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Porter, Kristin E. – Journal of Research on Educational Effectiveness, 2018
Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs) are statistical…
Descriptors: Statistical Analysis, Program Effectiveness, Intervention, Hypothesis Testing
Porter, Kristin E. – Grantee Submission, 2017
Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs) are statistical…
Descriptors: Statistical Analysis, Program Effectiveness, Intervention, Hypothesis Testing
Porter, Kristin E. – MDRC, 2016
In education research and in many other fields, researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple…
Descriptors: Statistical Analysis, Program Effectiveness, Intervention, Hypothesis Testing
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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
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McGraw, Kenneth O.; And Others – Journal of Consulting and Clinical Psychology, 1994
Suggest practical procedure for estimating number of subjects that need to be screened to obtain sample of fixed size that meets multiple correlated criteria. Procedure described is based on fact that least-squares regression provides good quadratic fit for Monte Carlo estimates of multivariate probabilities when they are plotted as function of…
Descriptors: Measurement Techniques, Monte Carlo Methods, Research Methodology, Research Problems
Elliott, Ronald S.; Barcikowski, Robert S. – 1993
This Monte Carlo study examines whether, given various numbers of variables, treatments, and sample sizes, in a one-way multivariate analysis of variance, Type I error rates of the test approximations provided by the BMDP program, the Statistical Analysis System (SAS), and the Statistical Package for the Social Sciences (SPSS) for Roy's largest…
Descriptors: Analysis of Variance, Computer Simulation, Estimation (Mathematics), Monte Carlo Methods
Williams, Janice E. – 1987
A Monte Carlo study was done to determine the adequate sample size for quasi-experimental regression studies, which compare regression lines for two groups and estimate their point of intersection. Populations of 1,000 subjects in each of two groups were constructed (using random normal deviates) to yield equivalent regression lines of opposite…
Descriptors: Computer Simulation, Estimation (Mathematics), Monte Carlo Methods, Quasiexperimental Design
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
Tryon, Warren W. – 1984
A normally distributed data set of 1,000 values--ranging from 50 to 150, with a mean of 50 and a standard deviation of 20--was created in order to evaluate the bootstrap method of repeated random sampling. Nine bootstrap samples of N=10 and nine more bootstrap samples of N=25 were randomly selected. One thousand random samples were selected from…
Descriptors: Computer Simulation, Estimation (Mathematics), Higher Education, Monte Carlo Methods
Blair, R. Clifford; Higgins, James J. – 1985
Monte Carlo methods were employed to assess the relative power of the paired samples t test and Wilcoxon's signed-ranks test under ten population shapes. Results of the study indicated that: (1) each of the two statistics was more powerful than the other in given situations; (2) the power advantages of the t test under normal theory were small;…
Descriptors: Estimation (Mathematics), Literature Reviews, Measurement Techniques, Monte Carlo Methods
Tucker, Ledyard R.; And Others – 1986
A Monte Carlo study of five indices of dimensionality of binary items used a computer model that allowed sampling of both items and people. Five parameters were systematically varied in a factorial design: (1) number of common factors from one to five; (2) number of items, including 20, 30, 40, and 60; (3) sample sizes of 125 and 500; (4) nearly…
Descriptors: Correlation, Difficulty Level, Educational Research, Expectancy Tables
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