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Yuanfang Liu; Mark H. C. Lai; Ben Kelcey – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Measurement invariance holds when a latent construct is measured in the same way across different levels of background variables (continuous or categorical) while controlling for the true value of that construct. Using Monte Carlo simulation, this paper compares the multiple indicators, multiple causes (MIMIC) model and MIMIC-interaction to a…
Descriptors: Classification, Accuracy, Error of Measurement, Correlation
Miyazaki, Yasuo; Kamata, Akihito; Uekawa, Kazuaki; Sun, Yizhi – Educational and Psychological Measurement, 2022
This paper investigated consequences of measurement error in the pretest on the estimate of the treatment effect in a pretest-posttest design with the analysis of covariance (ANCOVA) model, focusing on both the direction and magnitude of its bias. Some prior studies have examined the magnitude of the bias due to measurement error and suggested…
Descriptors: Error of Measurement, Pretesting, Pretests Posttests, Statistical Bias
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Grantee Submission, 2021
Multilevel structural equation (MSEM) models allow researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This paper…
Descriptors: Sampling, Structural Equation Models, Factor Structure, Monte Carlo Methods
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
Valente, Matthew J.; Gonzalez, Oscar; Miocevic, Milica; MacKinnon, David P. – Educational and Psychological Measurement, 2016
Methods to assess the significance of mediated effects in education and the social sciences are well studied and fall into two categories: single sample methods and computer-intensive methods. A popular single sample method to detect the significance of the mediated effect is the test of joint significance, and a popular computer-intensive method…
Descriptors: Structural Equation Models, Sampling, Statistical Inference, Statistical Bias
Ciftci, S. Koza – Online Submission, 2019
In this study, the effect of mathematics teacher candidates' locus of control on math anxiety was tested, along with the effects of gender, achievement and class level moderators. The study was carried out according to a causal design in which locus of control was taken as an independent variable, while math anxiety was taken as the dependent…
Descriptors: Locus of Control, Mathematics Teachers, Mathematics Anxiety, Gender Differences
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
Li, Xin; Beretvas, S. Natasha – Structural Equation Modeling: A Multidisciplinary Journal, 2013
This simulation study investigated use of the multilevel structural equation model (MLSEM) for handling measurement error in both mediator and outcome variables ("M" and "Y") in an upper level multilevel mediation model. Mediation and outcome variable indicators were generated with measurement error. Parameter and standard…
Descriptors: Sample Size, Structural Equation Models, Simulation, Multivariate Analysis
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
Aydin, Burak; Leite, Walter L.; Algina, James – Educational and Psychological Measurement, 2016
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…
Descriptors: Error of Measurement, Predictor Variables, Randomized Controlled Trials, Experimental Groups
Westfall, Peter H.; Henning, Kevin S. S.; Howell, Roy D. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This article shows how interfactor correlation is affected by error correlations. Theoretical and practical justifications for error correlations are given, and a new equivalence class of models is presented to explain the relationship between interfactor correlation and error correlations. The class allows simple, parsimonious modeling of error…
Descriptors: Psychometrics, Correlation, Error of Measurement, Structural Equation Models
Diemer, Matthew A.; Rapa, Luke J. – Child Development, 2016
This research examines the complex patterns by which distinct dimensions of critical consciousness may lead marginalized adolescents toward distinct forms of political action. Structural equation modeling was applied to nationally representative data from the Civic Education Study (2,811 ninth graders; M[subscript age] = 14.6), first establishing…
Descriptors: Adolescents, Disadvantaged, Social Action, Voting
De Laet, Steven; Colpin, Hilde; Goossens, Luc; Van Leeuwen, Karla; Verschueren, Karine – Journal of Psychoeducational Assessment, 2014
Through an examination of measurement invariance, this study investigated whether attachment-related dimensions (i.e., secure base, safe haven, and negative interactions as measured with the Network of Relationships Inventory-Behavioral Systems Version) have the same psychological meaning for early adolescents in their relationships with parents…
Descriptors: Parent Child Relationship, Attachment Behavior, Error of Measurement, Teacher Student Relationship
Guerere, Claudia – ProQuest LLC, 2013
Scores from value-added models (VAMs), as used for educational accountability, represent the educational effect teachers have on their students. The use of these scores in teacher evaluations for high-stakes decision making is new for the State of Florida. Validity evidence that supports or questions the use of these scores is critically needed.…
Descriptors: Teacher Evaluation, Accountability, School Districts, Validity
Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D. – Psychometrika, 2011
Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…
Descriptors: Structural Equation Models, Simulation, Behavioral Sciences, Social Sciences