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Leslie Rutkowski; David Rutkowski – Journal of Creative Behavior, 2025
The Programme for International Student Assessment (PISA) introduced creative thinking as an innovative domain in 2022. This paper examines the unique methodological issues in international assessments and the implications of measuring creative thinking within PISA's framework, including stratified sampling, rotated form designs, and a distinct…
Descriptors: Creativity, Creative Thinking, Measurement, Sampling
Kraft, Matthew A. – Annenberg Institute for School Reform at Brown University, 2019
Researchers commonly interpret effect sizes by applying benchmarks proposed by Cohen over a half century ago. However, effects that are small by Cohen's standards are large relative to the impacts of most field-based interventions. These benchmarks also fail to consider important differences in study features, program costs, and scalability. In…
Descriptors: Data Interpretation, Effect Size, Intervention, Benchmarking
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
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
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
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
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
Cope, Bill; Kalantzis, Mary – Open Review of Educational Research, 2015
In this article, we argue that big data can offer new opportunities and roles for educational researchers. In the traditional model of evidence-gathering and interpretation in education, researchers are independent observers, who pre-emptively create instruments of measurement, and insert these into the educational process in specialized times and…
Descriptors: Data Collection, Data Interpretation, Evidence, Educational Research
Boerema, Albert J. – Journal of School Choice, 2009
Using student achievement data from British Columbia, Canada, this study is an exploration of the differences that lie within the private school sector using hierarchical linear modeling to analyze the data. The analysis showed that when controlling for language, parents' level of educational attainment, and prior achievement, the private school…
Descriptors: Private Schools, School Choice, Foreign Countries, Comparative Analysis
Luhmann, Christian C.; Ahn, Woo-kyoung – Psychological Review, 2007
Dealing with alternative causes is necessary to avoid making inaccurate causal inferences from covariation data. However, information about alternative causes is frequently unavailable, rendering them unobserved. The current article reviews the way in which current learning models deal, or could deal, with unobserved causes. A new model of causal…
Descriptors: Inferences, Learning Processes, Probability, Models