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Philipp Sterner; Florian Pargent; Dominik Deffner; David Goretzko – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent…
Descriptors: Structural Equation Models, Causal Models, Measurement, Predictor Variables
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
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
Lei, Wu; Qing, Fang; Zhou, Jin – International Journal of Distance Education Technologies, 2016
There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…
Descriptors: Causal Models, Attribution Theory, Correlation, Evaluation Methods
Roberts, Ros; Johnson, Philip – Curriculum Journal, 2015
Recent school science curriculum developments in many countries emphasise that scientists derive evidence for their claims through different approaches; that such practices are bound up with disciplinary knowledge; and that the quality of data should be appreciated. This position paper presents an understanding of the validity of data as a set of…
Descriptors: Educational Quality, Data, Concept Mapping, Scientific Concepts
Ting, Choo-Yee; Sam, Yok-Cheng; Wong, Chee-Onn – Computers & Education, 2013
Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…
Descriptors: Concept Formation, Bayesian Statistics, Inquiry, Science Instruction