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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
Nestler, Steffen; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2022
The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM)…
Descriptors: Interpersonal Relationship, Longitudinal Studies, Data Analysis, Structural Equation Models
Ziqian Xu – Grantee Submission, 2022
With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at…
Descriptors: Research Problems, Bayesian Statistics, Structural Equation Models, Data Analysis
Xiaohui Luo; Yueqin Hu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…
Descriptors: Structural Equation Models, Longitudinal Studies, Data Analysis, Causal Models
Ke-Hai Yuan; Ling Ling; Zhiyong Zhang – Grantee Submission, 2024
Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…
Descriptors: Structural Equation Models, Computation, Social Science Research, Error of Measurement
Ke-Hai Yuan; Ling Ling; Zhiyong Zhang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…
Descriptors: Structural Equation Models, Computation, Social Science Research, Error of Measurement
Xiaying Zheng; Ji Seung Yang; Jeffrey R. Harring – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Measuring change in an educational or psychological construct over time is often achieved by repeatedly administering the same items to the same examinees over time and fitting a second-order latent growth curve model. However, latent growth modeling with full information maximum likelihood (FIML) estimation becomes computationally challenging…
Descriptors: Longitudinal Studies, Data Analysis, Item Response Theory, Structural Equation Models
Serang, Sarfaraz – New Directions for Child and Adolescent Development, 2021
Longitudinal research is often interested in identifying correlates of heterogeneity in change. This paper compares three approaches for doing so: the mixed-effects model (latent growth curve model), the growth mixture model, and structural equation model trees. Each method is described, with special focus given to how each structures…
Descriptors: Longitudinal Studies, National Surveys, Growth Models, Structural Equation Models
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
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
Selvi, Hüseyin; Alici, Devrim; Uzun, Nezaket Bilge – Asian Journal of Education and Training, 2020
This study aims to comparatively examine the resultant findings by testing the measurement invariance with structural equation modeling in cases where the missing data is handled using the expectation-maximization (EM), regression imputation, and mean substitution methods in the complete data matrix and the 5% missing data matrix that is randomly…
Descriptors: Error of Measurement, Structural Equation Models, Attitude Measures, Student Attitudes
Kuddar, Cagla; Cetin, Sevda – International Journal of Assessment Tools in Education, 2022
The purpose of the study is to analyze the affective traits that affect mathematics achievement through Structural Equation Modeling (SEM) as a traditional regression model and Multivariate Adaptive Regression Splines (MARS), as one of the data mining methods. Structural Equation Modeling, one of the regression-based methods, is quite popular for…
Descriptors: Mathematics Achievement, Structural Equation Models, Regression (Statistics), Achievement Tests
Buske, Ramona; Zlatkin-Troitschanskaia, Olga – Educational Management Administration & Leadership, 2019
In this study, we examine the relationships between principals' evidence-oriented attitudes and their data use in school leadership practices. Based on theoretical assumptions and existing empirical research, we develop hypotheses on the correlation of evidence-oriented attitudes, epistemological beliefs and principals' data use. We test these…
Descriptors: Principals, Administrator Attitudes, Data Analysis, Evidence Based Practice
Yu, Hongwei; Glanzer, Perry L.; Johnson, Byron R.; Sriram, Rishi; Moore, Brandon – Review of Higher Education, 2018
Though numerous studies have identified factors associated with academic misconduct, few have proposed conceptual models that could make sense of multiple factors. In this study, we used structural equation modeling (SEM) to test a conceptual model of five factors using data from a relatively large sample of 2,503 college students. The results…
Descriptors: College Students, Cheating, Structural Equation Models, Data Analysis
Kim, Seohyun; Lu, Zhenqiu; Cohen, Allan S. – Measurement: Interdisciplinary Research and Perspectives, 2018
Bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables.…
Descriptors: Bayesian Statistics, Structural Equation Models, Computation, Social Science Research