A Test by Any Other Name: P Values,Bayes Factors,and Statistical Inference

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.     

A Comparative Investigation of Confidence Intervals for IndependentVariables in Linear Regression

In linear regression, the most appropriate standardized effect size for individual independent variables having an arbitrary metric remains open to debate, despite researchers typically reporting a standardized regression coefficient. Alternative standardized measures include the semipartial correlation, the improvement in the squared multiple correlation, and the squared partial correlation. No arguments based on either theoretical or statistical grounds for preferring one of these standardized measures have been mounted in the literature. Using a Monte Carlo simulation, the performance of interval estimators for these effect-size measures was compared in a 5-way factorial design. Formal statistical design methods assessed both the accuracy and robustness of the four interval estimators. The coverage probability of a large-sample confidence interval for the semipartial correlation coefficient derived from Aloe and Becker was highly accurate and robust in 98% of instances. It was better in small samples than the Yuan-Chan large-sample confidence interval for a standardized regression coefficient. It was also consistently better than both a bootstrap confidence interval for the improvement in the squared multiple correlation and a noncentral interval for the squared partial correlation.     

Modeling Clustered Data with Very Few Clusters

Small-sample inference with clustered data has received increased attention recently in the methodological literature, with several simulation studies being presented on the small-sample behavior of many methods. However, nearly all previous studies focus on a single class of methods (e.g., only multilevel models, only corrections to sandwich estimators), and the differential performance of various methods that can be implemented to accommodate clustered data with very few clusters is largely unknown, potentially due to the rigid disciplinary preferences. Furthermore, a majority of these studies focus on scenarios with 15 or more clusters and feature unrealistically simple data-generation models with very few predictors. This article, motivated by an applied educational psychology cluster randomized trial, presents a simulation study that simultaneously addresses the extreme small sample and differential performance (estimation bias, Type I error rates, and relative power) of 12 methods to account for clustered data with a model that features a more realistic number of predictors. The motivating data are then modeled with each method, and results are compared. Results show that generalized estimating equations perform poorly; the choice of Bayesian prior distributions affects performance; and fixed effect models perform quite well. Limitations and implications for applications are also discussed.     

Estimation of the Coefficient of Variation with Minimum Risk: A Sequential Method for Minimizing Sampling Error and Study Cost

The coefficient of variation is an effect size measure with many potential uses in psychology and related disciplines. We propose a general theory for a sequential estimation of the population coefficient of variation that considers both the sampling error and the study cost, importantly without specific distributional assumptions. Fixed sample size planning methods, commonly used in psychology and related fields, cannot simultaneously minimize both the sampling error and the study cost. The sequential procedure we develop is the first sequential sampling procedure developed for estimating the coefficient of variation. We first present a method of planning a pilot sample size after the research goals are specified by the researcher. Then, after collecting a sample size as large as the estimated pilot sample size, a check is performed to assess whether the conditions necessary to stop the data collection have been satisfied. If not an additional observation is collected and the check is performed again. This process continues, sequentially, until a stopping rule involving a risk function is satisfied. Our method ensures that the sampling error and the study costs are considered simultaneously so that the cost is not higher than necessary for the tolerable sampling error. We also demonstrate a variety of properties of the distribution of the final sample size for five different distributions under a variety of conditions with a Monte Carlo simulation study. In addition, we provide freely available functions via the MBESS package in R to implement the methods discussed.     

Stimulus–response correspondence effect as a function of temporal overlap between relevant and irrelevant information processing

The stimulus–response correspondence (SRC) effect refers to advantages in performance when stimulus and response correspond in dimensions or features, even if the common features are irrelevant to the task. Previous research indicated that the SRC effect depends on the temporal course of stimulus information processing. The current study investigated how the temporal overlap between relevant and irrelevant stimulus processing influences the SRC effect. In this experiment, the irrelevant stimulus (a previously associated tone) preceded the relevant stimulus (a coloured rectangle). The irrelevant and relevant stimuli onset asynchrony was varied to manipulate the temporal overlap between the irrelevant and relevant stimuli processing. Results indicated that the SRC effect size varied as a quadratic function of the temporal overlap between the relevant stimulus and irrelevant stimulus. This finding extends previous experimental observations that the SRC effect size varies in an increasing or decreasing function with reaction time. The current study demonstrated a quadratic function between effect size and the temporal overlap.     

Linked proximal and distal changes in the reaching behavior of 5- to 12-month-old human infants grasping objects of different sizes

This study evaluates age-related proximal and distal changes in reaching organization for objects of different sizes. To this end, eight objects ranging from 2 to 9 cm diam. were presented to 23 infants ages 5 to 12 months. Proximal control was determined by the relative frequencies of bimanual reaching for large and small objects. Distal control was assessed by hand opening and orientation with respect to an object, and by the proportion of the object being included within hand opening at touch. Five-month-old infants tended to reach bimanually regardless of object size. Starting at 7 to 8 months, infants tended to reach for large objects bimanually more often than for small ones. Only at 11 to 12 months did reaching closely reflect the object’s diameter. The frequency of thumb-index finger angle opening during the approach phase also increased after 7 to 8 months of age, as well as the adjustment of the angle to the object diameter and the proportion of the object within hand opening at touch. Proximal and distal changes appeared coupled at 5 to 6 months, when the few subjects showing evidence of some proximal adjustments to object size were also those who exhibited some distal adjustments. After they started to appear, however, proximal and distal adjustments seemed to be independent, as revealed by the lack of correlation of proximal and distal changes between 7 and 12 months.     

Relationships between factors of intelligence and brain volume

The recent explosion of studies aimed at determining the biological basis of intelligence has revealed that cognitive ability has a strong biological substrate. This study expands on this literature by examining the relation between cognitive ability and MRI-measured brain volume and head size in a sample of adult male siblings recruited from the London, Ontario community in Canada. It was found that brain volume correlated with IQ at 0.35 (P<0.01), thus replicating the results of past studies. Corrections for restriction of range and attenuation in both this and past studies suggest that the population value of the brain volume-IQ correlation is closer to 0.50. Head size variables, with one exception, also showed the expected positive correlations with IQ. The results of a vector analysis on factor scores indicated that the more highly g-loaded a test was the more highly it correlated with brain volume (r=0.59, P<0.01). The sum of the data suggested that although brain volume (and to a lesser extent, head size) is predictive of g, fluid ability, and memory, it does not predict crystallized ability. Unexpectedly, the higher the spatial imaging loading of a test, the less its correlation with brain volume (vector correlation=−0.84, P<0.001).     

Reviews

Rottschaefer, William A. The Biology and Psychology of Moral Agency
Mccutcheon, Russell T. Manufacturing Religion: The Discourse on Sui Generis Religion and the Politics of Nostalgia
Drengson, Alan The Practice of Technology: Exploring Technology,Ecophilosophy, and Spiritual Disciplines for Vital Links
Burrell, David B. and Mcginn, Bernard (eds.) God and Creation: An Ecumenical Symposium     

方差分析效果大小报告的新指标

心理学期刊论文中可重复性不高的现象,原因之一是研究结果的效果普遍较小。并且在报告效果大小的文章中,效果大小指标使用不当。在方差分析中最经常报告的是η2和η2_p,但是在不同的研究设计中,这些效果大小是无法直接进行比较的。广义eta方(η²_G)是近年来新出现的一种新的效果大小指标,可克服的η2和η2_p不足,灵活处理重复测量等多种研究设计下个体差异的计算问题,实现跨研究设计效果大小的可比性。论文结合实例介绍了η²_G的原理和计算方法,并对其优缺点、使用和报告等问题进行了讨论。研究人员在报告效果大小时要考虑到不同的研究设计和研究假设,并选择恰当的指标防止过高估计效果大小。