Use of baseline measurements in the two-period crossover design

It is proposed that baseline measurements be obtained prior to each period in a two-period crossover design. These measurements are used in a preliminary test for determining the validity of a test for treatment comparison and also for testing the hypothesis of equal treatment effects. The null hypothesis in this preliminary test consists of the following three hypotheses: that there is no difference in disease conditions prior to the two periods, no difference in residual effects of the drugs, and no treatment × period interaction.. A numerical example is given and the efficiencies of several methods are computed.     

Performance of the weighted least squares apprdach to categorical data analysis

The weighted least squares method of Grizzle, Starmer and Koch is a common and convenient way to analyze categorical data. Tests of significance based on this method are typically performed by calculating a quadratic form which is asymptotically distrib-uted as a chi-square random variable under an appropriate null hy- pothesis.

This paper explores the validity of this distributional approximation for small samples in terms of the accuracy and power associated with this chi-square statistic. We report simulation results from the weighted least squares method applied to balanced factorial experiments, The simulations which constitute the majorpart of this work also provide insight into the relative performance of several response functions.     

A General formula for the upper tail significance levels of empirical distribution function test statistics

In this paper ve obtain an asymptotic expression for the upper tail area of the distribution of an infinite weighted sum of chi-square random variables and show how this can be applied to distributions of various goodness of fit test statistics. Results obtained by this general approach are comparable with those reported previously in the literature. In the case of the Cramer-von Mises statistic an empirical adjustment is given vhich significantly improves on previous approximations. For the Kuiper statistic the corresponding empirical adjustment leads to an existing highly accurate approximation.     

Lagrance-multiplier tersts for weak exogeneity: a synthesis

This paper unifies two seemingly separate approaches to test weak exogeneity in dynamic regression models with Lagrange-mulptiplier statistics. The first class of tests focuses on the orthogonality between innovations and conditioning variables, and thus is related to the Durbin-Wu-Hausman specification test. The second approach has been developed more recently in the context of context of cointegration and error correction models, ad concentrates on the question whether the conditioning variables display error correction behaviour. It is shown that the vital difference between the two approaches stems from the choice of the parmeters of interest. A new test is derived, which encompasses both its predecessors. The test is applied to an error correction model of the demand for money in Switzerland.     

Alternative approaches to testing non-nested models with autocorrelated disturbances

Since departures from the classical assumptions regarding the disturbances in a linear tegression model arise frequently in empirical application, deveral computationally Straightforward procedutes are presented in this paper for testiog non-nested models when the disturbances of these models follow first- or higher-order autoregressive processes. Anempirical example is used to illustrate how the procedures may be used to test competing Keynesian and New Classical non-nested models of unemployment for the U.S using annual time series data for 1955-85.     

Analysis of means approach for random factor analysis

Analysis of means (ANOM) is a powerful tool for comparing means and variances in fixed-effects models. The graphical exhibit of ANOM is considered as a great advantage because of its interpretability and its ability to evaluate the practical significance of the mean effects. However, the presence of random factors may be problematic for the ANOM method. In this paper, we propose an ANOM approach that can be applied to test random effects in many different balanced statistical models including fixed-, random- and mixed-effects models. The proposed approach utilizes the range of the treatment averages for identifying the dispersions of the underlying populations. The power performance of the proposed procedure is compared to the analysis of variance (ANOVA) approach in a wide range of situations via a Monte Carlo simulation study. Illustrative examples are used to demonstrate the usefulness of the proposed approach and its graphical exhibits, provide meaningful interpretations, and discuss the statistical and practical significance of factor effects.     

Three optimal cut-point selection criteria based on sensitivity and specificity with user-defined weights

Methods: Based on the index S (S = SENSITIVITY (SEN) × SPECIFICITY (SPE)), the new weighted product index S_w is defined as S_w = (SEN)^2w × (SPE)^2(1-w), where (0≤w≤1). The S_w is developed to be a new tool to select the optimal cut point in ROC analysis and be compared with the other two commonly used criteria.

Results: Comparing the optimal cut point for the three criteria, the wave range of the optimal cut point for the maximized weighted Youden index criterion is the widest, the weighted closest-to-(0,1) criterion is the narrowest and the weighted product index S_w criterion lays between the ranges of the two criteria.     

Effectiveness of mindfulness-based coloring for university students’ test anxiety

Abstract

Objective: This study compared the effectiveness of mindfulness coloring (mandala), free drawing/coloring, and a noncoloring control activity for university students’ test anxiety, and assessed the relationship of dispositional mindfulness and response to intervention on mindfulness and test anxiety states. Participants: University students (n?=?167; 81.4% female; M_age = 21.29?years, SD?=?4.46) were randomly assigned to a mandala (n?=?57), free draw/coloring (n?=?58), or noncoloring condition (n?=?52). Methods: Participants completed standardized measures assessing test anxiety and state mindfulness pre–postactivity before completing a test, and two dispositional mindfulness measures. Results: Participants in both coloring conditions reported significant decreases in test anxiety and significant increases in state mindfulness pre–postintervention, and participants in the control condition reported significant increases in test anxiety. Reports of preintervention state mindfulness and test anxiety fully mediated relations between dispositional mindfulness and postintervention state mindfulness and test anxiety. Conclusions: Implications for research and practice on mindfulness coloring and test anxiety are discussed.     

Using the dependent wild bootstrap for the nonparametric goodness-of-fit test for density functions

In this paper, we consider the well-known nonparametric consistent model-specification test for the stationary density function (see [Aït-Sahalia Y. Testing continuous-time models of the spot interest rate. Rev Financ Stud. 1996;9:385–426; Li Q. Nonparametric testing of closeness between two unknown distribution functions. Econ Rev. 1996;15:261–274; Fan Y, Ullah A. On goodness-of-fit tests for weakly dependent processes using kernel method. J Nonparametric Stat. 2000;11:337–360]) and reinvestigate it carefully using asymptotics and simulation. Our work reveals that the test is subject to power and size distortions, which are mainly caused by dependence or convergence rate changes under the null and alternative hypothesis. A dependent wild bootstrap is newly suggested as a feasible remedy to such distortions. Our result provides a complete explanation as well as a solution to the problem that experienced by Aït-Sahalia [Testing continuous-time models of the spot interest rate. Rev Financ Stud. 1996;9:385–426], that is, that the test rejects true models too often when independent and identically distributed asymptotic critical values are used.     

A Primer on Visualizations for Comparing Populations,Including the Issue of Overlapping Confidence Intervals

In comparing a collection of K populations, it is common practice to display in one visualization confidence intervals for the corresponding population parameters θ₁, θ₂, …, θ_K. For a pair of confidence intervals that do (or do not) overlap, viewers of the visualization are cognitively compelled to declare that there is not (or there is) a statistically significant difference between the two corresponding population parameters. It is generally well known that the method of examining overlap of pairs of confidence intervals should not be used for formal hypothesis testing. However, use of a single visualization with overlapping and nonoverlapping confidence intervals leads many to draw such conclusions, despite the best efforts of statisticians toward preventing users from reaching such conclusions. In this article, we summarize some alternative visualizations from the literature that can be used to properly test equality between a pair of population parameters. We recommend that these visualizations be used with caution to avoid incorrect statistical inference. The methods presented require only that we have K sample estimates and their associated standard errors. We also assume that the sample estimators are independent, unbiased, and normally distributed.