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Showing 1 to 15 of 44 results Save | Export
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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
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Xiao Liu; Lijuan Wang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
In parallel process latent growth curve mediation models, the mediation pathways from treatment to the intercept or slope of outcome through the intercept or slope of mediator are often of interest. In this study, we developed causal mediation analysis methods for these mediation pathways. Particularly, we provided causal definitions and…
Descriptors: Causal Models, Mediation Theory, Psychological Studies, Educational Research
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Educational and Psychological Measurement, 2022
Multilevel structural equation modeling (MSEM) allows 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 article…
Descriptors: Structural Equation Models, Factor Structure, Statistical Bias, Error of Measurement
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Yangqiuting Li; Chandralekha Singh – Physical Review Physics Education Research, 2024
Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying…
Descriptors: Structural Equation Models, Statistical Analysis, Educational Research, Causal Models
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Ghasemy, Majid; Teeroovengadum, Viraiyan; Becker, Jan-Michael; Ringle, Christian M. – Higher Education: The International Journal of Higher Education Research, 2020
The relevance and prominence of the partial least squares structural equation modeling (PLS-SEM) method has recently increased in higher education research, especially in explanatory and predictive studies. We therefore first aim to assess previous PLS-SEM applications by providing a systematic review; second, we aim to highlight and summarize…
Descriptors: Least Squares Statistics, Structural Equation Models, Higher Education, Educational Research
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Núñez-Regueiro, Fernando; Juhel, Jacques; Bressoux, Pascal; Nurra, Cécile – Journal of Educational Psychology, 2022
Part of the evidence used to corroborate school motivation theories relies on modeling methods that estimate cross-lagged effects between constructs, that is, reciprocal effects from one occasion to another. Yet, the reliability of cross-lagged models rests on the assumption that students do not differ in their trajectories of growth over time…
Descriptors: High School Students, Student Motivation, Academic Achievement, High Achievement
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Tessa Johnson; Tracy Sweet – Society for Research on Educational Effectiveness, 2021
Background/Context: Social network methodology is particularly relevant to the types of social structures found in education research. The current study develops a finite mixture approach for clustering ensembles of networks (NetMix). Following a structural equation modeling framework, NetMix simultaneously estimates a measurement model comprised…
Descriptors: Social Networks, Network Analysis, Research Methodology, Educational Research
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Ben Kelcey; Fangxing Bai; Amota Ataneka; Yanli Xie; Kyle Cox – Society for Research on Educational Effectiveness, 2024
We develop a structural after measurement (SAM) method for structural equation models (SEMs) that accommodates missing data. The results show that the proposed SAM missing data estimator outperforms conventional full information (FI) estimators in terms of convergence, bias, and root-mean-square-error in small-to-moderate samples or large samples…
Descriptors: Structural Equation Models, Research Problems, Error of Measurement, Maximum Likelihood Statistics
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Lohmann, Julian F.; Zitzmann, Steffen; Voelkle, Manuel C.; Hecht, Martin – Large-scale Assessments in Education, 2022
One major challenge of longitudinal data analysis is to find an appropriate statistical model that corresponds to the theory of change and the research questions at hand. In the present article, we argue that "continuous-time models" are well suited to study the continuously developing constructs of primary interest in the education…
Descriptors: Longitudinal Studies, Structural Equation Models, Time, Achievement Tests
Petscher, Yaacov; Schatschneider, Christopher – Educational and Psychological Measurement, 2019
Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Furthermore, in many cases only some students may be nested within a unit while other students may…
Descriptors: Structural Equation Models, Causal Models, Randomized Controlled Trials, Hierarchical Linear Modeling
Petscher, Yaacov; Schatschneider, Christopher – Grantee Submission, 2019
Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Further, in many cases only some students may be nested within a unit while other students may not.…
Descriptors: Structural Equation Models, Causal Models, Randomized Controlled Trials, Hierarchical Linear Modeling
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Hall, James; Malmberg, Lars-Erik; Lindorff, Ariel; Baumann, Nicole; Sammons, Pam – International Journal of Research & Method in Education, 2020
This paper presents a new methodological model termed Airbag Moderation: That the relationship between two variables varies as a function of a third, and that this third variable depends upon one of the others. Airbag Moderation extends and bridges a number of theories and it can be implemented using existing statistical models and software…
Descriptors: Multivariate Analysis, Models, Evaluation Methods, Educational Research
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Toker, Turker; Green, Kathy – International Journal of Assessment Tools in Education, 2021
This study provides a comparison of the results of latent class analysis (LCA) and mixture Rasch model (MRM) analysis using data from the Trends in International Mathematics and Science Study -- 2011 (TIMSS-2011) with a focus on the 8th-grade mathematics section. The research study focuses on the comparison of LCA and MRM to determine if results…
Descriptors: Multivariate Analysis, Structural Equation Models, Item Response Theory, Achievement Tests
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Lai, Yi-Horng – Pedagogical Research, 2019
Background: Interactive whiteboard (IWB) is an important tool in computer-assisted instruction (CAI). Interactive whiteboard is an electronic instructional and interactive technology designed by use for teachers. Some studies declare that teachers' behaviors in using interactive whiteboard only be infected by perceived usefulness, for teachers are…
Descriptors: Educational Technology, Computer Uses in Education, Visual Aids, Educational Research
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Malmberg, Lars-Erik – International Journal of Research & Method in Education, 2020
With a growing interest in research on educational processes, there is a need to overview suitable latent variable models for students' learning experiences in real-time. This tutorial provides an introduction to intraindividual (multilevel) structural equation models (ISEM) for the analysis of process data (e.g. intensive longitudinal,…
Descriptors: Structural Equation Models, Learning Experience, Educational Research, Personal Autonomy
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