NotesFAQContact Us
Collection
Advanced
Search Tips
50 Years of ERIC
50 Years of ERIC
The Education Resources Information Center (ERIC) is celebrating its 50th Birthday! First opened on May 15th, 1964 ERIC continues the long tradition of ongoing innovation and enhancement.

Learn more about the history of ERIC here. PDF icon

Showing 1 to 15 of 34 results
Peer reviewed Peer reviewed
Direct linkDirect link
Willoughby, Michael; Holochwost, Steven J.; Blanton, Zane E.; Blair, Clancy B. – Measurement: Interdisciplinary Research and Perspectives, 2014
The primary objective of this article was to critically evaluate the routine use of confirmatory factor analysis (CFA) for representing an individual's performance across a battery of executive function tasks. A conceptual review and statistical reanalysis of N = 10 studies that used CFA methods of EF tasks was undertaken. Despite evidence of…
Descriptors: Executive Function, Cognitive Measurement, Factor Analysis, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Roos, J. Micah – Measurement: Interdisciplinary Research and Perspectives, 2014
The Vanishing Tetrad Test (VTT) (Bollen, Lennox, & Dahly, 2009; Bollen & Ting, 2000; Hipp, Bauer, & Bollen, 2005) is an extension of the Confirmatory Tetrad Analysis (CTA) proposed by Bollen and Ting (Bollen & Ting, 1993). VTT is a powerful tool for detecting model misspecification and can be particularly useful in cases in which…
Descriptors: Measurement, Models, Statistical Analysis, Goodness of Fit
Peer reviewed Peer reviewed
Direct linkDirect link
Bainter, Sierra A.; Bollen, Kenneth A. – Measurement: Interdisciplinary Research and Perspectives, 2014
In measurement theory, causal indicators are controversial and little understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning…
Descriptors: Measurement, Statistical Analysis, Data Interpretation, Causal Models
Peer reviewed Peer reviewed
Direct linkDirect link
Wang, Jue; Engelhard, George, Jr.; Lu, Zhenqiu – Measurement: Interdisciplinary Research and Perspectives, 2014
The authors of the focus article in this issue have emphasized the continuing confusion among some researchers regarding various indicators used in structural equation models (SEMs). Their major claim is that causal indicators are not inherently unstable, and even if they are unstable they are at least not more unstable than other types of…
Descriptors: Structural Equation Models, Measurement, Statistical Analysis, Causal Models
Peer reviewed Peer reviewed
Direct linkDirect link
Howell, Roy D. – Measurement: Interdisciplinary Research and Perspectives, 2014
Building on the work of Bollen (2007) and Bollen & Bauldry (2011), Bainter and Bollen (this issue) clarifies several points of confusion in the literature regarding causal indicator models. This author would certainly agree that the effect indicator (reflective) measurement model is inappropriate for some indicators (such as the social…
Descriptors: Statistical Analysis, Measurement, Causal Models, Data Interpretation
Peer reviewed Peer reviewed
Direct linkDirect link
West, Stephen G.; Grimm, Kevin J. – Measurement: Interdisciplinary Research and Perspectives, 2014
These authors agree with Bainter and Bollen that causal effects represents a useful measurement structure in some applications. The structure of the science of the measurement problem should determine the model; the measurement model should not determine the science. They also applaud Bainter and Bollen's important reminder that the full…
Descriptors: Causal Models, Measurement, Test Theory, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Markus, Keith A. – Measurement: Interdisciplinary Research and Perspectives, 2014
In a series of articles and comments, Kenneth Bollen and his collaborators have incrementally refined an account of structural equation models that (a) model a latent variable as the effect of several observed variables and (b) carry an interpretation of the observed variables as, in some sense, measures of the latent variable that they cause.…
Descriptors: Measurement, Structural Equation Models, Statistical Analysis, Causal Models
Peer reviewed Peer reviewed
Direct linkDirect link
McCoach, D. Betsy; Kenny, David A. – Measurement: Interdisciplinary Research and Perspectives, 2014
In this commentary, Betsy McCoach and David Kenny state they are in general agreement with Bainter and Bollen (this issue) that causal indicators are not inherently unstable. Herein, they outline several similarities and differences between latent variables with reflective and causal indicators. In their examination of the two models, they find…
Descriptors: Causal Models, Statistical Analysis, Measurement
Peer reviewed Peer reviewed
Direct linkDirect link
Cai, Li; Monroe, Scott – Measurement: Interdisciplinary Research and Perspectives, 2013
In this commentary, the authors congratulate Professor Alberto Maydeu-Olivares on his article [EJ1023617: "Goodness-of-Fit Assessment of Item Response Theory Models, Measurement: Interdisciplinary Research and Perspectives," this issue] as it provides a much needed overview on the mathematical underpinnings of the theory behind the…
Descriptors: Goodness of Fit, Item Response Theory, Models, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Maydeu-Olivares, Alberto – Measurement: Interdisciplinary Research and Perspectives, 2013
The article provides an overview of goodness-of-fit assessment methods for item response theory (IRT) models. It is now possible to obtain accurate "p"-values of the overall fit of the model if bivariate information statistics are used. Several alternative approaches are described. As the validity of inferences drawn on the fitted model…
Descriptors: Goodness of Fit, Item Response Theory, Models, Maximum Likelihood Statistics
Peer reviewed Peer reviewed
Direct linkDirect link
Oberski, Daniel L.; Vermunt, Jeroen K. – Measurement: Interdisciplinary Research and Perspectives, 2013
These authors congratulate Albert Maydeu-Olivares on his lucid and timely overview of goodness-of-fit assessment in IRT models, a field to which he himself has contributed considerably in the form of limited information statistics. In this commentary, Oberski and Vermunt focus on two aspects of model fit: (1) what causes there may be of misfit;…
Descriptors: Goodness of Fit, Item Response Theory, Models, Test Bias
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Borkenau, Peter – Measurement: Interdisciplinary Research and Perspectives, 2012
Describing, explaining, and discussing various modern indices of scholarly impact as accomplished by Ruscio, Seaman, D'Oriano, Stremlo, and Mahalchik (this issue) is highly commendable, as such measures get increasingly important in hiring and promotion decisions. The author agrees with almost all points made in the target article, except the…
Descriptors: Periodicals, Correlation, Measurement, Outcome Measures
Peer reviewed Peer reviewed
Direct linkDirect link
Ruscio, John; Seaman, Florence; D'Oriano, Carianne; Stremlo, Elena; Mahalchik, Krista – Measurement: Interdisciplinary Research and Perspectives, 2012
Scholarly impact is studied frequently and used to make consequential decisions (e.g., hiring, tenure, promotion, research support, professional honors), and therefore it is important to measure it accurately. Developments in information technology and statistical methods provide promising new metrics to complement traditional information sources…
Descriptors: Citation Indexes, Citation Analysis, Outcome Measures, Scholarship
Previous Page | Next Page ยป
Pages: 1  |  2  |  3