Individual differences in anger and sadness: in pursuit of active situational features and psychological processes

In the cognitive-affective personality system (CAPS) theory of Mischel and Shoda (1995, 1998), personality is conceived as a system of cognitions and affects that mediates between active situational features and behavior. Two major tasks for this approach to personality are the search for active situational features and for mediating psychological processes within a behavioral domain of interest. We report two studies to address these tasks for the domain of anger and sadness. To design these studies and to analyze the obtained data, novel extensions of our previous developed Triple Typology Model (TTM; Vansteelandt & Van Mechelen, 1998) are proposed. These extensions allow the researcher to test hypotheses concerning potentially relevant active situational features in a systematic and confirmatory way and to examine psychological processes as they occur in concrete situations.     

A Mixed Model to Disentangle Variance and Serial Autocorrelation in Affective Instability Using Ecological Momentary Assessment Data

Affective instability, the tendency to experience emotions that fluctuate frequently and intensively over time, is a core feature of several mental disorders including borderline personality disorder. Currently, affect is often measured with Ecological Momentary Assessment protocols, which yield the possibility to quantify the instability of affect over time. A number of linear mixed models are proposed to examine (diagnostic) group differences in affective instability. The models contribute to the existing literature by estimating simultaneously both the variance and serial dependency component of affective instability when observations are unequally spaced in time with the serial autocorrelation (or emotional inertia) declining as a function of the time interval between observations. In addition, the models can eliminate systematic trends, take between subject differences into account and test for (diagnostic) group differences in serial autocorrelation, short-term as well as long-term affective variability. The usefulness of the models is illustrated in a study on diagnostic group differences in affective instability in the domain of eating disorders. Limitations of the model are that they pertain to group (and not individual) differences and do not focus explicitly on circadian rhythms or cycles in affect.     

On a statistic arising in testing correlation

This paper is devoted to the study of a certain statistic,u, defined on samples from a bivariate population with variances ₁₁, ₂₂ and correlation . Let the parameter corresponding tou be . Under binormality assumptions the following is demonstrated. (i) If ₁₁ = ₂₂, then the distribution ofu can be obtained rapidly from theF distribution. Statistical inferences about = may be based onF. (ii) In the general case, allowing for _{11 22}, a certain quantity involvingu,r (sample correlation between the variables) and follows at distribution. Statistical inferences about may be based ont. (iii) In the general case a quantityt may be constructed which involves only the statisticu and only the parameter . If treated like at distributed magnitude,t admits conservative statistical inferences. (iv) TheF distributed quantity mentioned in (i) is equivalent to a certaint distributed quantity as follows from an appropriate transformation of the variable. (v) Three test statistics are given which can be utilized in making statistical inferences about = in the case ₁₁ = ₂₂. A comparison of expected lengths of confidence intervals for obtained from the three test statistics is made. (vi) The use of the formulas derived is illustrated by means of an application to coefficient alpha.This research was supported by the National Institute of Child Health and Human Development, under Research Grant 1 PO1 HD01762.     

Orthogonal inter-battery factor analysis

It is the purpose of this paper to present a method of analysis for obtaining (i) inter-battery factors and (ii) battery specific factors for two sets of tests when the complete correlation matrix including communalities is given. In particular, the procedure amounts to constructing an orthogonal transformation such that its application to an orthogonal factor solution of the combined sets of tests results in a factor matrix of a certain desired form. The factors isolated are orthogonal but may be subjected to any suitable final rotation, provided the above classification of factors into (i) and (ii) is preserved. The general coordinate-free solution of the problem is obtained with the help of methods pertaining to the theory of linear spaces. The actual numerical analysis determined by the coordinate-free solution turns out to be a generalization of the formalism of canonical correlation analysis for two sets of variables. A numerical example is provided.This investigation has been supported by the U.S. Office of Naval Research under Contract Nonr-2752(00).     

When is it wrong to eat animals? The relevance of different animal traits and behaviours

Research suggests that animals’ capacity for agency, experience, and benevolence predict beliefs about their moral treatment. Four studies built on this work by examining how fine‐grained information about animals’ traits and behaviours (e.g., can store food for later vs. can use tools) shifted moral beliefs about eating and harming animals. The information that most strongly affected moral beliefs was related to secondary emotions (e.g., can feel love), morality (e.g., will share food with others), empathy (e.g., can feel others' pain), social connections (e.g., will look for deceased family members), and moral patiency (e.g., can feel pain). In addition, information affected moral judgements in line with how it affected superordinate representations about animals’ capacity for experience/feeling but not agency/thinking. The results provide a fine‐grained outline of how, and why, information about animals’ traits and behaviours informs moral judgements.     

Are emotionally intelligent people less prejudiced? The importance of emotion management skills for outgroup attitudes

People vary in their ability to understand, process, and manage information about one's own and others’ emotions, a construct known as Emotional Intelligence (EI). Past research highlighted the importance of EI in interpersonal relations as well as the key role of emotions underlying outgroup prejudice. Remarkably, hardly any research has investigated the associations between EI and outgroup prejudice. In three studies (total N = 922) conducted in Spain and the United Kingdom, we measured emotional intelligence using self-report and performance tests and prejudice toward a variety of outgroups. Results showed that those with stronger performance-based emotion management skills expressed lower generalized ethnic prejudice (Studies 1–3), more positive attitudes toward immigrants (Study 2a) and refugees (Study 2b), and less homophobic attitudes (Study 3). This negative association between emotion management and prejudice was found with different performance-based EI measures and held after controlling for self-perceived EI (Study 1) and self-reported abilities to regulate emotions (Study 3). Study 3 further demonstrated that higher empathy partly accounted for the association between emotion management and prejudice. The findings suggest that emotion management abilities play an important, but so far largely neglected role in generalized prejudice.     

Latent Class Models for Diary Method Data: Parameter Estimation by Local Computations

The increasing use of diary methods calls for the development of appropriate statistical methods. For the resulting panel data, latent Markov models can be used to model both individual differences and temporal dynamics. The computational burden associated with these models can be overcome by exploiting the conditional independence relations implied by the model. This is done by associating a probabilistic model with a directed acyclic graph, and applying transformations to the graph. The structure of the transformed graph provides a factorization of the joint probability function of the manifest and latent variables, which is the basis of a modified and more efficient E-step of the EM algorithm. The usefulness of the approach is illustrated by estimating a latent Markov model involving a large number of measurement occasions and, subsequently, a hierarchical extension of the latent Markov model that allows for transitions at different levels. Furthermore, logistic regression techniques are used to incorporate restrictions on the conditional probabilities and to account for the effect of covariates. Throughout, models are illustrated with an experience sampling methodology study on the course of emotions among anorectic patients. Frank Rijmen was partly supported by the Fund for Scientific Research Flanders (FWO).     

The LMPCA program: A graphical user interface for fitting the linked-mode PARAFAC-PCA model to coupled real-valued data

In behavioral research, PARAFAC analysis, a three-mode generalization of standard principal component analysis (PCA), is often used to disclose the structure of three-way three-mode data. To get insight into the underlying mechanisms, one often wants to relate the component matrices resulting from such a PARAFAC analysis to external (two-way two-mode) information, regarding one of the modes of the three-way data. To this end, linked-mode PARAFAC-PCA analysis can be used, in which the three-way and the two-way data set, which have one mode in common, are simultaneously analyzed. More specifically, a PARAFAC and a PCA model are fitted to the three-way and the two-way data, respectively, restricting the component matrix for the common mode to be equal in both models. Until now, however, no software program has been publicly available to perform such an analysis. Therefore, in this article, the LMPCA program, a free and easy-to-use MATLAB graphical user interface, is presented to perform a linked-mode PARAFAC-PCA analysis. The LMPCA software can be obtained from the authors at http://ppw.kuleuven.be/okp/software/LMPCA. For users who do not have access to MATLAB, a stand-alone version is provided.     

The role of individual differences in understanding and enhancing intergroup contact

In a world characterized by divisive rhetoric, heightened xenophobia, and other forms of prejudice, it is increasingly important to find effective ways of promoting functional intergroup relations. Research on the relationship between intergroup contact and individual differences substantially contributes to achieving this goal. We review research considering the role played by individual differences in moderating the relationship between contact and prejudice and predicting contact, but also as an outcome of contact. We then outline potential directions for future research, including identifying underlying mechanisms, examining the role of context at an intergroup and societal level, and considering how positive–negative contact asymmetry may be influenced by individual differences. We then call for a broader range of individual difference and contact outcomes to be explored and encourage utilization of new methodological advances in the study of intergroup contact.