Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 7 |
Descriptor
| Hierarchical Linear Modeling | 7 |
| Structural Equation Models | 7 |
| Accuracy | 2 |
| Correlation | 2 |
| Sample Size | 2 |
| Simulation | 2 |
| Achievement Tests | 1 |
| Behavioral Science Research | 1 |
| Computation | 1 |
| Computer Software | 1 |
| Data Analysis | 1 |
| More ▼ | |
Source
| Structural Equation Modeling:… | 3 |
| Educational Measurement:… | 1 |
| Educational and Psychological… | 1 |
| Journal of Experimental… | 1 |
| Journal of Science Education… | 1 |
Author
| Martin Hecht | 2 |
| Steffen Zitzmann | 2 |
| Cox, Kyle | 1 |
| Finch, W. Holmes | 1 |
| Flake, Jessica K. | 1 |
| Guo, Qing | 1 |
| Ibrahim, Bashirah | 1 |
| Julia-Kim Walther | 1 |
| Julian F. Lohmann | 1 |
| Kelcey, Benjamin | 1 |
| Qiao, CuiLan | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 7 |
| Reports - Research | 5 |
| Reports - Descriptive | 2 |
Education Level
| Secondary Education | 1 |
Audience
| Practitioners | 1 |
| Students | 1 |
Location
| China | 1 |
Laws, Policies, & Programs
Assessments and Surveys
| Program for International… | 1 |
What Works Clearinghouse Rating
Steffen Nestler; Sarah Humberg – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Several variants of the autoregressive structural equation model were suggested over the past years, including, for example, the random intercept autoregressive panel model, the latent curve model with structured residuals, and the STARTS model. The present work shows how to place these models into a mixed-effects model framework and how to…
Descriptors: Structural Equation Models, Computer Software, Models, Measurement
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
Shaw, Mairead; Flake, Jessica K. – Educational Measurement: Issues and Practice, 2023
Clustered data structures are common in many areas of educational and psychological research (e.g., students clustered in schools, patients clustered by clinician). In the course of conducting research, questions are often administered to obtain scores reflecting latent constructs. Multilevel measurement models (MLMMs) allow for modeling…
Descriptors: Hierarchical Linear Modeling, Research Methodology, Data Analysis, Structural Equation Models
Cox, Kyle; Kelcey, Benjamin – Educational and Psychological Measurement, 2023
Multilevel structural equation models (MSEMs) are well suited for educational research because they accommodate complex systems involving latent variables in multilevel settings. Estimation using Croon's bias-corrected factor score (BCFS) path estimation has recently been extended to MSEMs and demonstrated promise with limited sample sizes. This…
Descriptors: Structural Equation Models, Educational Research, Hierarchical Linear Modeling, Sample Size
Finch, W. Holmes – Journal of Experimental Education, 2022
Multivariate analysis of variance (MANOVA) is widely used to test the null hypothesis of equal multivariate means across 2 or more groups. MANOVA rests upon an assumption that error terms are independent of one another, which can be violated if individuals are clustered or nested within groups, such as schools. Ignoring such nesting can result in…
Descriptors: Multivariate Analysis, Hypothesis Testing, Structural Equation Models, Hierarchical Linear Modeling
Julian F. Lohmann; Steffen Zitzmann; Martin Hecht – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The recently proposed "continuous-time latent curve model with structured residuals" (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle…
Descriptors: Time Management, Behavioral Science Research, Predictive Validity, Predictor Variables
Guo, Qing; Qiao, CuiLan; Ibrahim, Bashirah – Journal of Science Education and Technology, 2022
Information and communication technology (ICT) is key to educational development. This study explores the mechanism influencing the use of ICT on students' science literacy. We utilized two-level hierarchical linear models and structural equation models to analyze data collected from the 2015 Program for International Student Assessment (PISA) in…
Descriptors: Correlation, Scientific Literacy, Information Technology, Personal Autonomy

Peer reviewed
Direct link
