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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
Bentler, Peter M. – Measurement: Interdisciplinary Research and Perspectives, 2016
The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…
Descriptors: Causal Models, Factor Analysis, Prediction, Scores
Markus, Keith A. – Measurement: Interdisciplinary Research and Perspectives, 2016
In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…
Descriptors: Causal Models, Measurement, Criticism, Concept Mapping
Willoughby, Michael T. – Grantee Submission, 2014
The focus article (Willoughby et al., 2014) (1) introduced the distinction between formative and reflective measurement and (2) proposed that performance-based executive function tasks may be better conceptualized from the perspective of formative rather than reflective measurement. This proposal stands in sharp contrast to conventional…
Descriptors: Executive Function, Formative Evaluation, Cognitive Measurement, Factor Analysis
Edwards, Michael C. – Measurement: Interdisciplinary Research and Perspectives, 2013
This author has had the privilege of knowing Professor Maydeu-Olivares for almost a decade and although their paths cross only occasionally, such instances were always enjoyable and enlightening. Edwards states that Maydeu-Olivares' target article for this issue, ("Goodness-of-Fit Assessment of Item Response Theory Models") provides…
Descriptors: Goodness of Fit, Item Response Theory, Models, Factor 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
Weiss, Lawrence G.; Keith, Timothy Z.; Zhu, Jianjun; Chen, Hsinyi – Journal of Psychoeducational Assessment, 2013
This discussion article addresses issues related to expansion of the Wechsler model from four to five factors; multiple broad CHC abilities measured by the Arithmetic subtest; advantages and disadvantages of including complex tasks requiring integration of multiple broad abilities when measuring intelligence; limitations of factor analysis, which…
Descriptors: Measures (Individuals), Intelligence Tests, Brain Hemisphere Functions, Neurological Organization
MacCallum, Robert C.; Edwards, Michael C.; Cai, Li – Psychological Methods, 2012
Muthen and Asparouhov (2012) have proposed and demonstrated an approach to model specification and estimation in structural equation modeling (SEM) using Bayesian methods. Their contribution builds on previous work in this area by (a) focusing on the translation of conventional SEM models into a Bayesian framework wherein parameters fixed at zero…
Descriptors: Structural Equation Models, Bayesian Statistics, Computation, Expertise
Henson, Robert A. – Measurement: Interdisciplinary Research and Perspectives, 2009
The paper by Drs. Rupp and Templin provides a much needed step toward the general application of diagnostic classification modeling (DCMs). The authors have provided a summary of many of the concepts that one must consider to properly apply a DCM (which ranges from model selection and estimation, to assessing the appropriateness of the model using…
Descriptors: Classification, Psychometrics, Evaluation, Models
Widaman, Keith F.; Grimm, Kevin J. – Measurement: Interdisciplinary Research and Perspectives, 2009
Nesselroade, Gerstorf, Hardy, and Ram developed a new and interesting way to enforce invariance at the second-order level in P-technique models, while allowing first-order structure to stray from invariance. We discuss our concerns with this approach under the headings of falsifiability, the nature of manifest variables included in models, and…
Descriptors: Factor Structure, Models, Factor Analysis, Comparative Analysis
Nesselroade, John R.; Ram, Nilam; Gerstorf, Denis; Hardy, Sam A. – Measurement: Interdisciplinary Research and Perspectives, 2009
This article presents the authors' response which consists of three main parts. The first involves recapping the general thrust of their focus article. The second part consists of some general points that they hope will clarify issues raised by the commentators that were not made as clearly as they should have been in the focus article. The third…
Descriptors: Structural Equation Models, Evaluation, Factor Analysis, Statistics
Runger, Dennis; Nagy, Gabriel; Frensch, Peter A. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2009
Whether sequence learning entails a single or multiple memory systems is a moot issue. Recently, D. R. Shanks, L. Wilkinson, and S. Channon advanced a single-system model that predicts a perfect correlation between true (i.e., error free) response time priming and recognition. The Shanks model is contrasted with a dual-process model that…
Descriptors: Priming, Reaction Time, Recognition (Psychology), Factor Analysis
Borsboom, Denny; Dolan, Conor V. – Measurement: Interdisciplinary Research and Perspectives, 2007
Nesselroade, Gerstorf, Hardy, and Ram (this issue) propose to "filter out" idiosyncrasies of dynamic processes at the level of the individual through the application of dynamic factor analysis. The problem that they deal with is that individuals may differ in the items that are "salient" for a given construct, so that the same measurement model…
Descriptors: Factor Structure, Factor Analysis, Individual Differences, Models
West, Stephen G.; Ryu, Ehri – Measurement: Interdisciplinary Research and Perspectives, 2007
Nomothetic and idiographic approaches to research have long been in tension. John Nesselroade et al. have been at the forefront of a constructive rapprochement between these traditions. Heretofore their efforts have assumed a common measurement structure across persons. They have primarily focused on modeling relationships within persons, which…
Descriptors: Measurement Techniques, Factor Analysis, Models, Patients
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