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Kane, Mike – Measurement: Interdisciplinary Research and Perspectives, 2017
In the article "Rethinking Traditional Methods of Survey Validation" Andrew Maul describes a minimalist validation methodology for survey instruments, which he suggests is widely used in some areas of psychology and then critiques this methodology empirically and conceptually. He provides a reduction ad absurdum argument by showing that…
Descriptors: Surveys, Validity, Psychological Characteristics, Methods
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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
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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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Rhemtulla, Mijke; van Bork, Riet; Borsboom, Denny – Measurement: Interdisciplinary Research and Perspectives, 2015
In this commentary, Mijke Rhemtulla, Riet van Bork, and Denny Borsboom write that they were delighted to see Bainter and Bollen's paper as a focus article in "Measurement." In their view, psychological researchers who use SEM rely too reflexively on reflective measurement, without sufficiently considering whether their indicators are…
Descriptors: Causal Models, Measurement, Data Interpretation, Statistical Data
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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
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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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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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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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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
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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