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Physical Sciences and Mathematics Commons™
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Articles 1 - 9 of 9
Full-Text Articles in Physical Sciences and Mathematics
Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, Paul M. Brown, Justin A. Ezekowitz
Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, Paul M. Brown, Justin A. Ezekowitz
Journal of Modern Applied Statistical Methods
Composite endpoints are a popular outcome in controlled studies. However, the required sample size is not easily obtained due to the assortment of outcomes, correlations between them and the way in which the composite is constructed. Data simulations are required. A macro is developed that enables sample size and power estimation.
Outlier Impact And Accommodation On Power, Hongjing Liao, Yanju Li, Gordon P. Brooks
Outlier Impact And Accommodation On Power, Hongjing Liao, Yanju Li, Gordon P. Brooks
Journal of Modern Applied Statistical Methods
The outliers’ influence on power rates in ANOVA and Welch tests at various conditions was examined and compared with the effectiveness of nonparametric methods and Winsorizing in minimizing the impact of outliers. Results showed that, considering both power and Type I error, a nonparametric test is the safest choice to control the inflation of Type I error with a decent sample size and yield relatively high power.
Comparison Of Some Multivariate Nonparametric Tests In Profile Analysis To Repeated Measurements, Mehrdad Vossoughi, Shila Shahvali, Erfan Sadeghi
Comparison Of Some Multivariate Nonparametric Tests In Profile Analysis To Repeated Measurements, Mehrdad Vossoughi, Shila Shahvali, Erfan Sadeghi
Journal of Modern Applied Statistical Methods
Through Monte Carlo simulations, the performance of six multivariate nonparametric tests for testing the hypothesis of parallelism in profile analysis was studied. In conclusion, the tests based on ranks were as efficient as Hotelling's T2 under multivariate normal distribution. For the heavy tailed distribution, the tests based on signs performed best.
A Monte Carlo Comparison Of Robust Manova Test Statistics, Holmes Finch, Brian French
A Monte Carlo Comparison Of Robust Manova Test Statistics, Holmes Finch, Brian French
Journal of Modern Applied Statistical Methods
Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform well with respect to Type I error and power. There are several alternative test statistics that can be considered including robust statistics and the use of the structural equation modeling (SEM) framework. This simulation study focused on comparing the performance of the P test …
Comparison Of The T Vs. Wilcoxon Signed-Rank Test For Likert Scale Data And Small Samples, Gary E. Meek, Ceyhun Ozgur, Kenneth Dunning
Comparison Of The T Vs. Wilcoxon Signed-Rank Test For Likert Scale Data And Small Samples, Gary E. Meek, Ceyhun Ozgur, Kenneth Dunning
Journal of Modern Applied Statistical Methods
The one sample t-test is compared with the Wilcoxon Signed-Rank test for identical data sets representing various Likert scales. An empirical approach is used with simulated data. Comparisons are based on observed error rates for 27,850 data sets. Recommendations are provided.
Jmasm 26: Hettmansperger And Mckean Linear Model Aligned Rank Test For The Single Covariate And One-Way Ancova Case (Sas), Paul A. Nakonezny, Robert D. Shull
Jmasm 26: Hettmansperger And Mckean Linear Model Aligned Rank Test For The Single Covariate And One-Way Ancova Case (Sas), Paul A. Nakonezny, Robert D. Shull
Journal of Modern Applied Statistical Methods
A SAS program (SAS 9.1.3 release, SAS Institute, Cary, N.C.) is presented to implement the Hettmansperger and McKean (1983) linear model aligned rank test (nonparametric ANCOVA) for the single covariate and one-way ANCOVA case. As part of this program, SAS code is also provided to derive the residuals from the regression of Y on X (which is step 1 in the Hettmansperger and McKean procedure) using either ordinary least squares regression (proc reg in SAS) or robust regression with MM estimation (proc robustreg in SAS).
A Conversation With R. Clifford Blair On The Occasion Of His Retirement, Shlomo S. Sawilowsky
A Conversation With R. Clifford Blair On The Occasion Of His Retirement, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
An interview was conducted on 23 November 2003 with R. Clifford Blair on the occasion on his retirement from the University of South Florida. This article is based on that interview. Biographical sketches and images of members of his academic genealogy are provided.
A Nonparametric Fitted Test For The Behrens-Fisher Problem, Terry Hyslop, Paul J. Lupinacci
A Nonparametric Fitted Test For The Behrens-Fisher Problem, Terry Hyslop, Paul J. Lupinacci
Journal of Modern Applied Statistical Methods
A nonparametric test for the Behrens-Fisher problem that is an extension of a test proposed by Fligner and Policello was developed. Empirical level and power estimates of this test are compared to those of alternative nonparametric and parametric tests through simulations. The results of our test were better than or comparable to all tests considered.
Parametric Analyses In Randomized Clinical Trials, Vance W. Berger, Clifford E. Lunneborg, Michael D. Ernst, Jonathan G. Levine
Parametric Analyses In Randomized Clinical Trials, Vance W. Berger, Clifford E. Lunneborg, Michael D. Ernst, Jonathan G. Levine
Journal of Modern Applied Statistical Methods
One salient feature of randomized clinical trials is that patients are randomly allocated to treatment groups, but not randomly sampled from any target population. Without random sampling parametric analyses are inexact, yet they are still often used in clinical trials. Given the availability of an exact test, it would still be conceivable to argue convincingly that for technical reasons (upon which we elaborate) a parametric test might be preferable in some situations. Having acknowledged this possibility, we point out that such an argument cannot be convincing without supporting facts concerning the specifics of the problem at hand. Moreover, we have …