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Roy Levy; Daniel McNeish – Journal of Educational and Behavioral Statistics, 2025
Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…
Descriptors: Models, Statistical Analysis, Measurement, Data Interpretation
Thompson, Yutian T.; Song, Hairong; Shi, Dexin; Liu, Zhengkui – Educational and Psychological Measurement, 2021
Conventional approaches for selecting a reference indicator (RI) could lead to misleading results in testing for measurement invariance (MI). Several newer quantitative methods have been available for more rigorous RI selection. However, it is still unknown how well these methods perform in terms of correctly identifying a truly invariant item to…
Descriptors: Measurement, Statistical Analysis, Selection, Comparative Analysis
Xu Qin; Lijuan Wang – Grantee Submission, 2023
Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued in substantive research. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and…
Descriptors: Causal Models, Mediation Theory, Computer Software, Statistical Analysis
Önen, Emine – Universal Journal of Educational Research, 2019
This simulation study was conducted to compare the performances of Frequentist and Bayesian approaches in the context of power to detect model misspecification in terms of omitted cross-loading in CFA models with respect to the several variables (number of omitted cross-loading, magnitude of main loading, number of factors, number of indicators…
Descriptors: Factor Analysis, Bayesian Statistics, Comparative Analysis, Statistical Analysis
De Bondt, Niki; Van Petegem, Peter – High Ability Studies, 2017
The aim of this study is to investigate interrelationships between overexcitability and learning patterns from the perspective of personality development according to Dabrowski's theory of positive disintegration. To this end, Bayesian structural equation modeling (BSEM) is applied which allows for the simultaneous inclusion in the measurement…
Descriptors: Psychological Patterns, Structural Equation Models, Bayesian Statistics, College Students
van der Sande, Lisa; Hendrickx, Marloes M. H. G.; Boor-Klip, Henrike J.; Mainhard, Tim – Journal of Learning Disabilities, 2018
Although many studies have found that children with learning disabilities (LD) are less liked by peers than children without LD, the results are not unequivocal. In the present study, we investigated the social status (in terms of likeability and popularity) of children with LD by considering peer academic reputation and peer reputation of teacher…
Descriptors: Learning Disabilities, Social Status, Reputation, Peer Relationship
Sander, Julia; Schupp, Jürgen; Richter, David – Developmental Psychology, 2017
Frequent social interactions are strongly linked to positive affect, longevity, and good health. Although there has been extensive research on changes in the size of social networks over time, little attention has been given to the development of contact frequency across the life span. In this cohort-sequential longitudinal study, we examined…
Descriptors: Interpersonal Relationship, Foreign Countries, Longitudinal Studies, Bayesian Statistics
Smith, Andrea D.; Herle, Moritz; Fildes, Alison; Cooke, Lucy; Steinsbekk, Silje; Llewellyn, Clare H. – Journal of Child Psychology and Psychiatry, 2017
Background: "Food fussiness" (FF) is the tendency to be highly selective about which foods one is willing to eat, and emerges in early childhood; "food neophobia" (FN) is a closely related characteristic but specifically refers to rejection of unfamiliar food. These behaviors are associated, but the extent to which their…
Descriptors: Food, Fear, Genetics, Environmental Influences
Harring, Jeffrey R. – Educational and Psychological Measurement, 2014
Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the…
Descriptors: Regression (Statistics), Models, Statistical Analysis, Maximum Likelihood Statistics
Mammadov, Sakhavat; Ward, Thomas J.; Cross, Jennifer Riedl; Cross, Tracy L. – Roeper Review, 2016
To date, in gifted education and related fields various conventional factor analytic and clustering techniques have been used extensively for investigation of the underlying structure of data. Latent profile analysis is a relatively new method in the field. In this article, we provide an introduction to latent profile analysis for gifted education…
Descriptors: Statistical Analysis, Academically Gifted, Factor Analysis, Multivariate Analysis
Depaoli, Sarah – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Proper model specification is an issue for researchers, regardless of the estimation framework being utilized. Typically, indexes are used to compare the fit of one model to the fit of an alternate model. These indexes only provide an indication of relative fit and do not necessarily point toward proper model specification. There is a procedure in…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Statistical Analysis
Muthen, Bengt; Asparouhov, Tihomir – Psychological Methods, 2012
This rejoinder discusses the general comments on how to use Bayesian structural equation modeling (BSEM) wisely and how to get more people better trained in using Bayesian methods. Responses to specific comments cover how to handle sign switching, nonconvergence and nonidentification, and prior choices in latent variable models. Two new…
Descriptors: Structural Equation Models, Bayesian Statistics, Factor Analysis, Statistical Analysis
Rindskopf, David – Psychological Methods, 2012
Muthen and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By…
Descriptors: Structural Equation Models, Bayesian Statistics, Factor Analysis, Computation
Price, Larry R. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Descriptors: Sample Size, Time, Bayesian Statistics, Structural Equation Models