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Showing 1 to 15 of 29 results Save | Export
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James Ohisei Uanhoro – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean…
Descriptors: Bayesian Statistics, Structural Equation Models, Correlation, Monte Carlo Methods
Edgar C. Merkle; Oludare Ariyo; Sonja D. Winter; Mauricio Garnier-Villarreal – Grantee Submission, 2023
We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies differs from the prior distribution used during estimation. These situations can arise from the positive definite requirement on correlation matrices, from sign indeterminacy of factor loadings, and from order constraints on…
Descriptors: Models, Bayesian Statistics, Correlation, Evaluation Methods
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Marcoulides, Katerina M.; Yuan, Ke-Hai – International Journal of Research & Method in Education, 2020
Multilevel structural equation models (MSEM) are typically evaluated on the basis of goodness of fit indices. A problem with these indices is that they pertain to the entire model, reflecting simultaneously the degree of fit for all levels in the model. Consequently, in cases that lack model fit, it is unclear which level model is misspecified.…
Descriptors: Goodness of Fit, Structural Equation Models, Correlation, Inferences
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Nagy, Gabriel; Brunner, Martin; Lüdtke, Oliver; Greiff, Samuel – Journal of Experimental Education, 2017
We present factor extension procedures for confirmatory factor analysis that provide estimates of the relations of common and unique factors with external variables that do not undergo factor analysis. We present identification strategies that build upon restrictions of the pattern of correlations between unique factors and external variables. The…
Descriptors: Factor Analysis, Evaluation Methods, Identification, Correlation
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Peterson, Christina Hamme; Gischlar, Karen L.; Peterson, N. Andrew – Journal for Specialists in Group Work, 2017
Measures that accurately capture the phenomenon are critical to research and practice in group work. The vast majority of group-related measures were developed using the reflective measurement model rooted in classical test theory (CTT). Depending on the construct definition and the measure's purpose, the reflective model may not always be the…
Descriptors: Item Response Theory, Group Activities, Test Theory, Test Items
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Peeters, Carel F. W. – Psychometrika, 2012
In an addendum to his seminal 1969 article Joreskog stated two sets of conditions for rotational identification of the oblique factor solution under utilization of fixed zero elements in the factor loadings matrix (Joreskog in "Advances in factor analysis and structural equation models," pp. 40-43, 1979). These condition sets, formulated under…
Descriptors: Structural Equation Models, Factor Analysis, Correlation
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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
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Reise, Steven P. – Multivariate Behavioral Research, 2012
Bifactor latent structures were introduced over 70 years ago, but only recently has bifactor modeling been rediscovered as an effective approach to modeling "construct-relevant" multidimensionality in a set of ordered categorical item responses. I begin by describing the Schmid-Leiman bifactor procedure (Schmid & Leiman, 1957) and highlight its…
Descriptors: Models, Factor Structure, Factor Analysis, Correlation
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Muthen, Bengt; Asparouhov, Tihomir – Psychological Methods, 2012
This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed…
Descriptors: Factor Analysis, Cognitive Ability, Science Achievement, Structural Equation Models
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Beckstead, Jason W. – Multivariate Behavioral Research, 2012
The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…
Descriptors: Multiple Regression Analysis, Predictor Variables, Factor Analysis, Structural Equation Models
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Eastman, Jacqueline Kilsheimer; Iyer, Rajesh; Eastman, Kevin L. – Journal of Education for Business, 2011
The authors modeled the relationships between students' perceptions of interactive technology in terms of whether it helps them pay more attention and be better prepared in a Consumer Behavior course and their attitude toward and satisfaction with it. The results suggest that students who feel they pay more attention due to the use of Interactive…
Descriptors: Student Attitudes, Consumer Economics, Business Education, Educational Technology
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Cheung, Mike W. -L. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Confidence intervals (CIs) for parameters are usually constructed based on the estimated standard errors. These are known as Wald CIs. This article argues that likelihood-based CIs (CIs based on likelihood ratio statistics) are often preferred to Wald CIs. It shows how the likelihood-based CIs and the Wald CIs for many statistics and psychometric…
Descriptors: Intervals, Structural Equation Models, Simulation, Correlation
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Najdowski, Cynthia J.; Ullman, Sarah E. – Psychology of Women Quarterly, 2009
Prior research has demonstrated that self-blame is predictive of more posttraumatic stress disorder (PTSD) symptoms and poorer recovery (Frazier, 2003; Koss, Figueredo, & Prince, 2002), and perceived control over recovery is associated with less distress (Frazier, 2003) in adult sexual assault (ASA) survivors. A structural equation model was…
Descriptors: Sexual Abuse, Posttraumatic Stress Disorder, Coping, Symptoms (Individual Disorders)
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Raykov, Tenko; Mels, Gerhard – Structural Equation Modeling: A Multidisciplinary Journal, 2009
A readily implemented procedure is discussed for interval estimation of indexes of interrelationship between items from multiple-component measuring instruments as well as between items and total composite scores. The method is applicable with categorical (ordinal) observed variables, and can be widely used in the process of scale construction,…
Descriptors: Intervals, Structural Equation Models, Biomedicine, Correlation
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Wong, Emily M. L.; Li, Sandy C. – Australasian Journal of Educational Technology, 2011
Despite the common belief that information and communication technology (ICT) has the potential to support certain fundamental changes in learning, few have examined ICT implementation conceptually within a wider context of educational change. Methodologically, we are by and large limited to building simple models that accommodate only a single…
Descriptors: Foreign Countries, Technology Integration, Collegiality, Educational Change
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