Father regression Clinical narratives and theoretical reflections1

The author deals with love‐hate enthrallment and submission to a primitive paternal object. This is a father‐son relationship that extends through increasing degrees of ‘primitiveness’ or extremeness, and is illustrated through three different constellations that constitute a continuum. One pole of the continuum encompasses certain male patients who show a loving, de‐individuated connection to a father experienced as trustworthy, soft, and in need of protection. Further along the continuum is the case of a transsexual patient whose analysis revealed an intense ‘God‐transference’, a bondage to an idealized, feared, and ostensibly protective father‐God introject. A great part of this patient's analysis consisted in a fi erce struggle to liberate himself from this fi gure. The other end of the continuum is occupied by religious terrorists, who exemplify the most radical thralldom to a persecutory, godly object, a regressive submission that banishes woman and enthrones a cruel superego, and that ends in destruction and self‐destruction. Psychoanalytic thinking has traditionally dealt with the oedipal father and recently with the nurturing father, but there is a gap in thinking about the phallic, archaic father, and his relations with his son(s). The author aims at fi lling this gap, at the same time as she also raises the very question of ‘What is a father?’ linking it with literary and religious themes.     

优势分析方法及其应用

优势分析是近年来由Budescu等人新发展起来的一种确定多元回归方程中各预测变量相对重要性的方法。与传统方法相比,优势分析突出的特点是,全面比较了在由全模型所衍生出来的所有子模型情况下,各预测变量(X1,X…XP)在解释或预测标准变量у时,它们之间的相对重要性。本文从基本原理以及具体操作过程对这一新的统计分析方法进行了详细介绍。     

The WIG (weighted individual and group) shrinkage estimator

Regularization, or shrinkage estimation, refers to a class of statistical methods that constrain the variability of parameter estimates when fitting models to data. These constraints move parameters toward a group mean or toward a fixed point (e.g., 0). Regularization has gained popularity across many fields for its ability to increase predictive power over classical techniques. However, articles published in JEAB and other behavioral journals have yet to adopt these methods. This paper reviews some common regularization schemes and speculates as to why articles published in JEAB do not use them. In response, we propose our own shrinkage estimator that avoids some of the possible objections associated with the reviewed regularization methods. Our estimator works by mixing weighted individual and group (WIG) data rather than by constraining parameters. We test this method on a problem of model selection. Specifically, we conduct a simulation study on the selection of matching‐law‐based punishment models, comparing WIG with ordinary least squares (OLS) regression, and find that, on average, WIG outperforms OLS in this context.     

Robust inference with binary data

In this paper robustness properties of the maximum likelihood estimator (MLE) and several robust estimators for the logistic regression model when the responses are binary are analysed. It is found that the MLE and the classical Rao's score test can be misleading in the presence of model misspecification which in the context of logistic regression means either misclassification's errors in the responses, or extreme data points in the design space. A general framework for robust estimation and testing is presented and a robust estimator as well as a robust testing procedure are presented. It is shown that they are less influenced by model misspecifications than their classical counterparts. They are finally applied to the analysis of binary data from a study on breastfeeding.The author is partially supported by the Swiss National Science Foundation. She would like to thank Rand Wilcox, Eva Cantoni and Elvezio Ronchetti for their helpful comments on earlier versions of the paper, as well as Stephane Heritier for providing the routine to compute the OBRE.     

Prediction and classification in nonlinear data analysis: Something old,something new,something borrowed,something blue

Prediction and classification are two very active areas in modern data analysis. In this paper, prediction with nonlinear optimal scaling transformations of the variables is reviewed, and extended to the use of multiple additive components, much in the spirit of statistical learning techniques that are currently popular, among other areas, in data mining. Also, a classification/clustering method is described that is particularly suitable for analyzing attribute-value data from systems biology (genomics, proteomics, and metabolomics), and which is able to detect groups of objects that have similar values on small subsets of the attributes.This article is based on the Presidential Address Jacqueline Meulman gave on July 9, 2003 at the 68th Annual Meeting of the Psychometric Society held near Cagliari, Italy on the island of Sardinia.—Editor     

On the use of heterogeneous thresholds ordinal regression models to account for individual differences in response style

This paper proposes a general approach to accounting for individual differences in the extreme response style in statistical models for ordered response categories. This approach uses a hierarchical ordinal regression modeling framework with heterogeneous thresholds structures to account for individual differences in the response style. Markov chain Monte Carlo algorithms for Bayesian inference for models with heterogeneous thresholds structures are discussed in detail. A simulation and two examples based on ordinal probit models are given to illustrate the proposed methodology. The simulation and examples also demonstrate that failing to account for individual differences in the extreme response style can have adverse consequences for statistical inferences.The author is grateful to Ulf Böckenholt, an associate editor, and three anonymous reviewers for helpful comments, and Kristine Kuhn and Kshiti Joshi for providing the data.     

Driving errors,estimated performance and individual characteristics under simulated and real road traffic conditions – A validation study

BackgroundThe suitability of driving simulators for the prediction of driving behaviour in road traffic has been able to be confirmed in respect of individual assessment parameters. However, there is a need for overarching approaches that take into account the interaction between various influencing factors in order to establish proof of validity. The aim of this study was to explore the validity of our driving simulator in respect of its ability to predict driving behaviour based on participants‘ observed driving errors and driver’s individual characteristics.Method41 healthy participants were assessed both in a Smart-Realo-Simulator and on the road. By means of linear modelling, the correlation between observed driving errors was investigated. In addition, the influence of self-reported and externally assessed driving behaviour as well as individual parameters (education and training; driving history) were analysed.ResultsBy including these factors, 58% of the variance could be explained. For observed driving errors, a relative validity was established. For self-reported and externally assessed driving behaviour, an absolute to relative validity emerged. The amount of time spent in education and training proved to have a significant influence on driving performance in the simulator, but not on the road.DiscussionIn general, our results confirmed the validity of our driving simulator with regard to observed and self-reported driving behaviour. It emerged that education and training as potential indicators of cognitive resources played a differential role regarding the study conditions. Since real road driving is considerably automated in experienced drivers, this result suggests that simulation-related behavioural regulation is challenged by additional cognitive demands as opposed to behavioural regulation extending to real road driving. However, the source of these additional cognitive demands remains currently elusive and may form the subject of future research.     

Treatment effects on count outcomes with non-normal covariates

The effects of a treatment or an intervention on a count outcome are often of interest in applied research. When controlling for additional covariates, a negative binomial regression model is usually applied to estimate conditional expectations of the count outcome. The difference in conditional expectations under treatment and under control is then defined as the (conditional) treatment effect. While traditionally aggregates of these conditional treatment effects (e.g., average treatment effects) are computed by averaging over the empirical distribution, a recently proposed moment-based approach allows for computing aggregate effects as a function of distribution parameters. The moment-based approach makes it possible to control for (latent) multivariate normally distributed covariates and provides more reliable inferences under certain conditions. In this paper we propose three different ways to account for non-normally distributed continuous covariates in this approach: an alternative, known non-normal distribution; a plausible factorization of the joint distribution; and an approximation using finite Gaussian mixtures. A saturated model is used for categorical covariates, making a distributional assumption obsolete. We further extend the moment-based approach to allow for multiple treatment conditions and the computation of conditional effects for categorical covariates. An illustrative example highlighting the key features of our extension is provided.     

Concurrent schedules: How responses per reinforcer affects estimates of sensitivity to reinforcement

Conventionally, when choice is measured under concurrent schedules, all responses are included. However, the class of all responses consists of 2 sub-classes which are discriminable by their properties: Reinforced responses always equal obtained reinforcers, while unreinforced responses are free to vary. As a result, the inclusion of reinforced responses in choice measures results in sensitivity values in generalized matching that are biased toward larger values, and this bias becomes extreme in some combinations of overall response and reinforcer rates. Different ways of varying concurrent-schedule values also affect estimated sensitivity and the linearity between choice based on all responses and reinforcer ratios. To avoid spurious results and comparisons, and as a matter of good practice, generalized-matching fits and measures should be done using only unreinforced responses.     

The influence of transformational leadership on organisational citizenship behaviour in a South African combat military unit

This study investigated perceived individual transformational leadership (TL) to influence behaviours on follower organisational citizenship behaviour (OCB) in a South African combat military unit. A sample of 300 of 567 military followers (males = 78%; black Africans = 82%) completed a survey on their perceptions of transformational leadership qualities among their leaders who were trained in TL: Idealised influence, Inspirational motivation, Intellectual stimulation and Individualised consideration. The military followers also self-reported on their own OCB: Altruism, Civic virtue, Courtesy, Sportsmanship and Conscientiousness. Data were analysed utilising regression analysis to predict military followers’ self-reported OCB from perceived TL behaviours. Findings indicated that only Individualised consideration could significantly predict the OCB dimensions of Altruism, Civic virtue, Sportsmanship and Conscientiousness, but not Courtesy.