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Showing 1 to 15 of 74 results Save | Export
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Young, Cristobal – Sociological Methods & Research, 2019
The commenter's proposal may be a reasonable method for addressing uncertainty in predictive modeling, where the goal is to predict "y." In a treatment effects framework, where the goal is causal inference by conditioning-on-observables, the commenter's proposal is deeply flawed. The proposal (1) ignores the definition of…
Descriptors: Causal Models, Predictor Variables, Research Methodology, Ambiguity (Context)
Kaplan, Avi; Cromley, Jennifer; Perez, Tony; Dai, Ting; Mara, Kyle; Balsai, Michael – Educational Researcher, 2020
In this commentary, we complement other constructive critiques of educational randomized control trials (RCTs) by calling attention to the commonly ignored role of context in causal mechanisms undergirding educational phenomena. We argue that evidence for the central role of context in causal mechanisms challenges the assumption that RCT findings…
Descriptors: Context Effect, Educational Research, Randomized Controlled Trials, Causal Models
Kaplan, Avi; Cromley, Jennifer; Perez, Tony; Dai, Ting; Mara, Kyle; Balsai, Michael – Grantee Submission, 2020
In this commentary, we complement other constructive critiques of educational randomized control trials (RCTs) by calling attention to the commonly ignored role of context in causal mechanisms undergirding educational phenomena. We argue that evidence for the central role of context in causal mechanisms challenges the assumption that RCT findings…
Descriptors: Context Effect, Educational Research, Randomized Controlled Trials, Causal Models
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Howell, Roy D.; Breivik, Einar – Measurement: Interdisciplinary Research and Perspectives, 2016
In this article, Roy Howell, and Einar Breivik, congratulate Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M., for their work (2016) "Omission of Causal Indicators: Consequences and Implications for Measurement," Measurement: Interdisciplinary Research and Perspectives, 14(3), 75-97. doi:10.1080/15366367.2016.1205935. They call it…
Descriptors: Causal Models, Measurement, Predictor Variables
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Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M. – Measurement: Interdisciplinary Research and Perspectives, 2016
The authors begin this brief rejoinder by thanking all the authors who took time to provide comments on their work, which appeared in "Measurement: Interdisciplinary Research and Perspectives" v14 n3 2016. All commentaries appear to suggest that causal indicators cannot be used for measurement but differ in how strongly this conclusion…
Descriptors: Causal Models, Measurement, Research Needs
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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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Cadogan, John W.; Lee, Nick – Measurement: Interdisciplinary Research and Perspectives, 2016
In this commentary from Issue 14, n3, authors John Cadogan and Nick Lee applaud the paper by Aguirre-Urreta, Rönkkö, and Marakas "Measurement: Interdisciplinary Research and Perspectives", 14(3), 75-97 (2016), since their explanations and simulations work toward demystifying causal indicator models, which are often used by scholars…
Descriptors: Causal Models, Measurement, Validity, Statistical Analysis
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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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Dawid, A. Philip; Faigman, David L.; Fienberg, Stephen E. – Sociological Methods & Research, 2015
We welcome Professor Pearl's comment on our original article, Dawid et al. Our focus there on the distinction between the "Effects of Causes" (EoC) and the "Causes of Effects" (CoE) concerned two fundamental problems, one a theoretical challenge in statistics and the other a practical challenge for trial courts. In this…
Descriptors: Causal Models, Influences, Evidence, Statistics
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Maggin, Daniel M. – Journal of Behavioral Education, 2015
This paper responds to the commentary provided by Hitchcock et al. "(Journal of Behavioral Education," 2015) which provides updated information on the procedures for considering the generality of an intervention using the "What Works Clearinghouse" (WWC) single-case guidelines. The new information provided by Hitchcock et al.…
Descriptors: Scoring Rubrics, Generalization, Research Methodology, Review (Reexamination)
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