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Hurtz, Gregory M.; Jones, J. Patrick; Jones, Christian N. – Applied Psychological Measurement, 2008
This study compares the efficacy of different strategies for translating item-level, proportion-correct standard-setting judgments into a theta-metric test cutoff score for use with item response theory (IRT) scoring, using Monte Carlo methods. Simulated Angoff-type ratings, consisting of 1,000 independent 75 Item x13 Rater matrices, were…
Descriptors: Monte Carlo Methods, Measures (Individuals), Item Response Theory, Standard Setting
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Blair, R. Clifford; Higgins, James J. – Psychological Bulletin, 1985
Uses Monte Carlo methods to assess the relative power of the paired samples t test and Wilcoxon's signed-ranks test under 10 population shapes. Concludes that, insofar as these two statistics are concerned, the often-repeated claim that parametric tests are more powerful than nonparametric tests is not justified. (Author/CB)
Descriptors: Comparative Analysis, Monte Carlo Methods, Nonparametric Statistics, Sample Size
Olejnik, Stephen; Algina, James – 1987
The purpose of this study was to develop a single procedure for comparing population variances which could be used for distribution forms. Bootstrap methodology was used to estimate the variability of the sample variance statistic when the population distribution was normal, platykurtic and leptokurtic. The data for the study were generated and…
Descriptors: Comparative Analysis, Estimation (Mathematics), Measurement Techniques, Monte Carlo Methods
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Liew, Chong K.; And Others – Journal of the American Society for Information Science, 1985
Introduces two data distortion methods (Frequency-Imposed Distortion, Frequency-Imposed Probability Distortion) and uses a Monte Carlo study to compare their performance with that of other distortion methods (Point Distortion, Probability Distortion). Indications that data generated by these two methods produce accurate statistics and protect…
Descriptors: College Faculty, Comparative Analysis, Data Processing, Monte Carlo Methods
Robey, Randall R.; Barcikowski, Robert S. – 1986
This paper reports the results of a Monte Carlo investigation of Type I errors in the single group repeated measures design where multiple measures are collected from each observational unit at each measurement occasion. The Type I error of three multivariate tests were examined. These were the doubly multivariate F test, the multivariate mixed…
Descriptors: Analysis of Variance, Behavioral Science Research, Comparative Analysis, Hypothesis Testing
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