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Direct linkCox, Bradley E.; McIntosh, Kadian; Reason, Robert D.; Terenzini, Patrick T. – Review of Higher Education, 2014
Nearly all quantitative analyses in higher education draw from incomplete datasets-a common problem with no universal solution. In the first part of this paper, we explain why missing data matter and outline the advantages and disadvantages of six common methods for handling missing data. Next, we analyze real-world data from 5,905 students across…
Descriptors: Data Analysis, Statistical Inference, Research Problems, Computation
Bollen, Kenneth A. – Psychological Methods, 2007
R. D. Howell, E. Breivik, and J. B. Wilcox (2007) have argued that causal (formative) indicators are inherently subject to interpretational confounding. That is, they have argued that using causal (formative) indicators leads the empirical meaning of a latent variable to be other than that assigned to it by a researcher. Their critique of causal…
Descriptors: Researchers, Structural Equation Models, Formative Evaluation, Transformative Learning
Peer reviewedHumphreys, Lloyd G.; And Others – Applied Psychological Measurement, 1993
Two articles discuss the controversy about the relationship between reliability and the power of significance tests in response to the discussion of Donald W. Zimmerman, Richard H. Williams, and Bruno D. Zumbo. Lloyd G. Humphreys emphasizes the differences between what statisticians can do and constraints on researchers. Zimmerman, Williams, and…
Descriptors: Error of Measurement, Individual Differences, Power (Statistics), Research Methodology
