)  \nA 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  \nJongho Shin, Assistant Professor at the Department of Education of the Seoul National University.  \nChristine 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).  \ntesting 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  \nfor multiple administrations within a one-year time frame.  \nOver 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  \nGrowth Modeling  \nassumed to be associated with st","cbCaidiUC8hI2QUz","https://ap.wps.com/l/cbCaidiUC8hI2QUz","pdf",127231,13,"English","# Introduction\n## Measuring Academic Growth for Students with Learning Difficulties\n## Curriculum-Based Measurement (CBM) and Repeated Measurement\n# Modeling Approach\n## Applying HLM to Multi-Wave CBM\n## Psychometric Features of CBM\n## Comparing Statistical Methods for Growth Assessment\n# Applications and Discussion\n## Assessing Effects of Student and Instruction Variables\n## Research Questions and Improving Instruction","[{\"question\":\"What is the main objective of this paper?\",\"answer\":\"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.\"},{\"question\":\"Why is HLM considered useful for academic growth modeling?\",\"answer\":\"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.\"},{\"question\":\"What role does CBM play in assessing students with learning difficulties?\",\"answer\":\"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.\"}]","Use of Hierarchical Linear Modeling and Curriculum-Based Measurement for Assessing Academic Growth and Instructional Factors for Students with Learning Difficulties | PDF",1792073832,33]