Direction of Effects in Multiple Linear Regression Models

Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D’Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.     

Meta-Analysis of Pragmatic and Theoretical Research: A Critique

Meta-analysis refers to a set of statistical procedures used to summarize and integrate many empirical studies that focus on one issue. This numerical method of integrating research findings is said to be superior to the narrative type of reviews because it is more objective, reliable, and rigorous. Moreover, the meta-analytic approach is supposedly capable of resolving research controversies, strengthening empirical hypotheses, and discovering new relationships among variables. In this study, these claims are examined and found to be wanting. Some objections to the use of meta-analysis as a means of substantiating theoretical assertions are raised with reference to the rationale of experimentation and to how knowledge evolves. It is concluded that it is inappropriate to apply meta-analysis to integrate theoretical research.     

A Data Analysis Method for Using Longitudinal Binary Outcome Data from a SMART to Compare Adaptive Interventions

Sequential multiple assignment randomized trials (SMARTs) are a useful and increasingly popular approach for gathering information to inform the construction of adaptive interventions to treat psychological and behavioral health conditions. Until recently, analysis methods for data from SMART designs considered only a single measurement of the outcome of interest when comparing the efficacy of adaptive interventions. Lu et al. proposed a method for considering repeated outcome measurements to incorporate information about the longitudinal trajectory of change. While their proposed method can be applied to many kinds of outcome variables, they focused mainly on linear models for normally distributed outcomes. Practical guidelines and extensions are required to implement this methodology with other types of repeated outcome measures common in behavioral research. In this article, we discuss implementation of this method with repeated binary outcomes. We explain how to compare adaptive interventions in terms of various summaries of repeated binary outcome measures, including average outcome (area under the curve) and delayed effects. The method is illustrated using an empirical example from a SMART study to develop an adaptive intervention for engaging alcohol- and cocaine-dependent patients in treatment. Monte Carlo simulations are provided to demonstrate the good performance of the proposed technique.     

Multiple regression analyses in artificial-grammar learning: The importance of control groups

In artificial-grammar learning, it is crucial to ensure that above-chance performance in the test stage is due to learning in the training stage but not due to judgemental biases. Here we argue that multiple regression analysis can be successfully combined with the use of control groups to assess whether participants were able to transfer knowledge acquired during training when making judgements about test stimuli. We compared the regression weights of judgements in a transfer condition (training and test strings were constructed by the same grammar but with different letters) with those in a control condition. Predictors were identical in both conditions—judgements of control participants were treated as if they were based on knowledge gained in a standard training stage. The results of this experiment as well as reanalyses of a former study support the usefulness of our approach.     

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way we analyse these relationships does not seem to reflect the way we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modelling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such that R ² for worn-out and job satisfaction were significantly higher in the neural networks. Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method, and data.     

Change management: Time for a change!

We discuss some core issues in the field of change management. We use these topics to identify some mindsets that dominate the practice of change management, and argue that these should be replaced by some alternatives. The alternatives are drawn largely from operations management and sociotechnical thinking. We characterize existing approaches as partial, and speculate that this may be one of the reasons why so many change initiatives are ineffective at meeting their goals. We identify some of the reasons why existing mindsets are sustained. We also point to some ways forward, focusing on changes in the mindsets and language we use. We speculate that these would improve the effectiveness of change initiatives.     

The Longevity Effects of Religious and Nonreligious Participation: A Meta‐Analysis and Meta‐Regression

Scholars have long argued that the reduced mortality risk associated with frequent participation in religious services derives from two sources: social participation and religious belief efficacy. In contrast, the reduced mortality risk associated with participation in nonreligious groups is thought to derive solely from the social participation component. This study tests the religious efficacy hypothesis by comparing the effects of religious participation with nonreligious participation using meta‐analyses of 312 mortality risk estimates from 74 publications (providing data on more than 300,000 persons). We found no significant difference between the mean hazard ratio (HR) for low religious participation (HR, 1.32; 95% CI, 1.24–1.41) and the mean HR for low nonreligious participation (HR, 1.25; 95% CI, 1.17–1.33). These findings suggest that the positive health effects of religious participation may largely be attributed to the social participation component, rather than to the religious component of the act.     

Neuropsychological test norms controlled for physical health: Does it matter?

The objective of the present study was to investigate the effects of physical health on neuropsychological test norms. Medical and neuropsychological data from 118 healthy volunteer controls, aged 26–91 years, were collected during five recruitment occasions. The examinations included a clinical investigation, brain neuroimaging, and a comprehensive neuropsychological test battery. Test‐specific statistical regression‐weights for age, education and gender were calculated to establish preliminary test norms. Hierarchical regression analyses demonstrated that control in addition for physical health moved best performance from age 60 to 65 for abstraction; replaced a plateau above age 70 for verbal fluency, with a continued rise in performance; eliminated significant negative influences of age on auditory learning, spatial reasoning and complex copying; reduced them on wordlist recall, psychomotor speed, visual scanning and mental shifting; and slightly reduced negative influences of low education on most verbal tests, several memory tests, and psychomotor speed, indicating rises in normative scores of up to 0.8 SD at age 80 and 0.4 SD at age 60. No differences were found at age 40. Although the sample size is not adequate to be used for normative data, the findings indicate that norms uncontrolled for health overestimate the negative influence of advanced age and low education, implying a risk of drawing false diagnostic conclusions.     

The factors of female taxi drivers’ speeding offenses in Taiwan

Females choosing taxi driving as a career is rare, therefore, investigating such samples is often difficult. Speeding is one of the most common driving violations, however, there has been no research looking into female taxi drivers’ speeding issue. This study explores the factors of female taxi drivers’ speeding offenses in Taiwan. Data is based on a national survey and includes 235 professional female taxi drivers. The results indicate that female taxi drivers work approximately 27.37 days per month, at a mean of 9.76 h per day. Of the female taxi drivers represented in this study, 22.8% reported at least one speeding offense over a one-year period. The results of a logistic regression model reveal that the determinant factors associated with female taxi drivers’ speeding offenses are significantly related to age, educational level and mileage driven. However, job experience, business operating style, and vehicle engine size are not associated with committing speeding offenses. Practical implications for traffic safety of female taxi drivers are also discussed.     

父母亲职压力与儿童对立违抗行为的关系:一项交叉滞后分析

采用亲职压力量表和对立违抗行为表现评估表调查了来自北京、山东和云南三地14所小学有ODD(oppositional defiant disorder)行为表现的301名儿童及其父母。通过为期两年的追踪研究,采用交叉滞后分析探讨了父母亲职压力与儿童的ODD行为之间的关系。结果发现:(1)在第一年和第二年的数据中,父母亲职压力和儿童的ODD行为表现均存在显著正相关;(2)第一年和第二年的父母亲职压力得分显著正相关,同样,两年的儿童ODD行为表现得分也显著正相关;(3)交叉滞后分析发现,对于有ODD行为表现的男孩来说,其父母第一年的亲职压力能预测他们第二年的ODD行为表现,但对于有ODD行为表现的女孩来说,其父母第一年的亲职压力并不能预测她们第二年的ODD行为表现;而不管是男孩,还是女孩,第一年的ODD行为表现都不能预测第二年的父母亲职压力。研究结果揭示出男孩父母的亲职压力可预测男孩的ODD行为表现,而不论是男孩或女孩的ODD行为表现都不能预测其父母亲职压力,说明父母亲职压力可能是男孩ODD行为表现的影响因素。