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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
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Cao, Chunhua; Kim, Eun Sook; Chen, Yi-Hsin; Ferron, John; Stark, Stephen – Educational and Psychological Measurement, 2019
In multilevel multiple-indicator multiple-cause (MIMIC) models, covariates can interact at the within level, at the between level, or across levels. This study examines the performance of multilevel MIMIC models in estimating and detecting the interaction effect of two covariates through a simulation and provides an empirical demonstration of…
Descriptors: Hierarchical Linear Modeling, Structural Equation Models, Computation, Identification
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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
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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
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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
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Dicke, Theresa; Marsh, Herbert W.; Parker, Philip D.; Pekrun, Reinhard; Guo, Jiesi; Televantou, Ioulia – Journal of Educational Psychology, 2018
School-average achievement is often reported to have positive effects on individual achievement (peer spillover effect). However, it is well established that school-average achievement has negative effects on academic self-concept (big-fish-little-pond effect [BFLPE]) and that academic self-concept and achievement are positively correlated and…
Descriptors: Academic Achievement, Self Concept, Peer Influence, Children
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Arens, A. Katrin; Morin, Alexandre J. S. – American Educational Research Journal, 2017
This study illustrates an integrative psychometric framework to investigate two sources of construct-relevant multidimensionality in answers to the Self-Perception Profile for Children (SPPC). Using a sample of 2,353 German students attending Grades 3 to 6, we contrasted: (a) first-order versus hierarchical and bifactor models to investigate…
Descriptors: Self Concept, Structural Equation Models, Factor Analysis, Error of Measurement
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Wang, Ze – Educational Psychology, 2015
Using data from the Trends in International Mathematics and Science Study (TIMSS) 2007, this study examined the big-fish-little-pond-effects (BFLPEs) in 49 countries. In this study, the effect of math ability on math self-concept was decomposed into a within- and a between-level components using implicit mean centring and the complex data…
Descriptors: Nonverbal Ability, Mathematics, Self Concept, Hierarchical Linear Modeling
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Televantou, Ioulia; Marsh, Herbert W.; Kyriakides, Leonidas; Nagengast, Benjamin; Fletcher, John; Malmberg, Lars-Erik – School Effectiveness and School Improvement, 2015
The main objective of this study was to quantify the impact of failing to account for measurement error on school compositional effects. Multilevel structural equation models were incorporated to control for measurement error and/or sampling error. Study 1, a large sample of English primary students in Years 1 and 4, revealed a significantly…
Descriptors: Hierarchical Linear Modeling, Statistical Bias, Error of Measurement, Educational Research
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Jacob, Robin T.; Goddard, Roger D.; Kim, Eun Sook – Educational Evaluation and Policy Analysis, 2014
It is often difficult and costly to obtain individual-level student achievement data, yet, researchers are frequently reluctant to use school-level achievement data that are widely available from state websites. We argue that public-use aggregate school-level achievement data are, in fact, sufficient to address a wide range of evaluation questions…
Descriptors: Academic Achievement, Data, Information Utilization, Educational Assessment
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Long, Mark C. – Journal of Research on Educational Effectiveness, 2016
Using a "naïve" specification, this paper estimates the relationship between 36 high school characteristics and 24 student outcomes controlling for students' pre-high school characteristics. The goal of this exploration is not to generate casual estimates, but rather to: (a) compare the size of the relationships to determine which inputs…
Descriptors: Hypothesis Testing, Effect Size, High School Students, Student Characteristics
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Morin, Alexandre J. S.; Marsh, Herbert W.; Nagengast, Benjamin; Scalas, L. Francesca – Journal of Experimental Education, 2014
Many classroom climate studies suffer from 2 critical problems: They (a) treat climate as a student-level (L1) variable in single-level analyses instead of a classroom-level (L2) construct in multilevel analyses; and (b) rely on manifest-variable models rather than on latent-variable models that control measurement error at L1 and L2, and sampling…
Descriptors: Classroom Environment, Hierarchical Linear Modeling, Structural Equation Models, Grade 5
Diakow, Ronli Phyllis – ProQuest LLC, 2013
This dissertation comprises three papers that propose, discuss, and illustrate models to make improved inferences about research questions regarding student achievement in education. Addressing the types of questions common in educational research today requires three different "extensions" to traditional educational assessment: (1)…
Descriptors: Inferences, Educational Assessment, Academic Achievement, Educational Research