Ordinal state-trait regression for intensive longitudinal data

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).     

定序变量回归模型在心理数据分析中的应用

定序变量在心理现象和心理数据中随处可见, 采用综合的定序变量回归分析模型可以对“镜像模式”和“漏斗模型”的心理现象做出合理的解释和预测。首先通过非参数检验对影响因素进行初步降维, 其次用Probit定序回归对降维后的影响因素贡献率进行判别, 从而进一步筛选具有显著性判断水平的有效指标, 最后用Logistic回归模型对某种特定的心理现象发生与否进行信息量足够大的解释和预测。大学毕业生工作生活质量满意度的预测对这种综合定序变量回归分析模型的实例拟合, 证实了综合定序变量回归分析模型在心理现象和心理数据分析中的应用价值。     

Pentheus rather than Oedipus: On perversion,survival and analytic ‘presencing’1

Following an introductory review of the main developments in the psychoanalytic thinking on perversion, the author focuses on her own understanding of perversion and its treatment, based on the psychoanalytic treatment of patients with severe sexual perversions. This paper uses the term ‘autotomy’ (borrowed from the fi eld of biology) to describe perversion formation as an ‘autotomous’ defence solution involving massive dissociative splitting in the service of psychic survival within a violent, traumatic early childhood situation; thus, a compulsively enacted ‘desire for ritualised trauma’ ensues. The specifi c nature of the perverse scenario embodies the specifi c experiential core quality of the traumatic situation. It is an actual repetition in the present of the imprint of a past destructive experience which is pre‐arranged and stage‐managed; it thus encounters haunting scenes of dread or psychic annihilation while, at the same time, controlling, sanitising and disavowing them. Hence, the world of severe perversion is no longer oedipal, but rather the world of Pentheus, Euripides's most tragic hero‐a world dominated by a mixture of a mother's madness, devourment, destruction and rituals of desire. According to this view, the (diffi cult) psychoanalytic treatment of perversion focuses on patient‐analyst interconnectedness‐brought about by the analyst's ‘givenness to being present’ or ‘presencing’‐at a deep, primary level of contact and impact (the emphasis being on the ontological dimension of experience). This evolving therapeutic entity creates and actualises a new, alternative experiential‐emotional reality within the pervert's alienated world, eventually generating a change in the perverse essence. The author illustrate this approach with three clinical vignettes.     

Early trauma and narcissism—autism bipolarity1

The analyst makes a series of considerations taken a posteriori from the analysis of a small number of patients. These patients have saved themselves from an early narcissistic catastrophe by developing precocious mental processes, while affective relationships rudimentarily repeat the impact with the original trauma. Primitive defences, essentially denial and vertical splitting, dissociate the tear in the psyche and structure a narcissism–autism bipolarity, revealed in aspects of the character which oblige the patient to automatically repeat a single matrix of experience. In therapy, it is necessary to construct a rst time of the trauma, by pending and linking threads of the primary relationship and strengthening them in the analytic relationship. This reconstruction of the background, a screen to project what had originally been rejected, is the prerequisite for coming out, in deferred action, from the hold of the pathological identifications. The author dedicates particular attention to the undifferentiated background, the nature‐environment torn by the trauma, and to the need to reconstruct this fabric of experience in the analytical relationship, as a fundamental element to the recomposition of the dissociated nuclei. In the clinical case, the analyst describes in particular how the analyst's words encounter an unbridgeable gap, a failure in the capacity for representation when opening the autistic nucleus. Through a regression lasting for about a year, a patient was able to live the experience of primitive agonies and that of an unbearable helplessness and, at the same time, was able to feel how the analyst supported her sense of existence. Subsequently, the patient was able to give shape, through visual images, to deep states of being and start the process of metabolising and symbolising the trauma.     

Meta‐CART: A tool to identify interactions between moderators in meta‐analysis

In the framework of meta‐analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta‐regression has difficulties in identifying interactions between them. To overcome this problem, meta‐CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The previous version of meta‐CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta‐CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta‐CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta‐regression trees with random‐effects weights and a 0.5‐standard‐error pruning rule perform best. The required sample size for meta‐CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.     

Order‐constrained linear optimization

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order‐constrained linear least‐squares (OCLO) optimization algorithm that maximizes the linear least‐squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out‐of‐sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat‐tailed distributions), OCLO shows less bias and variance, and substantially better out‐of‐sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data.     

AFFECT,SYMPTOM, FANTASY,DREAM: CLINICAL AND THEORETICAL CONSIDERATIONS

A symptom being studied in the process of analysis can be seen as not unlike the unconscious affect it sprang from. The author presents a case in which a symptom, premature ejaculation, was analogous to the unconscious affect of guilt, which itself seemed to be a premature defensive transformation of a deeper current of anger. Guilt was interpreted as if it were a psychic premature ejaculation, a defensive derailment of anger. Fantasy and dream seemed to be engaged in similar transformations, with a fantasy of “premature incarceration” not unlike the symptom itself in its analogous functioning. Analysis of affect, symptom, fantasy, and dream in complex, integrative analytic process led not only to resolution of the symptom itself, but also to a deeper understanding of the mind's complex functioning in general.     

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.     

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.     

Individual ambidexterity: the duality of exploration and exploitation and its relationship with innovative performance

Organizational ambidexterity has been established as an important antecedent of organizational innovation and performance. Recently, researchers have started to argue that ambidexterity is not only essential at the organizational, but also at the individual level. Thus, to be innovative, individuals need to engage in both explorative and exploitative behaviours. However, questions remain regarding the optimal balance of explorative and exploitative behaviours and how ambidexterity can be operationalized. At the organizational level, most empirical research utilized either the difference between, or the product of, exploration and exploitation. In this article, we criticize these approaches on conceptual and methodological grounds and argue for an alternative operationalization of ambidexterity: polynomial regression and response surface methodology. In two diary studies with daily and weekly data, we demonstrate the advantages of this approach. We discuss implications for ambidexterity research and innovation practice.