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Showing 1 to 15 of 48 results Save | Export
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Petrosino, Anthony J.; Mann, Michele J. – Journal of College Science Teaching, 2018
Although data modeling, the employment of statistical reasoning for the purpose of investigating questions about the world, is central to both mathematics and science, it is rarely emphasized in K-16 instruction. The current work focuses on developing thinking about data modeling with undergraduates in general and preservice teachers in…
Descriptors: Undergraduate Students, Preservice Teachers, Mathematical Models, Data
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Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M. – Society for Research on Educational Effectiveness, 2013
Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…
Descriptors: Probability, Scores, Statistical Analysis, Statistical Bias
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Hung, Su-Pin; Chen, Po-Hsi; Chen, Hsueh-Chih – Creativity Research Journal, 2012
Product assessment is widely applied in creative studies, typically as an important dependent measure. Within this context, this study had 2 purposes. First, the focus of this research was on methods for investigating possible rater effects, an issue that has not received a great deal of attention in past creativity studies. Second, the…
Descriptors: Item Response Theory, Creativity, Interrater Reliability, Undergraduate Students
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Wetcher-Hendricks, Debra – Psychological Methods, 2006
With respect to the often-present covariance between error terms of correlated variables, D. W. Zimmerman and R. H. Williams's (1977) adjusted correction for attenuation estimates the strength of the pairwise correlation between true scores without assuming independence of error scores. This article focuses on the derivation and analysis of…
Descriptors: Correlation, Scores, Error Correction, Error of Measurement
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Gardner, P. L. – Journal of Educational Measurement, 1970
Descriptors: Error of Measurement, Mathematical Models, Statistical Analysis, Test Reliability
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Peng, Chao-Ying, J.; Subkoviak, Michael J. – Journal of Educational Measurement, 1980
Huynh (1976) suggested a method of approximating the reliability coefficient of a mastery test. The present study examines the accuracy of Huynh's approximation and also describes a computationally simpler approximation which appears to be generally more accurate than the former. (Author/RL)
Descriptors: Error of Measurement, Mastery Tests, Mathematical Models, Statistical Analysis
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Reichardt, Charles; Gollob, Harry – New Directions for Program Evaluation, 1986
Causal models often omit variables that should be included, use variables that are measured fallibly, and ignore time lags. Such practices can lead to severely biased estimates of effects. The discussion explains these biases and shows how to take them into account. (Author)
Descriptors: Effect Size, Error of Measurement, High Schools, Mathematical Models
van der Burg, Eeke; de Leeuw, Jan – 1987
The estimation of mean and standard errors of the eigenvalues and category quantifications in generalized non-linear canonical correlation analysis (OVERALS) is discussed. Starting points are the delta method equations. The jackknife and bootstrap methods are compared for providing finite difference approximations to the derivatives. Examining the…
Descriptors: Correlation, Elementary Secondary Education, Error of Measurement, Estimation (Mathematics)
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Kristof, Walter – Psychometrika, 1973
Paper is concerned with the hypothesis that two variables have a perfect disattenuated correlation, hence measure the same trait except for errors of measurement. (Author/RK)
Descriptors: Analysis of Variance, Correlation, Error of Measurement, Mathematical Models
Hedges, Larry V. – 1982
Meta-analysis has become an important supplement to traditional methods of research reviewing, although many problems must be addressed by the reviewer who carries out a meta-analysis. These problems include identifying and obtaining appropriate studies, extracting estimates of effect size from the studies, coding or classifying studies, analyzing…
Descriptors: Analysis of Variance, Correlation, Error of Measurement, Mathematical Models
Folsom, Ralph E., Jr. – 1975
In large-scale surveys, it is no longer uncommon for repeated measurements to be obtained from respondents and analyses performed to gauge the magnitiude of nonsampling errors. This is particularly true for periodic surveys and longitudinal surveys where a very large investment in data collection is made. This technical note, aimed at the analysis…
Descriptors: Error of Measurement, Longitudinal Studies, Mathematical Models, Reliability
Lord, Frederic M. – 1973
Faced with a nonstandard, complicated practical problem in statistical inference, the applied statistician sometimes must use asymptotic approximations in order to compute standard errors and confidence intervals and to test hypotheses. This usually requires that he derive formulas for one or more asymptotic sampling variances (and covariances)…
Descriptors: Computer Programs, Data Processing, Error of Measurement, Hypothesis Testing
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Huynh, Huynh; Saunders, Joseph C. – Journal of Educational Measurement, 1980
Single administration (beta-binomial) estimates for the raw agreement index p and the corrected-for-chance kappa index in mastery testing are compared with those based on two test administrations in terms of estimation bias and sampling variability. Bias is about 2.5 percent for p and 10 percent for kappa. (Author/RL)
Descriptors: Comparative Analysis, Error of Measurement, Mastery Tests, Mathematical Models
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Brink, Nicholas E. – Educational and Psychological Measurement, 1972
Study compares the Rasch and the Guttman models of measurement and thus adds to the description of the characteristics of Rasch's logistic model. Such knowledge is of importance in making decisions as to which model and which statistics should be used in evaluations of tests. (Author/CB)
Descriptors: Comparative Analysis, Educational Testing, Error of Measurement, Goodness of Fit
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Horn, John L. – Educational and Psychological Measurement, 1971
Descriptors: Analysis of Variance, Error of Measurement, Hypothesis Testing, Mathematical Models
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