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Lee, Nick; Chamberlain, Laura – Measurement: Interdisciplinary Research and Perspectives, 2016
Aguirre-Urreta, Rönkkö, and Marakas' (2016) paper in "Measurement: Interdisciplinary Research and Perspectives" (hereafter referred to as ARM2016) is an important and timely piece of scholarship, in that it provides strong analytic support to the growing theoretical literature that questions the underlying ideas behind causal and…
Descriptors: Measurement, Causal Models, Formative Evaluation, Evaluation Methods
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Wang, Jue; Engelhard, George, Jr. – Measurement: Interdisciplinary Research and Perspectives, 2016
The authors of the focus article describe an important issue related to the use and interpretation of causal indicators within the context of structural equation modeling (SEM). In the focus article, the authors illustrate with simulated data the effects of omitting a causal indicator. Since SEMs are used extensively in the social and behavioral…
Descriptors: Structural Equation Models, Measurement, Causal Models, Construct Validity
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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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Guyon, Hervé; Tensaout, Mouloud – Measurement: Interdisciplinary Research and Perspectives, 2015
This article is a commentary on the Focus Article, "Interpretational Confounding or Confounded Interpretations of Causal Indicators?" and a commentary that was published in issue 12(4) 2014 of "Measurement: Interdisciplinary Research & Perspectives". The authors challenge two claims: (a) Bainter and Bollen argue that the…
Descriptors: Causal Models, Measurement, Data Interpretation, Structural Equation Models
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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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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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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
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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
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Hancock, Gregory R. – Measurement: Interdisciplinary Research and Perspectives, 2009
As Rupp and Templin (2008) stated directly, diagnostic classification methods "are confirmatory in nature." Methods, though, are neither inherently confirmatory nor exploratory. Diagnostic classification modeling, with its analytical and computational obstacles eventually yielding as a comprehensive and potent discipline emerges, will…
Descriptors: Structural Equation Models, Test Items, Models, Diagnostic Tests
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Willoughby, Michael T.; Sideris, John – Measurement: Interdisciplinary Research and Perspectives, 2007
In this article, the authors comment on Nesselroade, Gerstorf, Hardy, and Ram's efforts (this issue) to grapple with the challenge of accommodating idiographic assessment as it pertains to measurement invariance (MI). Although the authors are in complete agreement with the motivation for Nesselroade et al.'s work, the authors have concerns about…
Descriptors: Measurement Techniques, Evaluation, Motivation, Individual Differences