Time-series analysis in operant research

A time-series method is presented, nontechnically, for analysis of data generated in individual-subject operant studies, and is recommended as a supplement to visual analysis of behavior change in reversal or multiple-baseline experiments. The method can be used to identify three kinds of statistically significant behavior change: (a) changes in score levels from one experimental phase to another, (b) reliable upward or downward trends in scores, and (c) changes in trends between phases. The detection of, and reliance on, serial dependency (autocorrelation among temporally adjacent scores) in individual-subject behavioral scores is emphasized. Examples of published data from the operant literature are used to illustrate the time-series method.     

A study on understanding cognitive states through gait analysis

In this work, we attempted to find out the relationship between different gait patterns and their corresponding cognitive states by using different statistical and machine learning approaches. This paper strongly focusses on the simulations followed by implementation of the proposed cognitive states i.e. (i) EmotionOriented State (EOS) (ii) Thinking Oriented State (TOS) (iii) Memory Oriented State(MOS) (iv) Simple Regular Oriented State (SROS). A novel approach was implemented by creating different environmental contexts for different gaits in our lab. An experimental method was performed to isolate movement artifact using Independent Component Analysis from recorded EEG(Electroencephalogram) signals. Measurement of joint angles from joint positions captured using Kinect V2 sensors was done with the help of OpenSim software. The relationship between different gaits and mental states was established using Pearsons Correlation Coefficient, ANOVA(Analysis of variance) and SVM(Support Vector Machine) classifier respectively. A strong relationship was found between them. The SVM classifier for the EOS and the non-EOS states based on joint angles inferred an accuracy of 81.08%. The ROC Curve for SVM classification depicted an AUC (area under the curve) of 0.9724.     

On normal theory based inference for multilevel models with distributional violations

Data in social and behavioral sciences are often hierarchically organized though seldom normal, yet normal theory based inference procedures are routinely used for analyzing multilevel models. Based on this observation, simple adjustments to normal theory based results are proposed to minimize the consequences of violating normality assumptions. For characterizing the distribution of parameter estimates, sandwich-type covariance matrices are derived. Standard errors based on these covariance matrices remain consistent under distributional violations. Implications of various covariance estimators are also discussed. For evaluating the quality of a multilevel model, a rescaled statistic is given for both the hierarchical linear model and the hierarchical structural equation model. The rescaled statistic, improving the likelihood ratio statistic by estimating one extra parameter, approaches the same mean as its reference distribution. A simulation study with a 2-level factor model implies that the rescaled statistic is preferable.This research was supported by grants DA01070 and DA00017 from the National Institute on Drug Abuse and a University of North Texas faculty research grant. We would like to thank the Associate Editor and two reviewers for suggestions that helped to improve the paper.     

Still cautious: Personality characteristics of extremely low birth weight adults in their early 30s

Extremely low birth weight survivors (ELBW; <1000 g) display a personality style characterized by cautiousness, shyness, and risk aversion in their 20s. We examined whether non-impaired ELBW survivors were still more cautious than their normal birth weight (NBW) peers at ages 30–35 and assessed the stability of this personality characteristic across approximately a decade in the oldest known cohort of ELBW survivors. Of the 154 participants tested at ages 22–26, 111 (i.e., 72%) of them returned approximately 10 years later [i.e., 69% (49/71) ELBW and 75% (62/83) NBW], and once again they completed the Eysenck Personality Questionnaire-Revised (EPQ-R). We created the same theoretically and empirically derived composite measure of cautiousness at each visit by summing the EPQ-R psychoticism scale (reverse scored) and the EPQ-R lie scale. We found that ELBW adults reported higher cautiousness than their NBW counterparts at ages 30–35. We also found that levels of cautiousness remained stable over approximately 10 years in both groups. Our findings suggest that individuals born at ELBW were more cautious than NBW peers and that this personality characteristic remained stable into their early 30s. The present study appears to be the first empirical demonstration of personality stability among adult ELBW survivors.     

Perceiving vocal age and gender: An adaptation approach

Aftereffects of adaptation have revealed both independent and interactive coding of facial signals including identity and expression or gender and age. By contrast, interactive processing of non-linguistic features in voices has rarely been investigated. Here we studied bidirectional cross-categorical aftereffects of adaptation to vocal age and gender. Prolonged exposure to young (~ 20 yrs) or old (~ 70 yrs) male or female voices biased perception of subsequent test voices away from the adapting age (Exp. 1) and the adapting gender (Exp. 2). Relative to gender-congruent adaptor-test pairings, vocal age aftereffects (VAAEs) were reduced but remained significant when voice gender changed between adaptation and test. This suggests that the VAAE relies on both gender-specific and gender-independent age representations for male and female voices. By contrast, voice gender aftereffects (VGAEs) were not modulated by age-congruency of adaptor and test voices (Exp. 2). Instead, young voice adaptors generally induced larger VGAEs than old voice adaptors. This suggests that young voices are particularly efficient gender adaptors, likely reflecting more pronounced sexual dimorphism in these voices. In sum, our findings demonstrate how high-level processing of vocal age and gender is partially intertwined.     

在押毒品犯人格的基本特征及其相关因素的研究

本研究以 3 78名在押毒品犯为被试 ,以CPI为测量工具 ,探讨了在押毒品犯的基本特征和类型特征 ,并采用方差分析技术研究了影响毒品犯人格的若干因素。结果发现 ,与对照组相比 ,在押毒品犯除了在“自我控制”分量表上未发现显著差异外 ,其余的 1 9个分量表上均有显著差异 ;在押毒品犯在四种人格类型上的分布不平衡 ,主要集中在Delta类型 ;年龄、关押时间对毒品犯的人格特征的变异有一定影响 ,而减刑次数的影响不显著     

Can we disregard the whole model? Omnibus non-inferiority testing for R2 in multi-variable linear regression and in ANOVA

Determining a lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model (i.e., to confirm the lack of a meaningful association between an outcome and predictors). Despite this, the literature rarely offers information about, or technical recommendations concerning, the appropriate statistical methodology to be used to accomplish this task. This paper introduces non-inferiority tests for ANOVA and linear regression analyses, which correspond to the standard widely used F test for and R², respectively. A simulation study is conducted to examine the Type I error rates and statistical power of the tests, and a comparison is made with an alternative Bayesian testing approach. The results indicate that the proposed non-inferiority test is a potentially useful tool for ‘testing the null’.     

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。     

Analysis of vehicle-based lateral performance measures during distracted driving due to phone use

Distracted driving due to mobile phone use has been identified as a major contributor to accidents; therefore, it is required to develop ways for detecting driver distraction due to phone use. Though prior literature has documented various visual behavioural and physiological techniques to identify driver distraction, comparatively little is known about vehicle based performance features which can identify driver’s distracted state during phone conversation and texting while driving. Therefore, this study examined the effects of simple conversation, complex conversation, simple texting and complex texting tasks on vehicle based performance parameters such as standard deviation of lane positioning, number of lane excursions, mean and standard deviation of lateral acceleration, mean and standard deviation of steering wheel angle and steering reversal rates (for 1°, 5° and 10° angle differences). All these performance measures were collected for 100 licensed drivers, belonging to three age groups (young, mid-age and old age), with the help of a driving simulator. Effects of all the phone use conditions and driver demographics (age, gender and phone use habits) on the measures were analysed by repeated measures ANOVA tests. Results showed that 1°, 5° SRRs are able to identify all the distracted conditions except for simple conversation; while, 10° SSR can detect all the distracted conditions (including simple conversation). The results suggest that 10° SRR can be included in intelligent in-vehicle devices in order to detect distraction and alert drivers of their distracted state. This can prevent mobile phone use during driving and therefore can help in reducing the road accidents due to mobile phone distractions.     

方差分析的统计检验力和效果大小的常用方法比较

本文对用方差分析统计检验力和效果大小进行估计的几种不同方法作了简要的介绍和比较。