Skip to main content

Breadcrumb

Home arrow_forward_ios Information on IES-Funded Research arrow_forward_ios Investigating the Technical Adequac ...
Home arrow_forward_ios ... arrow_forward_ios Investigating the Technical Adequac ...
Information on IES-Funded Research
Grant Closed

Investigating the Technical Adequacy of Progress Monitoring Measures for Kindergarten Students at Risk for Reading Disabilities

NCSER
Program: Special Education Research Grants
Program topic(s): Reading, Writing, and Language
Award amount: $1,599,401
Principal investigator: Nathan Clemens
Awardee:
Texas A&M University
Year: 2013
Award period: 3 years 11 months (07/01/2013 - 06/30/2017)
Project type:
Measurement
Award number: R324A150270

Purpose

The research team proposed to investigate the technical adequacy of six existing early literacy measures and validate their use for monitoring the reading progress of kindergarten students at risk for reading disabilities. Progress monitoring is recommended for students who are considered at risk for reading disabilities. While progress monitoring measures are readily available and frequently used in early elementary school, little research has been conducted on which reading monitoring measures are most appropriate for use in kindergarten and there is little information about their psychometric properties. This project aimed to identify the progress monitoring measures that are the most reliable, sensitive to growth, valid, and feasible for monitoring reading progress.

Project Activities

This project planned to compare results from six reading progress monitoring measures. Students in the first two cohorts were to be assessed in kindergarten and followed through 2nd grade to examine the predictive validity of the progress monitoring measures for later reading achievement. A third cohort was to be assessed only in kindergarten to investigate the practical implementation of the measures and their acceptability and ease of interpretation by teachers and school staff.  The research team proposed to determine the technical adequacy of each progress monitoring measure, including reliability of estimated trend lines, concurrent and predictive validity, and validity of slope estimates; whether there were differences in the technical adequacy for students who are English learners compared to those who are not; and which progress measures best reflect students' reading skills and are easiest to administer and interpret.

Structured Abstract

Setting

Data collection will occur in kindergarten classrooms from urban, suburban, and rural school districts in Texas.

Sample

Approximately 500 kindergarteners at risk for reading disabilities will participate in this research. Students identified as at risk on the school-administered measure in the fall of kindergarten will be given a brief battery of standardized measures of phonological awareness, rapid automatic naming, and letter identification skills. Students scoring within at-risk levels on the school-administered screener and below the 30th percentile compared to national norms on the standardized, researcher-administered assessments will be enrolled in the study. At least 30 percent of the sample will be English learners.

Assessment

The project will compare six commonly used measures that have been recommended for monitoring reading progress in kindergarten: (1) Letter Name Fluency measures of the EasyCBM system; (2) Letter Sound Fluency of EasyCBM; (3) Phoneme Segmentation Fluency measures of EasyCBM to measure sensitivity to the sound structure of language and ability to identify, segment, and manipulate sounds; (4) Nonsense Word Fluency of the Dynamic Indicators of Basic Early Literacy Skills to measure letter-sound correspondence and decoding skills; (5) Word Reading Fluency of the EasyCBM; and (6) STAR Early Literacy, which measures a variety of early literacy skills using computer-adaptive technology.

Research design and methods

This project will compare results from different reading progress monitoring measures with the same sample of students at risk for reading disabilities. Students will be recruited in three cohorts of approximately 175 students per cohort. Students in cohorts 1 and 2 will be assessed in kindergarten and followed through 2nd grade to examine the predictive validity of the progress monitoring measures for later reading achievement. Cohort 3 will be assessed only in kindergarten to investigate the practical implementation of the measures and their acceptability and ease of interpretation by school staff. The Letter Name Fluency, Letter Sound Fluency, and Phoneme Segmentation Fluency progress monitoring measures will be administered every 2 weeks in the fall and spring semesters of kindergarten while the Nonsense Word Fluency and Word Reading Fluency progress monitoring measures will be administered every 2 weeks in the spring semester. The STAR Early Literacy measures will be administered four times across the kindergarten school year. An additional battery of standardized measures of reading and early literacy skills will also be administered in the fall, winter, and spring of kindergarten as well as the fall and spring of 1st and 2nd grades for cohorts 1 and 2.

Control condition

Due to the nature of the research design, there is no control condition.

Key measures

In addition to the measures listed above, a series of measures will be administered to assess students' reading skills at various points in time across the project period to allow for analyses of reading outcomes and long-term predictive validity of the individual progress monitoring measures. The measures include Woodcock Reading Mastery Test-III Phonological Awareness, Rapid Automatic Naming, Letter Identification, Word Identification, Word Attack, and Passage Comprehension subtests; Test of Word Reading Efficiency-2 Sight Word Efficiency and Phonemic Decoding Efficiency subtests; Reading-CBM/Oral Reading Fluency from the EasyCBM system; Test of Silent Reading Efficiency and Comprehension; and Gates-MacGinitie Reading Test-4th Edition Reading Comprehension subtest. In addition, teachers will be surveyed on each progress monitoring measure for its ease of administration, ease in interpreting the data, and overall utility for informing instruction.

Data analytic strategy

Latent growth curve analyses will be used to investigate the technical adequacy of each progress monitoring measure, including the reliability of the estimated trend lines, concurrent and predictive validity, and validity of slope estimates. In addition, the team will use the models to investigate whether there are differences in the technical adequacies of these measures for students who are English learners compared to those who are not. Finally, they will use multi-level models to analyze teacher ratings to determine which progress measures are easiest to administer and interpret and best reflect students' reading skills.

People and institutions involved

IES program contact(s)

Sarah Brasiel

Education Research Analyst
NCSER

Products and publications

ERIC Citations: Find available citations in ERIC for this award here and here.

Additional project information

Previous award details:

Previous award number:
R324A130214
Previous awardee:
Texas A&M University

Supplemental information

Co-Principal Investigators: Hagan-Burke, Shanna; Kwok, Oi-man; Al Otaiba, Stephanie

Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

 

Tags

Data and Assessments

Share

Icon to link to Facebook social media siteIcon to link to X social media siteIcon to link to LinkedIn social media siteIcon to copy link value

Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

 

You may also like

Zoomed in IES logo
Workshop/Training

Data Science Methods for Digital Learning Platform...

August 18, 2025
Read More
Zoomed in IES logo
Workshop/Training

Meta-Analysis Training Institute (MATI)

July 28, 2025
Read More
Zoomed in Yellow IES Logo
Workshop/Training

Bayesian Longitudinal Data Modeling in Education S...

July 21, 2025
Read More
icon-dot-govicon-https icon-quote