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Pinder, Jonathan P. – Decision Sciences Journal of Innovative Education, 2014
Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…
Descriptors: Data Collection, Data Analysis, Regression (Statistics), Predictive Measurement
Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2015
Person-fit assessment may help the researcher to obtain additional information regarding the answering behavior of persons. Although several researchers examined person fit, there is a lack of research on person-fit assessment for mixed-format tests. In this article, the lz statistic and the ?2 statistic, both of which have been used for tests…
Descriptors: Test Format, Goodness of Fit, Item Response Theory, Bayesian Statistics
McCoach, D. Betsy; Adelson, Jill L. – Gifted Child Quarterly, 2010
This article provides a conceptual introduction to the issues surrounding the analysis of clustered (nested) data. We define the intraclass correlation coefficient (ICC) and the design effect, and we explain their effect on the standard error. When the ICC is greater than 0, then the design effect is greater than 1. In such a scenario, the…
Descriptors: Statistical Significance, Error of Measurement, Correlation, Data Analysis
Murphy, Richard; Weinhardt, Felix – Centre for Economic Performance, 2013
We find an individual's rank within their reference group has effects on later objective outcomes. To evaluate the impact of local rank, we use a large administrative dataset tracking over two million students in England from primary through to secondary school. Academic rank within primary school has sizable, robust and significant effects on…
Descriptors: Foreign Countries, Class Rank, Progress Monitoring, Effect Size
Schochet, Peter Z. – National Center for Education Evaluation and Regional Assistance, 2009
This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…
Descriptors: Control Groups, Causal Models, Statistical Significance, Computation
Yuan, Ke-Hai; Bentler, Peter M. – Educational and Psychological Measurement, 2004
In mean and covariance structure analysis, the chi-square difference test is often applied to evaluate the number of factors, cross-group constraints, and other nested model comparisons. Let model M[a] be the base model within which model M[b] is nested. In practice, this test is commonly used to justify M[b] even when M[a] is misspecified. The…
Descriptors: Statistical Significance, Item Response Theory, Computation, Statistical Analysis

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