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Yang, Shitao; Black, Ken – Teaching Statistics: An International Journal for Teachers, 2019
Summary Employing a Wald confidence interval to test hypotheses about population proportions could lead to an increase in Type I or Type II errors unless the hypothesized value, p0, is used in computing its standard error rather than the sample proportion. Whereas the Wald confidence interval to estimate a population proportion uses the sample…
Descriptors: Error Patterns, Evaluation Methods, Error of Measurement, Measurement Techniques
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Xie, Zilong; Reetzke, Rachel; Chandrasekaran, Bharath – Journal of Speech, Language, and Hearing Research, 2019
Purpose: Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and language processing. Here, we highlight the practical application of machine learning (ML)-based approaches to analyzing speech-evoked neurophysiological responses. Method: Two categories of ML-based…
Descriptors: Speech Language Pathology, Intervention, Communication Problems, Speech Impairments
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Bishara, Anthony J.; Hittner, James B. – Educational and Psychological Measurement, 2015
It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared…
Descriptors: Research Methodology, Monte Carlo Methods, Correlation, Simulation
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Kim, Eun Sook; Yoon, Myeongsun; Lee, Taehun – Educational and Psychological Measurement, 2012
Multiple-indicators multiple-causes (MIMIC) modeling is often used to test a latent group mean difference while assuming the equivalence of factor loadings and intercepts over groups. However, this study demonstrated that MIMIC was insensitive to the presence of factor loading noninvariance, which implies that factor loading invariance should be…
Descriptors: Test Items, Simulation, Testing, Statistical Analysis
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Wilcox, Rand R. – Educational and Psychological Measurement, 2006
For two random variables, X and Y, let D = X - Y, and let theta[subscript x], theta[subscript y], and theta[subscript d] be the corresponding medians. It is known that the Wilcoxon-Mann-Whitney test and its modern extensions do not test H[subscript o] : theta[subscript x] = theta[subscript y], but rather, they test H[subscript o] : theta[subscript…
Descriptors: Scores, Inferences, Comparative Analysis, Statistical Analysis
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Zimmerman, Donald W. – Psicologica: International Journal of Methodology and Experimental Psychology, 2004
It is well known that the two-sample Student t test fails to maintain its significance level when the variances of treatment groups are unequal, and, at the same time, sample sizes are unequal. However, introductory textbooks in psychology and education often maintain that the test is robust to variance heterogeneity when sample sizes are equal.…
Descriptors: Sample Size, Nonparametric Statistics, Probability, Statistical Analysis
Brennan, Robert L.; Kane, Michael F. – 1975
When classes are the units of analyses, estimates of the reliability of class means are needed. Using classical test theory it is difficult to treat this problem adequately. Generalizability theory, however, provides a natural framework for dealing with the problem. Each of four possible formulas for the generalizability of class means is derived…
Descriptors: Analysis of Variance, Classes (Groups of Students), Correlation, Error Patterns
Russ-Eft, Darlene F.; Brandt, David A. – 1982
Error profiles for the Fall Enrollment Survey of the Higher Education General Information Survey (HEGIS) were developed as part of an assessment of the quality of survey data. Three statistics were of particular interest: the count of full-time equivalent students, the breakdown by race/ethnicity, and the count of unclassified students. Attention…
Descriptors: Data Analysis, Data Collection, Enrollment Trends, Error Patterns
Tatsuoka, Kikumi K. – 1982
This study introduced a probabilistic model utilizing item response theory (IRT) for dealing with a variety of misconceptions. The model can be used for evaluating the transition behavior of error types, advancement of learning stages, or the stability and persistence of particular misconceptions. Moreover, it apparently can be used for relating…
Descriptors: Adaptive Testing, Elementary Secondary Education, Error Patterns, Evaluation Methods
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Blasiak, Wladyslaw – Physics Education, 1983
Classifies errors as either systematic or blunder and uncertainties as either systematic or random. Discusses use of error/uncertainty analysis in direct/indirect measurement, describing the process of planning experiments to ensure lowest possible uncertainty. Also considers appropriate level of error analysis for high school physics students'…
Descriptors: Error of Measurement, Error Patterns, High Schools, Mathematics Skills
Tella, Alfred – 1976
For one important set of data, namely, the data on unemployment and employment collected by the Census Bureau in its monthly Current Population Survey (CPS), some information on nonsampling error is available that can be used to evaluate the regularly reported labor force figures. The paper is concerned with the nonsampling error that relates…
Descriptors: Business Cycles, Census Figures, Data Analysis, Employment Level
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection