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Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…
Descriptors: Error of Measurement, Monte Carlo Methods, Data Collection, Simulation
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Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…
Descriptors: Error of Measurement, Correlation, Simulation, Bayesian Statistics
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DeVoe, Jill Fleury; Bauer, Lynn – National Center for Education Statistics, 2010
Student victimization in schools is a major concern of educators, policymakers, administrators, parents, and students. Understanding the scope of the criminal victimization of students, as well as the factors associated with it, is an essential step in developing solutions to address the issues of school crime and violence. This report uses data…
Descriptors: Weapons, Crime, Bullying, Criminals
Robey, Randall R.; Barcikowski, Robert S. – 1987
The mixed model analysis of variance assumes a mathematical property known as sphericity. Several preliminary tests have been proposed to detect departures from the sphericity assumption. The logic of the preliminary testing procedure is to conduct the mixed model analysis of variance if the preliminary test suggests that the sphericity assumption…
Descriptors: Analysis of Variance, Error of Measurement, Hypothesis Testing, Mathematical Models
Wolfle, Lee M.; Ethington, Corinna A. – 1984
To correct for the effects of measurement error on structural parameter estimates, many researchers are now estimating models of educational achievement with LISREL. In order to estimate such models it is desirable to obtain multiple manifest measures of the latent constructs. Many researchers restrict their models to two manifest measures per…
Descriptors: Academic Achievement, Error of Measurement, Estimation (Mathematics), Goodness of Fit
Hummel, Thomas J.; Johnston, Charles B. – 1986
This study investigated seven methods for analyzing multivariate group differences. Bonferroni t statistics, multivariate analysis of variance (MANOVA) followed by analysis of variance (ANOVA), and five other methods were studied using Monte Carlo methods. Methods were compared with respect to (1) experimentwise error rate; (2) power; (3) number…
Descriptors: Analysis of Variance, Comparative Analysis, Correlation, Differences
Carlson, James E.; Spray, Judith A. – 1986
This paper discussed methods currently under study for use with multiple-response data. Besides using Bonferroni inequality methods to control type one error rate over a set of inferences involving multiple response data, a recently proposed methodology of plotting the p-values resulting from multiple significance tests was explored. Proficiency…
Descriptors: Cutting Scores, Data Analysis, Difficulty Level, Error of Measurement
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Kaufman, Phillip; Bradbury, Denise – National Center for Education Statistics, 1992
The National Education Longitudinal Study of 1988 (NELS:88) is a large-scale, national longitudinal study designed and sponsored by the National Center for Education Statistics (NCES), with support from other government agencies. Beginning in the spring of 1988 with a cohort of eighth graders (25,000) attending public and private schools across…
Descriptors: At Risk Students, Longitudinal Studies, Statistical Analysis, Demography