Publication Date
In 2025 | 1 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 5 |
Since 2016 (last 10 years) | 9 |
Since 2006 (last 20 years) | 12 |
Descriptor
Error of Measurement | 12 |
Hierarchical Linear Modeling | 12 |
Scores | 12 |
Middle School Students | 6 |
Pretests Posttests | 5 |
Academic Achievement | 4 |
Computation | 4 |
Elementary School Students | 4 |
Measurement Techniques | 4 |
Reliability | 4 |
Statistical Analysis | 4 |
More ▼ |
Source
Author
Cho, Sun-Joo | 3 |
Bottge, Brian A. | 2 |
Preacher, Kristopher J. | 2 |
Alexander Robitzsch | 1 |
Artelt, Cordula | 1 |
Brunner, Martin | 1 |
Curtis, David D. | 1 |
Forrow, Lauren | 1 |
Gill, Brian | 1 |
Ho, Andrew D. | 1 |
Kalogrides, Demetra | 1 |
More ▼ |
Publication Type
Reports - Research | 9 |
Journal Articles | 7 |
Reports - Descriptive | 2 |
Dissertations/Theses -… | 1 |
Education Level
Middle Schools | 5 |
Elementary Education | 4 |
Secondary Education | 4 |
High Schools | 3 |
Junior High Schools | 3 |
Elementary Secondary Education | 1 |
Grade 8 | 1 |
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
Pennsylvania | 3 |
China | 1 |
Germany | 1 |
Iran | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Motivated Strategies for… | 1 |
National Assessment of… | 1 |
Trends in International… | 1 |
What Works Clearinghouse Rating
Oliver Lüdtke; Alexander Robitzsch – Journal of Experimental Education, 2025
There is a longstanding debate on whether the analysis of covariance (ANCOVA) or the change score approach is more appropriate when analyzing non-experimental longitudinal data. In this article, we use a structural modeling perspective to clarify that the ANCOVA approach is based on the assumption that all relevant covariates are measured (i.e.,…
Descriptors: Statistical Analysis, Longitudinal Studies, Error of Measurement, Hierarchical Linear Modeling
Reardon, Sean F.; Ho, Andrew D.; Kalogrides, Demetra – Stanford Center for Education Policy Analysis, 2019
Linking score scales across different tests is considered speculative and fraught, even at the aggregate level (Feuer et al., 1999; Thissen, 2007). We introduce and illustrate validation methods for aggregate linkages, using the challenge of linking U.S. school district average test scores across states as a motivating example. We show that…
Descriptors: Test Validity, Evaluation Methods, School Districts, Scores
Stallasch, Sophie E.; Lüdtke, Oliver; Artelt, Cordula; Brunner, Martin – Journal of Research on Educational Effectiveness, 2021
To plan cluster-randomized trials with sufficient statistical power to detect intervention effects on student achievement, researchers need multilevel design parameters, including measures of between-classroom and between-school differences and the amounts of variance explained by covariates at the student, classroom, and school level. Previous…
Descriptors: Foreign Countries, Randomized Controlled Trials, Intervention, Educational Research
Forrow, Lauren; Starling, Jennifer; Gill, Brian – Regional Educational Laboratory Mid-Atlantic, 2023
The Every Student Succeeds Act requires states to identify schools with low-performing student subgroups for Targeted Support and Improvement or Additional Targeted Support and Improvement. Random differences between students' true abilities and their test scores, also called measurement error, reduce the statistical reliability of the performance…
Descriptors: At Risk Students, Low Achievement, Error of Measurement, Measurement Techniques
Regional Educational Laboratory Mid-Atlantic, 2023
This Snapshot highlights key findings from a study that used Bayesian stabilization to improve the reliability (long-term stability) of subgroup proficiency measures that the Pennsylvania Department of Education (PDE) uses to identify schools for Targeted Support and Improvement (TSI) or Additional Targeted Support and Improvement (ATSI). The…
Descriptors: At Risk Students, Low Achievement, Error of Measurement, Measurement Techniques
Regional Educational Laboratory Mid-Atlantic, 2023
The "Stabilizing Subgroup Proficiency Results to Improve the Identification of Low-Performing Schools" study used Bayesian stabilization to improve the reliability (long-term stability) of subgroup proficiency measures that the Pennsylvania Department of Education (PDE) uses to identify schools for Targeted Support and Improvement (TSI)…
Descriptors: At Risk Students, Low Achievement, Error of Measurement, Measurement Techniques
Lee, HyeSun – Applied Measurement in Education, 2018
The current simulation study examined the effects of Item Parameter Drift (IPD) occurring in a short scale on parameter estimates in multilevel models where scores from a scale were employed as a time-varying predictor to account for outcome scores. Five factors, including three decisions about IPD, were considered for simulation conditions. It…
Descriptors: Test Items, Hierarchical Linear Modeling, Predictor Variables, Scores
Cho, Sun-Joo; Preacher, Kristopher J. – Educational and Psychological Measurement, 2016
Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…
Descriptors: Error of Measurement, Error Correction, Multivariate Analysis, Hierarchical Linear Modeling
Cho, Sun-Joo; Bottge, Brian A. – Grantee Submission, 2015
In a pretest-posttest cluster-randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre-test scores and other related covariates while estimating an intervention effect at post-test. In many applications in education, the total post-test and pre-test scores that ignores measurement error in the…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Pretests Posttests, Scores
Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A. – Grantee Submission, 2015
Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test…
Descriptors: Structural Equation Models, Hierarchical Linear Modeling, Intervention, Program Effectiveness
Quesen, Sarah – ProQuest LLC, 2016
When studying differential item functioning (DIF) with students with disabilities (SWD) focal groups typically suffer from small sample size, whereas the reference group population is usually large. This makes it possible for a researcher to select a sample from the reference population to be similar to the focal group on the ability scale. Doing…
Descriptors: Test Items, Academic Accommodations (Disabilities), Testing Accommodations, Disabilities
Wang, Lihui; Lawson, Michael J.; Curtis, David D. – Language Teaching Research, 2015
Imagery training has been shown to improve reading comprehension. Recent research has also shown that the quality of visual mental imagery used is important for reading comprehension. A review of literature shows that there has been relatively little detailed research on the quality of imagery used by learners, especially in the case of students…
Descriptors: Educational Quality, Teaching Methods, English (Second Language), Second Language Learning