Publication Date
In 2025 | 1 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 7 |
Since 2006 (last 20 years) | 13 |
Descriptor
Source
Author
Cao, Chunhua | 2 |
Chen, Yi-Hsin | 2 |
Ferron, John | 2 |
Kim, Eun Sook | 2 |
Anderson-Clark, Helen | 1 |
Blackman, Horatio | 1 |
Blalock, Toscha | 1 |
Davila, Heather Goldsworthy | 1 |
Du, Han | 1 |
Enders, Craig K. | 1 |
Gage, Nicholas A. | 1 |
More ▼ |
Publication Type
Reports - Research | 11 |
Journal Articles | 10 |
Dissertations/Theses -… | 1 |
Information Analyses | 1 |
Reports - Descriptive | 1 |
Education Level
Elementary Education | 3 |
Junior High Schools | 3 |
Middle Schools | 3 |
Secondary Education | 3 |
Early Childhood Education | 1 |
Elementary Secondary Education | 1 |
Grade 1 | 1 |
Grade 6 | 1 |
Grade 7 | 1 |
Grade 8 | 1 |
High Schools | 1 |
More ▼ |
Audience
Location
California | 1 |
Qatar | 1 |
Texas | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Iowa Tests of Basic Skills | 1 |
What Works Clearinghouse Rating
Julia-Kim Walther; Martin Hecht; Steffen Zitzmann – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized…
Descriptors: Sample Size, Hierarchical Linear Modeling, Structural Equation Models, Matrices
Ismail Dilek – ProQuest LLC, 2022
Hierarchical data is often observed in education data. Analyzing such data with Multilevel Modeling becomes crucial to understanding the relationship at the individual and group levels. However, one of the most significant problems with this kind of data is small sample sizes and very low Intraclass Correlations. The multivariate Latent Covariate…
Descriptors: Education, Data, Hierarchical Linear Modeling, Methods
Cao, Chunhua; Kim, Eun Sook; Chen, Yi-Hsin; Ferron, John – Educational and Psychological Measurement, 2021
This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates…
Descriptors: Goodness of Fit, Hierarchical Linear Modeling, Computation, Models
Enders, Craig K.; Hayes, Timothy; Du, Han – Grantee Submission, 2018
Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random…
Descriptors: Data Analysis, Statistical Bias, Sample Size, Correlation
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
Huang, Francis L. – School Psychology Quarterly, 2018
The use of multilevel modeling (MLM) to analyze nested data has grown in popularity over the years in the study of school psychology. However, with the increase in use, several statistical misconceptions about the technique have also proliferated. We discuss some commonly cited myths and golden rules related to the use of MLM, explain their…
Descriptors: Hierarchical Linear Modeling, School Psychology, Misconceptions, Correlation
Rhoads, Christopher – Journal of Educational and Behavioral Statistics, 2017
Researchers designing multisite and cluster randomized trials of educational interventions will usually conduct a power analysis in the planning stage of the study. To conduct the power analysis, researchers often use estimates of intracluster correlation coefficients and effect sizes derived from an analysis of survey data. When there is…
Descriptors: Statistical Analysis, Hierarchical Linear Modeling, Surveys, Effect Size
Schweig, Jonathan – Journal of Educational and Behavioral Statistics, 2014
Measures of classroom environments have become central to policy efforts that assess school and teacher quality. This has sparked a wide interest in using multilevel factor analysis to test measurement hypotheses about classroom-level variables. One approach partitions the total covariance matrix and tests models separately on the…
Descriptors: Factor Analysis, Robustness (Statistics), Measurement, Classroom Environment
Gage, Nicholas A.; Lewis, Timothy J. – Journal of Special Education, 2014
The identification of evidence-based practices continues to provoke issues of disagreement across multiple fields. One area of contention is the role of single-subject design (SSD) research in providing scientific evidence. The debate about SSD's utility centers on three issues: sample size, effect size, and serial dependence. One potential…
Descriptors: Hierarchical Linear Modeling, Meta Analysis, Research Design, Sample Size
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
May, Henry; Sirinides, Philip; Gray, Abby; Davila, Heather Goldsworthy; Sam, Cecile; Blalock, Toscha; Blackman, Horatio; Anderson-Clark, Helen; Schiera, Andrew J. – Society for Research on Educational Effectiveness, 2015
As part of the 2010 economic stimulus, a $55 million "Investing in Innovation" (i3) grant from the US Department of Education was awarded to scale up Reading Recovery across the nation. This paper presents the final round of results from the large-scale, mixed methods randomized evaluation of the implementation and impacts of Reading…
Descriptors: Reading Programs, Program Evaluation, Reading Achievement, Mixed Methods Research
Yen, Wendy M.; Lall, Venessa F.; Monfils, Lora – ETS Research Report Series, 2012
Alternatives to vertical scales are compared for measuring longitudinal academic growth and for producing school-level growth measures. The alternatives examined were empirical cross-grade regression, ordinary least squares and logistic regression, and multilevel models. The student data used for the comparisons were Arabic Grades 4 to 10 in…
Descriptors: Foreign Countries, Scaling, Item Response Theory, Test Interpretation
Jiang, Zhonghong; White, Alexander; Rosenwasser, Alana – Journal of Mathematics Education at Teachers College, 2011
The project reported here is conducting repeated randomized control trials of an approach to high school geometry that utilizes Dynamic Geometry (DG) software to supplement ordinary instructional practices. It compares effects of that intervention with standard instruction that does not make use of computer drawing/exploration tools. The basic…
Descriptors: Randomized Controlled Trials, Geometry, Comparative Analysis, Computer Uses in Education