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Ellison, George T. H. – Journal of Statistics and Data Science Education, 2021
Temporality-driven covariate classification had limited impact on: the specification of directed acyclic graphs (DAGs) by 85 novice analysts (medical undergraduates); or the risk of bias in DAG-informed multivariable models designed to generate causal inference from observational data. Only 71 students (83.5%) managed to complete the…
Descriptors: Statistics Education, Medical Education, Undergraduate Students, Graphs
Deke, John; Wei, Thomas; Kautz, Tim – National Center for Education Evaluation and Regional Assistance, 2017
Evaluators of education interventions are increasingly designing studies to detect impacts much smaller than the 0.20 standard deviations that Cohen (1988) characterized as "small." While the need to detect smaller impacts is based on compelling arguments that such impacts are substantively meaningful, the drive to detect smaller impacts…
Descriptors: Intervention, Educational Research, Research Problems, Statistical Bias
Bernard, Robert M.; Borokhovski, Eugene; Schmid, Richard F.; Tamim, Rana M. – Journal of Computing in Higher Education, 2014
This article contains a second-order meta-analysis and an exploration of bias in the technology integration literature in higher education. Thirteen meta-analyses, dated from 2000 to 2014 were selected to be included based on the questions asked and the presence of adequate statistical information to conduct a quantitative synthesis. The weighted…
Descriptors: Meta Analysis, Bias, Technology Integration, Higher Education
Shoemaker, David M. – 1972
Investigated empirically through post mortem item-examinee sampling was the feasibility of the jackknife as a procedure for approximating standard errors of estimate in multiple matrix sampling. The parameters estimated were the mean test score, second through fourth central moments of the test score distribution, and the variance of the item…
Descriptors: Error of Measurement, Error Patterns, Item Sampling, Matrices

Calkins, Dick S. – 1974
The mathematical derivation of the statistics used for inference in some linear models assumes that the values of the independent variables are measured without error. This assumption is often disregarded when these models are utilized in research. This study is an investigation of the consequences of the violation of this assumption for one…
Descriptors: Analysis of Covariance, Computer Programs, Error of Measurement, Error Patterns

Kleinke, David J. – Applied Psychological Measurement, 1979
Lord's, Millman's and Saupe's methods of approximating the standard error of measurement are reviewed. Through an empirical demonstration involving 200 university classroom tests, all three approximations are shown to be biased. (Author/JKS)
Descriptors: Error of Measurement, Error Patterns, Higher Education, Mathematical Formulas
Shoemaker, David M. – 1972
Described and listed herein with concomitant sample input and output is the Fortran IV program which estimates parameters and standard errors of estimate per parameters for parameters estimated through multiple matrix sampling. The specific program is an improved and expanded version of an earlier version. (Author/BJG)
Descriptors: Computer Oriented Programs, Computer Programs, Error of Measurement, Error Patterns
Linn, Robert L.; Werts, Charles E. – 1971
Failure to consider errors of measurement when using partial correlation or analysis of covariance techniques can result in erroneous conclusions. Certain aspects of this problem are discussed and particular attention is given to issues raised in a recent article by Brewar, Campbell, and Crano. (Author)
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Correlation
Smith, Richard M. – 1983
Previous studies of test item bias have investigated how different groups of examinees perform differently on a given set of items. These studies imply that examinees should be treated in a certain way because they are of a particular sex or race rather than as individuals in their own right, but it is unrealistic and unfair to assume such an…
Descriptors: Academic Ability, Error of Measurement, Error Patterns, Higher Education