Cognitive Bias for the Distribution of Ball Landing Positions in Amateur Tennis Players (Cognitive Bias for the Motor Variance in Tennis)

This study aimed to investigate whether the isotropy bias (estimating one's own motor variance as an approximately circular distribution rather than a vertically elongated distribution) arises in tennis players for the estimation of the two-dimensional variance for forehand strokes in tennis (Experiment 1), as well as the process underlying the isotropy bias (Experiment 2). In Experiment 1, 31 tennis players were asked to estimate prospectively their distribution of ball landing positions. They were then instructed to hit 50 forehand strokes. We compared the eccentricity of the ellipse calculated from estimated and observed landing positions. Eccentricity was significantly smaller in the estimated ellipse than in the observed ellipse. We assumed that the isotropy bias for the estimated ellipse comes from the process of variance estimation. In Experiment 2, nine participants estimated the 95% confidence interval of 300 dots. Eccentricity was significantly smaller in their estimated ellipses than it was in the ellipses for the dots, though the magnitude of bias decreased for the estimation of dots. These results suggest that the isotropy bias in tennis ball landing position includes the bias of recognizing landing position and the bias of estimating the ellipse confidence interval from the recognized landing position.     

共同方法变异是“致命瘟疫”吗?——论争、新知与应对

共同方法变异是由构念间相似的测量方法特征引起的系统变异, 可歪曲构念间的关系, 造成共同方法偏差。60年来, 这一问题在社会科学研究中被反复提及, 但它是否严重威胁研究效度尚无定论。虽然实证证据表明, 共同方法变异普遍存在, 数据来源、测量时间、问卷设计等因素可导致共同方法偏差, 使自我报告的横断式调查研究饱受质疑, 但部分学者从测量误差和非共同方法变异的制约作用等角度做出了回应和辩护, 认为无需过度担忧。以测量为中心的新视角强调共同方法变异是测量方法和被测构念交互作用的产物, 应从方法和构念两个维度评估共同方法变异风险。建议研究者树立均衡无偏的态度, 接纳共同方法变异的存在, 纠正对自我报告的偏见, 着重通过改进研究设计做好预先应对。     

校正的Bootstrap方法对概化理论方差分量及其变异量估计的改善

Bootstrap方法是一种有放回的再抽样方法, 可用于概化理论的方差分量及其变异量估计。用Monte Carlo技术模拟四种分布数据, 分别是正态分布、二项分布、多项分布和偏态分布数据。基于p×i设计, 探讨校正的Bootstrap方法相对于未校正的Bootstrap方法, 是否改善了概化理论估计四种模拟分布数据的方差分量及其变异量。结果表明：跨越四种分布数据, 从整体到局部, 不论是“点估计”还是“变异量”估计, 校正的Bootstrap方法都要优于未校正的Bootstrap方法, 校正的Bootstrap方法改善了概化理论方差分量及其变异量估计。     

An investigation of intra-individual variability in children with fetal alcohol spectrum disorder (FASD)

Intra-individual variability (IIV) is defined as systematic within-person variation in performance either across test sessions (e.g., test/retest performance on the same task) or in one session (e.g., variations in performance on multiple trials of a single task). Higher levels of IIV have been noted as a characteristic of neurodevelopmental disorders such as attention deficit/hyperactivity disorder? (ADHD), but IIV is yet to be investigated in fetal alcohol spectrum disorder (FASD). FASD is a term used to describe a range of conditions resulting from prenatal exposure to alcohol. As part of a comprehensive neuropsychological battery, four study groups (1. fetal alcohol syndrome/partial fetal alcohol syndrome; 2. static encephalopathy/alcohol exposed; 3. neurobehavioral disorder/alcohol exposed as diagnosed using the University of Washington FASD 4-Digit Code; 4. typically-developing (TD) age-matched children with no prenatal alcohol exposure) were administered measures of motor response and inhibitory control, attention, and adaptive behavior. The results indicate increased levels of IIV in those with FASD compared to the TD controls. It was found that IIV uniquely contributes to predicting adaptive behavior above and beyond attention, while attention partially mediates the relationship between IIV and adaptive behavior. This is the first study to the authors’ knowledge to show the presence of increased IIV in children with FASD. It additionally provides evidence that IIV measures some inherent variability in performance independent of poor attention in children with FASD.     

Inferential theory for partially disattenuated correlation coefficients

Four measurement designs are presented for use with correlation coefficients corrected, in one variable, for attenuation due to unreliability—coefficients that we term partially disattenuated correlation coefficients. Asymptotic expressions are derived for the variances and covariances of the estimates accompanying each design. Empirical simulation results that bear on the preceding mathematical developments are then presented. In addition to providing insights into the distributions of the estimates, the empirical results demonstrate satisfactory Type I error control for typical inferential applications. Power is shown to be equal to or greater than that of corresponding product-moment correlations in three of the four designs. Implications for practice are discussed.Support for the research reported in this article was provided by the Natural Sciences and Engineering Research Council of Canada. The authors acknowledge with thanks the contributions of Nancy E. Heckman to some of the theoretical aspects of the study.     

Some developments in multivariate generalizability

This article is concerned with estimation of components of maximum generalizability in multifacet experimental designs involving multiple dependent measures. Within a Type II multivariate analysis of variance framework, components of maximum generalizability are defined as those composites of the dependent measures that maximize universe score variance for persons relative to observed score variance. The coefficient of maximum generalizability, expressed as a function of variance component matrices, is shown to equal the squared canonical correlation between true and observed scores. Emphasis is placed on estimation of variance component matrices, on the distinction between generalizability- and decision-studies, and on extension to multifacet designs involving crossed and nested facets. An example of a two-facet partially nested design is provided.Appreciation is expressed to the Office of Research in Medical Education, University of Texas Medical Branch, for permitting use of their data.     

Comment on Levy's “an empirical comparison of the Z-variance and Box-Scheffé tests for homogeneity of variance”

An alternative selection of subsample sizes for the Box-Scheffé test is compared to the singlem used in Levy, An empirical comparison of the Z-variance and Box-Scheffé tests for homogeneity of variance. Use of the alternative subsample sizes is shown to suggest greater power for the Box-Scheffé test, although the test would still be less powerful than the Z-variance test. Because of the nonrobustness of the Z-variance test and the robustness of the Box-Scheffé test, the latter is recommended as a general technique unless an experimenter has assurance that all populations in the experiment are normal. Alternative techniques are also considered.The opinions expressed do not necessarily reflect the position or policy of the U. S. Civil Service Commission.     

Best linear prediction of composite universe scores

The problem of predicting universe scores for samples of examinees based on their responses to samples of items is treated. A general measurement procedure is described in which multiple test forms are developed from a table of specifications and each form is administered to a different sample of examinees. The measurement model categorizes items according to the cells of such a table, and the linear function derived for minimizing error variance in prediction uses responses to these categories. In addition, some distinctions are drawn between aspects of the approach taken here and the familiar regressed score estimates.The author thanks Robert L. Brennan, Michael J. Kolen, and Richard Sawyer for helpful comments and corrections, and anonymous reviewers for suggested improvements.     

ANOVA and ANCOVA of pre- and post-test,ordinal data

With random assignment to treatments and standard assumptions, either a one-way ANOVA of post-test scores or a two-way, repeated measures ANOVA of pre- and post-test scores provides a legitimate test of the equal treatment effect null hypothesis for latent variable . In an ANCOVA for pre- and post-test variablesX andY which are ordinal measures of and , respectively, random assignment and standard assumptions ensure the legitimacy of inferences about the equality of treatment effects on latent variable . Sample estimates of adjustedY treatment means are ordinal estimators of adjusted post-test means on latent variable .     

Teaching Principal Components Using Correlations

Introducing principal components (PCs) to students is difficult. First, the matrix algebra and mathematical maximization lemmas are daunting, especially for students in the social and behavioral sciences. Second, the standard motivation involving variance maximization subject to unit length constraint does not directly connect to the “variance explained” interpretation. Third, the unit length and uncorrelatedness constraints of the standard motivation do not allow re-scaling or oblique rotations, which are common in practice. Instead, we propose to motivate the subject in terms of optimizing (weighted) average proportions of variance explained in the original variables; this approach may be more intuitive, and hence easier to understand because it links directly to the familiar “R-squared” statistic. It also removes the need for unit length and uncorrelatedness constraints, provides a direct interpretation of “variance explained,” and provides a direct answer to the question of whether to use covariance-based or correlation-based PCs. Furthermore, the presentation can be made without matrix algebra or optimization proofs. Modern tools from data science, including heat maps and text mining, provide further help in the interpretation and application of PCs; examples are given. Together, these techniques may be used to revise currently used methods for teaching and learning PCs in the behavioral sciences.