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Tenko Raykov; Christine DiStefano; Natalja Menold – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This article is concerned with the assumption of linear temporal development that is often advanced in structural equation modeling-based longitudinal research. The linearity hypothesis is implemented in particular in the popular intercept-and-slope model as well as in more general models containing it as a component, such as longitudinal…
Descriptors: Structural Equation Models, Hypothesis Testing, Longitudinal Studies, Research Methodology
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Lennert J. Groot; Kees-Jan Kan; Suzanne Jak – Research Synthesis Methods, 2024
Researchers may have at their disposal the raw data of the studies they wish to meta-analyze. The goal of this study is to identify, illustrate, and compare a range of possible analysis options for researchers to whom raw data are available, wanting to fit a structural equation model (SEM) to these data. This study illustrates techniques that…
Descriptors: Meta Analysis, Structural Equation Models, Research Methodology, Data Analysis
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Steffen Erickson – Society for Research on Educational Effectiveness, 2024
Background: Structural Equation Modeling (SEM) is a powerful and broadly utilized statistical framework. Researchers employ these models to dissect relationships into direct, indirect, and total effects (Bollen, 1989). These models unpack the "black box" issues within cause-and-effect studies by examining the underlying theoretical…
Descriptors: Structural Equation Models, Causal Models, Research Methodology, Error of Measurement
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Aitana González-Ortiz de Zárate; Helena Roig-Ester; Paulina E. Robalino Guerra; Anja Garone; Carla Quesada-Pallarès – International Journal of Training and Development, 2025
Transfer beliefs are understudied in the training transfer field, whereas structural equation modelling (SEM) has been a widely used technique to study transfer models. New methodologies are needed to study training transfer and network analysis (NA) has emerged as a new approach that provides a visual representation of a given network. We…
Descriptors: Trainees, Student Attitudes, Beliefs, Transfer of Training
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Suyoung Kim; Sooyong Lee; Jiwon Kim; Tiffany A. Whittaker – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits.…
Descriptors: Social Science Research, Behavioral Sciences, Structural Equation Models, Statistical Analysis
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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
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
Joao M. Souto-Maior; Kenneth A. Shores; Rachel E. Fish – Annenberg Institute for School Reform at Brown University, 2025
Whether selection processes contribute to group-level disparities or merely reflect pre-existing inequalities is an important societal question. In the context of observational data, researchers, concerned about omitted-variable bias, assess selection-contributing inequality via a kitchen-sink approach, comparing selection outcomes of…
Descriptors: Control Groups, Predictor Variables, Correlation, Selection Criteria
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Everson, Kimberlee C. – Journal of Statistics and Data Science Education, 2022
This study aims to identify some perceived gaps in a selection of statistical skills and software abilities of professors of education in United States colleges and universities. In addition to a general U. S. sample, a sample of education professors in Historically Black Colleges and Universities (HBCUs) was examined in order to understand their…
Descriptors: College Faculty, Teacher Competencies, Statistics, Computer Literacy
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Wilson, Sandra Jo; Polanin, Joshua R.; Lipsey, Mark W. – Research Synthesis Methods, 2016
A modification of the first stage of the standard procedure for two-stage meta-analytic structural equation modeling for use with large complex datasets is presented. This modification addresses two common problems that arise in such meta-analyses: (a) primary studies that provide multiple measures of the same construct and (b) the correlation…
Descriptors: Meta Analysis, Structural Equation Models, Correlation, Research Methodology
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López-Bonilla, Luis Miguel; López-Bonilla, Jesús Manuel – British Journal of Educational Technology, 2017
The debate about the role of attitude in the technology acceptance model (TAM) seems to have re-emerged in two prestigious journals in the field of educational technology. Among the publications on this debate, there are authors in favour of excluding the attitude of TAM, whereas others are in favour of including it. These opinions are derived…
Descriptors: Computer Attitudes, Adoption (Ideas), Models, Educational Technology
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Marchand, Gwen C.; Gutierrez, Antonio P. – Journal of Experimental Education, 2017
The purpose of this study was to investigate the relations among perceived instructional support (provision of relevance and involvement), subjective task value beliefs (utility, attainment, and intrinsic value), and engagement (behavioral and emotional) over the course of a semester for graduate students enrolled in an introductory research…
Descriptors: Graduate Students, Learner Engagement, Student Attitudes, Beliefs
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Karakaya-Ozyer, Kubra; Aksu-Dunya, Beyza – International Journal of Research in Education and Science, 2018
Structural equation modeling (SEM) is one of the most popular multivariate statistical techniques in Turkish educational research. This study elaborates the SEM procedures employed by 75 educational research articles which were published from 2010 to 2015 in Turkey. After documenting and coding 75 academic papers, categorical frequencies and…
Descriptors: Literature Reviews, Structural Equation Models, Educational Technology, Multivariate Analysis
Shanshan Wang; Carrie Biales; Ying Guo; Allison Breit-Smith – Sage Research Methods Cases, 2017
This case study uses structural equation modeling to examine the predictive validity of the Read Aloud Profile-Together, a measure of the distinct behaviors of parents and children during shared book reading, in relation to preschool children's early reading competency. Using secondary data analysis, this case study includes 800 parent-child pairs…
Descriptors: Predictive Validity, Preschool Children, Books, Reading Skills
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Green, Teegan – Studies in Higher Education, 2016
Despite increases in the number of articles published in higher education journals using structural equation modelling (SEM), research addressing their statistical sufficiency, methodological appropriateness and quantitative rigour is sparse. In response, this article provides a census of all covariance-based SEM articles published up until 2013…
Descriptors: Higher Education, Educational Research, Structural Equation Models, Sample Size
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