The Experts below are selected from a list of 17061 Experts worldwide ranked by ideXlab platform

F. Y. Hsieh - One of the best experts on this subject based on the ideXlab platform.

  • Comparing Sample Size formulae for trials with unbalanced allocation using the logrank test.
    Statistics in medicine, 1992
    Co-Authors: F. Y. Hsieh
    Abstract:

    This paper compares the Sample Size formulae given by Schoenfeld, Freedman, Hsieh and Shuster for unbalanced designs. Freedman's formula predicts the highest power for the logrank test when the Sample Size ratio of the two groups Equals the reciprocal of the hazard ratio. The other three formulae predict highest powers when Sample Sizes in the two groups are Equal. Results of Monte Carlo simulations performed for the power of the logrank test with various Sample Size ratios show that the power curve of the logrank test is almost flat between a Sample Size ratio of one and a Sample Size ratio close to the reciprocal of the hazard ratio. An Equal Sample-Size allocation may not maximize the power of the logrank test. Monte Carlo simulations also show that, under an exponential model, when the Sample Size ratio is toward the reciprocal of the hazard ratio, Freedman's formula predicts more accurate powers. Schoenfeld's formula, however, seems best for predicting powers with Equal Sample Size.

Chao-ying Joanne Peng - One of the best experts on this subject based on the ideXlab platform.

  • The sensitivity of three methods to nonnormality and unEqual variances in interval estimation of effect Sizes
    Behavior Research Methods, 2015
    Co-Authors: Li-ting Chen, Chao-ying Joanne Peng
    Abstract:

    Confidence interval (CI) estimation for an effect Size (ES) provides a range of possible population ESs supported by data. In this article, we investigated the noncentral t method, Bonett’s method, and the bias-corrected and accelerated (BCa) bootstrap method for constructing CIs when a standardized linear contrast of means is defined as an ES. The noncentral t method assumes normality and Equal variances, Bonett’s method assumes only normality, and the BCa bootstrap method makes no assumptions. We simulated data for three and four groups from a variety of populations (one normal and five nonnormals) with varied variance ratios (1, 2.25, 4, 8), population ESs (0, 0.2, 0.5, 0.8), and Sample Size patterns (one Equal and two unEqual). Results showed that the noncentral method performed the best among the three methods under the joint condition of ES = 0 and Equal variances. Performance of the noncentral method was comparable to that of the other two methods under (1) Equal Sample Size, unEqual weight for each group, and the last group Sampled from a leptokurtic distribution, or (2) Equal Sample Size and Equal weight for all groups, when all are Sampled from a normal population, or only the last group Sampled from a nonnormal distribution. In the remaining conditions, Bonett’s and the BCa bootstrap methods performed better than the noncentral method. The BCa bootstrap method is the method of choice when the Sample Size per group is 30 or more. Findings from this study have implications for simultaneous comparisons of means and of ranked means in between- and within-subjects designs.

Joerg Evermann - One of the best experts on this subject based on the ideXlab platform.

  • Multiple-Group Analysis Using the sem Package in the R System
    Structural Equation Modeling: A Multidisciplinary Journal, 2010
    Co-Authors: Joerg Evermann
    Abstract:

    Multiple-group analysis in covariance-based structural equation modeling (SEM) is an important technique to ensure the invariance of latent construct measurements and the validity of theoretical models across different subpopulations. However, not all SEM software packages provide multiple-group analysis capabilities. The sem package for the R system, which holds an important position as the only open-source SEM software, does not currently offer multigroup analysis. This article offers an alternative to true multigroup modeling that is easy to understand and apply in the R software. It is limited, however, by the constraint that groups require Equal Sample Size.

Mete B. Sirvanci - One of the best experts on this subject based on the ideXlab platform.

  • A two-Sample test for reliability comparison
    Quality Engineering, 2004
    Co-Authors: Mete B. Sirvanci
    Abstract:

    Abstract A new test for the comparison of reliabilities of two populations is proposed. The test statistic uses only the number of failures in each Sample. The test procedure is conditional in the sense that the unknown parameter p, the probability of failure during the test period, under the null hypothesis is estimated first by the pooled Sample proportion. Critical values are tabulated for several Equal Sample Size cases and asymptotic properties are obtained. The use of this test in practice is illustrated by several examples. It is shown that the power of the proposed test is larger than that of Fisher's exact test for the cases considered. Applications in survival analysis are also studied, and the power of the test procedure is compared with those of other tests, which are based on failure times in addition to failure counts.

Li-ting Chen - One of the best experts on this subject based on the ideXlab platform.

  • The sensitivity of three methods to nonnormality and unEqual variances in interval estimation of effect Sizes
    Behavior Research Methods, 2015
    Co-Authors: Li-ting Chen, Chao-ying Joanne Peng
    Abstract:

    Confidence interval (CI) estimation for an effect Size (ES) provides a range of possible population ESs supported by data. In this article, we investigated the noncentral t method, Bonett’s method, and the bias-corrected and accelerated (BCa) bootstrap method for constructing CIs when a standardized linear contrast of means is defined as an ES. The noncentral t method assumes normality and Equal variances, Bonett’s method assumes only normality, and the BCa bootstrap method makes no assumptions. We simulated data for three and four groups from a variety of populations (one normal and five nonnormals) with varied variance ratios (1, 2.25, 4, 8), population ESs (0, 0.2, 0.5, 0.8), and Sample Size patterns (one Equal and two unEqual). Results showed that the noncentral method performed the best among the three methods under the joint condition of ES = 0 and Equal variances. Performance of the noncentral method was comparable to that of the other two methods under (1) Equal Sample Size, unEqual weight for each group, and the last group Sampled from a leptokurtic distribution, or (2) Equal Sample Size and Equal weight for all groups, when all are Sampled from a normal population, or only the last group Sampled from a nonnormal distribution. In the remaining conditions, Bonett’s and the BCa bootstrap methods performed better than the noncentral method. The BCa bootstrap method is the method of choice when the Sample Size per group is 30 or more. Findings from this study have implications for simultaneous comparisons of means and of ranked means in between- and within-subjects designs.