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Villafañe, Sachel M.; Lewis, Jennifer E. – Chemistry Education Research and Practice, 2016
Decisions about instruction, research, or policy often require the interpretation of student assessment scores. Increasingly, attitudinal variables are included in an assessment strategy, and it is important to ensure that interpretations of students' attitudinal status are based on instrument scores that apply similarly for diverse students. In…
Descriptors: Scientific Attitudes, Introductory Courses, Chemistry, Attitude Measures
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Keselman, H. J.; Miller, Charles W.; Holland, Burt – Psychological Methods, 2011
There have been many discussions of how Type I errors should be controlled when many hypotheses are tested (e.g., all possible comparisons of means, correlations, proportions, the coefficients in hierarchical models, etc.). By and large, researchers have adopted familywise (FWER) control, though this practice certainly is not universal. Familywise…
Descriptors: Validity, Statistical Significance, Probability, Computation
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Coffman, Donna L. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Mediation is usually assessed by a regression-based or structural equation modeling (SEM) approach that we refer to as the classical approach. This approach relies on the assumption that there are no confounders that influence both the mediator, "M", and the outcome, "Y". This assumption holds if individuals are randomly…
Descriptors: Structural Equation Models, Simulation, Regression (Statistics), Probability
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Kazi, Mansoor A. F.; Pagkos, Brian; Milch, Heidi A. – Research on Social Work Practice, 2011
Objectives: The purpose of this study was to develop a realist evaluation paradigm in social work evidence-based practice. Method: Wraparound (at Gateway-Longview Inc., New York) used a reliable outcome measure and an electronic database to systematically collect and analyze data on the interventions, the client demographics and circumstances, and…
Descriptors: Intervals, Data Analysis, Social Work, Sample Size
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Cribbie, Robert A. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
Researchers conducting structural equation modeling analyses rarely, if ever, control for the inflated probability of Type I errors when evaluating the statistical significance of multiple parameters in a model. In this study, the Type I error control, power and true model rates of famsilywise and false discovery rate controlling procedures were…
Descriptors: Probability, Inferences, Structural Equation Models, Statistical Significance