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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 16 to 30 of 248 results
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Peterson, Eric; Welsh, Marilyn C. – Measurement: Interdisciplinary Research and Perspectives, 2014
Research into executive functioning (EF) has indeed grown exponentially across the past few decades, but as the Willoughby et al. critique makes clear, there remain fundamental questions to be resolved. The crux of their argument is built upon an examination of the confirmatory factor analysis (CFA) approach to understanding executive processes.…
Descriptors: Executive Function, Measurement, Factor Analysis, Reliability
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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
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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
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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
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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
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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
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Widaman, Keith F. – Measurement: Interdisciplinary Research and Perspectives, 2014
Latent variable structural equation modeling has become the analytic method of choice in many domains of research in psychology and allied social sciences. One important aspect of a latent variable model concerns the relations hypothesized to hold between latent variables and their indicators. The most common specification of structural equation…
Descriptors: Structural Equation Models, Predictor Variables, Educational Research, Causal Models
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Wyse, Adam E. – Measurement: Interdisciplinary Research and Perspectives, 2013
Construct maps are tools that display how the underlying achievement construct upon which one is trying to set cut-scores is related to other information used in the process of standard setting. This article reviews what construct maps are, uses construct maps to provide a conceptual framework to view commonly used standard-setting procedures (the…
Descriptors: Standard Setting (Scoring), Maps, Cutting Scores, Methods
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Kingston, Neal M.; Tiemann, Gail C.; Loughran, Jessica T. – Measurement: Interdisciplinary Research and Perspectives, 2013
The authors of this article comment on "Construct Maps as a Foundation for Standard Setting," by Adam E. Wyse (this issue) in which Wyse presents construct maps, a visual display of a variety of sources of evidence that support standard-setting decisions, and shows how this approach could be used with a variety of existing…
Descriptors: Standard Setting (Scoring), Maps, Methods, Misconceptions
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McClarty, Katie Larsen – Measurement: Interdisciplinary Research and Perspectives, 2013
The construct map is a promising tool for organizing the data standard-setting panelists interpret. The challenge in applying construct maps to standard-setting procedures will be the judicious selection of data to include within this organizing framework. Therefore, this commentary focuses on decisions about what to include in the construct map.…
Descriptors: Standard Setting (Scoring), Maps, Validity, Evidence
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Popham, W. James – Measurement: Interdisciplinary Research and Perspectives, 2013
The author recalls that as a child, he grooved on teeter-totters. Also known as a seesaw, a teeter-totter is a long, narrow board that's elevated with a pivot point in the middle so that as one end goes down the other end goes up. When going up or going down, sometimes quite rapidly, teeter-totters can provide their two riders with some…
Descriptors: Standard Setting (Scoring), Maps, Performance, Standards
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Skaggs, Gary – Measurement: Interdisciplinary Research and Perspectives, 2013
The construct map is a particularly good way to approach instrument development, and this author states that he was delighted to read Adam Wyse's thoughts about how to use construct maps for standard setting. For a number of popular standard-setting methods, Wyse shows how typical feedback to panelists fits within a construct map framework.…
Descriptors: Standard Setting (Scoring), Maps, Test Construction, Measurement
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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
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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
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Kane, Michael T.; Tannenbaum, Richard J. – Measurement: Interdisciplinary Research and Perspectives, 2013
The authors observe in this commentary that construct maps can help standard-setting panels to make realistic and internally consistent recommendations for performance-level descriptions (PLDs) and cut-scores, but the benefits may not be realized if policymakers do not fully understand the rationale for the recommendations provided by the…
Descriptors: Standard Setting (Scoring), Maps, Cutting Scores, Policy
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