NotesFAQContact Us
Collection
Advanced
Search Tips
Publication Date
In 20250
Since 20240
Since 2021 (last 5 years)0
Since 2016 (last 10 years)4
Since 2006 (last 20 years)11
Audience
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing all 12 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Karadag, Engin; Oztekin-Bayir, Ozge – International Journal of Educational Leadership and Management, 2018
In the study, the effect of school principals' authentic leadership behaviors on teachers' perceptions of school culture was tested with the structural equation model. The study was carried out with the correlation research design. Authentic leadership behavior was taken as the independent variable, and school culture was taken as the dependent…
Descriptors: School Culture, Structural Equation Models, Principals, Administrator Behavior
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Freeman, Ruth; Gibson, Barry; Humphris, Gerry; Leonard, Helen; Yuan, Siyang; Whelton, Helen – Health Education Journal, 2016
Objective: To use a model of health learning to examine the role of health-learning capacity and the effect of a school-based oral health education intervention (Winning Smiles) on the health outcome, child oral health-related quality of life (COHRQoL). Setting: Primary schools, high social deprivation, Ireland/Northern Ireland. Design: Cluster…
Descriptors: Health Education, Role, Intervention, Dental Health
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Harring, Jeffrey R.; Weiss, Brandi A.; Hsu, Jui-Chen – Psychological Methods, 2012
Two Monte Carlo simulations were performed to compare methods for estimating and testing hypotheses of quadratic effects in latent variable regression models. The methods considered in the current study were (a) a 2-stage moderated regression approach using latent variable scores, (b) an unconstrained product indicator approach, (c) a latent…
Descriptors: Structural Equation Models, Geometric Concepts, Computation, Comparative Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Rose, Susan A.; Feldman, Judith F.; Jankowski, Jeffery J.; Van Rossem, Ronan – Intelligence, 2012
This study provides the first direct evidence of cognitive continuity for multiple specific information processing abilities from infancy and toddlerhood to pre-adolescence, and provides support for the view that infant abilities form the basis of later childhood abilities. Data from a large sample of children (N = 131) were obtained at five…
Descriptors: Evidence, Structural Equation Models, Intelligence Quotient, Infants
Peer reviewed Peer reviewed
Direct linkDirect link
Leite, Walter L.; Sandbach, Robert; Jin, Rong; MacInnes, Jann W.; Jackman, M. Grace-Anne – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and…
Descriptors: Structural Equation Models, Probability, Computation, Observation
Peer reviewed Peer reviewed
Direct linkDirect link
Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S. – Multivariate Behavioral Research, 2012
A Monte Carlo simulation was conducted to investigate the robustness of 4 latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of nonnormality of the observed…
Descriptors: Monte Carlo Methods, Computation, Robustness (Statistics), Structural Equation Models
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Schochet, Peter Z. – National Center for Education Evaluation and Regional Assistance, 2009
This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…
Descriptors: Control Groups, Causal Models, Statistical Significance, Computation
Peer reviewed Peer reviewed
Direct linkDirect link
Poncy, Brian C.; Skinner, Christopher H.; Axtell, Philip K. – Journal of Psychoeducational Assessment, 2005
Generalizability (G) theory was used with a sample of 37 third-grade students to assess the variability in words correct per minute (WCPM) scores caused by student skill and passage variability. Reliability-like coefficients and the SEM based on a specific number of assessments using different combinations of passages demonstrated how manipulating…
Descriptors: Generalizability Theory, Curriculum Based Assessment, Error of Measurement, Reliability