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Gyeongcheol Cho; Heungsun Hwang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed…
Descriptors: Prediction, Methods, Networks, Simulation
Kang, Yoonjeong; Hancock, Gregory R. – Journal of Experimental Education, 2017
Structured means analysis is a very useful approach for testing hypotheses about population means on latent constructs. In such models, a z test is most commonly used for testing the statistical significance of the relevant parameter estimates or of the differences between parameter estimates, where a z value is computed based on the asymptotic…
Descriptors: Models, Statistical Analysis, Hypothesis Testing, Statistical Significance
Cheung, Mike W.-L.; Cheung, Shu Fai – Research Synthesis Methods, 2016
Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM.…
Descriptors: Statistical Analysis, Models, Meta Analysis, Structural Equation Models
Guyon, Hervé; Tensaout, Mouloud – Measurement: Interdisciplinary Research and Perspectives, 2016
In this article, the authors extend the results of Aguirre-Urreta, Rönkkö, and Marakas (2016) concerning the omission of a relevant causal indicator by testing the validity of the assumption that causal indicators are entirely superfluous to the measurement model and discuss the implications for measurement theory. Contrary to common wisdom…
Descriptors: Causal Models, Structural Equation Models, Formative Evaluation, Measurement
Wagner, Richard K.; Herrera, Sarah K.; Spencer, Mercedes; Quinn, Jamie M. – Journal of Learning Disabilities, 2015
Recently, Tunmer and Chapman provided an alternative model of how decoding and listening comprehension affect reading comprehension that challenges the simple view of reading. They questioned the simple view's fundamental assumption that oral language comprehension and decoding make independent contributions to reading comprehension by arguing…
Descriptors: Reading Comprehension, Decoding (Reading), Listening Comprehension, Oral Language
Jenkins, Andrew; Wiggins, Richard D. – International Review of Education, 2015
There is a growing interest among researchers and policy-makers in the influence of adult learning on a range of outcomes, notably health and well-being. Much of the research to date has tended to focus on younger adults and the immediate benefits of course participation. The longer-term outcomes, such as the potential of accumulated learning…
Descriptors: Adult Education, Lifelong Learning, Adults, Participation
Thissen, David – Measurement: Interdisciplinary Research and Perspectives, 2013
In this commentary, David Thissen states that "Goodness-of-fit assessment for IRT models is maturing; it has come a long way from zero." Thissen then references prior works on "goodness of fit" in the index of Lord and Novick's (1968) classic text; Yen (1984); Drasgow, Levine, Tsien, Williams, and Mead (1995); Chen and…
Descriptors: Goodness of Fit, Item Response Theory, Models, Statistical Analysis
A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates
Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…
Descriptors: Models, Statistical Analysis, Structural Equation Models, Factor Analysis
McGrath, Robert E.; Walters, Glenn D. – Psychological Methods, 2012
Statistical analyses investigating latent structure can be divided into those that estimate structural model parameters and those that detect the structural model type. The most basic distinction among structure types is between categorical (discrete) and dimensional (continuous) models. It is a common, and potentially misleading, practice to…
Descriptors: Factor Structure, Factor Analysis, Monte Carlo Methods, Computation
Muthen, Bengt; Asparouhov, Tihomir; Hunter, Aimee M.; Leuchter, Andrew F. – Psychological Methods, 2011
This article uses a general latent variable framework to study a series of models for nonignorable missingness due to dropout. Nonignorable missing data modeling acknowledges that missingness may depend not only on covariates and observed outcomes at previous time points as with the standard missing at random assumption, but also on latent…
Descriptors: Structural Equation Models, Depression (Psychology), Models, Trend Analysis
Cheng, Pi-Yueh; Hsu, Ping-Kun; Chiou, Wen-Bin – Asia Pacific Education Review, 2012
Previous research on professional certification has primarily focused on graduate certificates in intensive care nursing, writing certificates for practitioners, maintenance of certification in radiation oncology, and the certification of teachers and surgeons. Research on certification in the domain of business and management from an…
Descriptors: Certification, Tests, Foreign Countries, Structural Equation Models
Marsh, Herbert W.; Ludtke, Oliver; Nagengast, Benjamin; Trautwein, Ulrich; Morin, Alexandre J. S.; Abduljabbar, Adel S.; Koller, Olaf – Educational Psychologist, 2012
Classroom context and climate are inherently classroom-level (L2) constructs, but applied researchers sometimes--inappropriately--represent them by student-level (L1) responses in single-level models rather than more appropriate multilevel models. Here we focus on important conceptual issues (distinctions between climate and contextual variables;…
Descriptors: Foreign Countries, Classroom Environment, Educational Research, Research Design
Scheier, Lawrence M.; Grenard, Jerry L.; Holtz, Kristen D. – Journal of Drug Education, 2011
This study evaluated the efficacy of "Above the Influence" (ATI), a national media-based health persuasion campaign to deter youth drug use. The campaign uses public service anti-drug prevention messages and targets youth between the ages of 14 and 16, a period of heightened susceptibility to peer influences. The evaluation utilized mall…
Descriptors: Evidence, Advertising, Structural Equation Models, Drug Use
Abar, Caitlin C.; Maggs, Jennifer L. – Journal of College Student Development, 2010
Research indicates that social influences impact college students' alcohol consumption; however, how selection processes may serve as an influential factor predicting alcohol use in this population has not been widely addressed. A model of influence and selection processes contributing to alcohol use across the transition to college was examined…
Descriptors: Structural Equation Models, Alcohol Abuse, Drinking, Social Influences
Stone, Gregory Ethan; Koskey, Kristin L. K.; Sondergeld, Toni A. – Educational and Psychological Measurement, 2011
Typical validation studies on standard setting models, most notably the Angoff and modified Angoff models, have ignored construct development, a critical aspect associated with all conceptualizations of measurement processes. Stone compared the Angoff and objective standard setting (OSS) models and found that Angoff failed to define a legitimate…
Descriptors: Cutting Scores, Standard Setting (Scoring), Models, Construct Validity