CHull: A generic convex-hull-based model selection method

Exploring Within-Person Variability in Qualitative Negative and Positive Emotional Granularity by Means of Latent Markov Factor Analysis

Emotional granularity (EG) is an individual's ability to describe their emotional experiences in a nuanced and specific way. In this paper, we propose that researchers adopt latent Markov factor analysis (LMFA) to investigate within-person variability in qualitative EG (i.e., variability in distinct granularity patterns between specific emotions across time). LMFA clusters measurement occasions into latent states according to state-specific measurement models. We argue that state-specific measurement models of repeatedly assessed emotion items can provide information about qualitative EG at a given point in time. Applying LMFA to the area of EG for negative and positive emotions separately by using data from an experience sampling study with 11,662 measurement occasions across 139 participants, we found three latent EG states for the negative emotions and three for the positive emotions. Momentary stress significantly predicted transitions between the EG states for both the negative and positive emotions. We further identified two and three latent classes of individuals who differed in state trajectories for negative and positive emotions, respectively. Neuroticism and dispositional mood regulation predicted latent class membership for negative (but not for positive) emotions. We conclude that LMFA may enrich EG research by enabling more fine-grained insights into variability in qualitative EG patterns.

Tucker3-PCovR: The Tucker3 principal covariates regression model

In behavioral research, it is very common to have manage multiple datasets containing information about the same set of individuals, in such a way that one dataset attempts to explain the others. To address this need, in this paper the Tucker3-PCovR model is proposed. This model is a particular case of PCovR models which focuses on the analysis of a three-way data array and a two-way data matrix where the latter plays the explanatory role. The Tucker3-PCovR model reduces the predictors to a few components and predicts the criterion by using these components and, at the same time, the three-way data is fitted by the Tucker3 model. Both the reduction of the predictors and the prediction of the criterion are done simultaneously. An alternating least squares algorithm is proposed to estimate the Tucker3-PCovR model. A biplot representation is presented to facilitate the interpretation of the results. Some applications are made to empirical datasets from the field of psychology.

How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we provide a step-by-step tutorial for the new user-friendly software package lmfa, which allows researchers to easily perform the analysis LMFA in the freely available software R to investigate MM differences in their own ILD.

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects' observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, Vogelsmeier, Vermunt, van Roekel, and De Roover (2019) introduced a one-step (full information maximum likelihood; FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.

Measurement properties of the Brain Balance® multidomain developmental survey: validated factor structure, internal reliability, and measurement invariance

This study aimed to refine and validate a multidomain developmental survey (MDS) used by the Brain Balance® (BB) program. Data were analyzed on 47,571 participants (68.5% male; 4–18 years) whose parents completed the survey before/after 3 months of in-center BB participation. Exploratory Factor Analysis was applied to a training sample (n = 28,254), reducing the original item pool from 98 to 31 items and suggesting a six-factor solution. The six factors were labeled as negative emotionality, reading/writing difficulties, hyperactive-disruptive, academic disengagement, motor/coordination problems, and social communication problems. Exploratory Structural Equation Modeling was applied to two validation samples (n = 9394 and 9923), and the factor structure demonstrated strong goodness-of-fit. Test–retest reliability coefficients (per Pearson correlations) were high for each of the subscales. Finally, the survey’s factor structure appeared equivalent across four groups stratified by reported gender and adolescent status. Overall, the BB-MDS demonstrated strong measurement properties, including validated factor structure, internal reliability, test–retest reliability, and measurement invariance.

Clusterwise Independent Component Analysis (C-ICA): Using fMRI resting state networks to cluster subjects and find neurofunctional subtypes

BACKGROUND

FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously.

NEW METHOD

We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs.

RESULTS

In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster.

COMPARISON WITH OTHER METHODS

Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods.

CONCLUSIONS

The successful performance of C-ICA indicates that it is a promising method to extract neurofunctional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity.

Motivation and empathic accuracy during conflict interactions in couples: it's complicated!

The aim of this study was to broadly investigate the role of relationship-, self-, and partner-serving motivation in empathic accuracy in couples' conflict interactions. To this end, a laboratory study was set up in which couples (n = 172) participated in a conflict interaction task, followed immediately by a video-review task during which they reported their own feelings and thoughts and inferred those of their partner to assess empathic accuracy. We used both trait and state measures of relationship-, self-, and partner-serving motivation, and we experimentally induced these three categories of motivation. Relationship-serving state motivation predicted greater empathic accuracy. In contrast, experimentally induced partner-serving motivation resulted in less empathic accuracy for men. Self-serving motivation was not found to be associated with empathic accuracy, nor were any of the trait measures. These findings underscore the complexity of the association between motivation and empathic accuracy in partners' conflict interactions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11031-022-09982-x.

A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models

The use of multilevel VAR(1) models to unravel within-individual process dynamics is gaining momentum in psychological research. These models accommodate the structure of intensive longitudinal datasets in which repeated measurements are nested within individuals. They estimate within-individual auto- and cross-regressive relationships while incorporating and using information about the distributions of these effects across individuals. An important quality feature of the obtained estimates pertains to how well they generalize to unseen data. Bulteel and colleagues (Psychol Methods 23(4):740-756, 2018a) showed that this feature can be assessed through a cross-validation approach, yielding a predictive accuracy measure. In this article, we follow up on their results, by performing three simulation studies that allow to systematically study five factors that likely affect the predictive accuracy of multilevel VAR(1) models: (i) the number of measurement occasions per person, (ii) the number of persons, (iii) the number of variables, (iv) the contemporaneous collinearity between the variables, and (v) the distributional shape of the individual differences in the VAR(1) parameters (i.e., normal versus multimodal distributions). Simulation results show that pooling information across individuals and using multilevel techniques prevent overfitting. Also, we show that when variables are expected to show strong contemporaneous correlations, performing multilevel VAR(1) in a reduced variable space can be useful. Furthermore, results reveal that multilevel VAR(1) models with random effects have a better predictive performance than person-specific VAR(1) models when the sample includes groups of individuals that share similar dynamics.

An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition

In recent years, a growing number of large, densely populated cities have emerged, which need urban traffic planning and therefore knowledge of mobility patterns. Knowledge of space-time distribution of passengers in cities is necessary for effective urban traffic planning and restructuring, especially in large cities. In this paper, the inbound ridership in the Barcelona metro is modelled into a three-way tensor so that each element contains the number of passenger in the ith station at the jth time on the kth day. Tucker3 decomposition is used to discover spatial clusters, temporal patterns, and the relationships between them. The results indicate that travel patterns differ between weekdays and weekends; in addition, rush and off-peak hours of each day have been identified, and a classification of stations has been obtained.

ConNEcT: A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time

Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.

The role of social connection on the experience of COVID-19 related post-traumatic growth and stress

BACKGROUND

Historically social connection has been an important way through which humans have coped with large-scale threatening events. In the context of the COVID-19 pandemic, lockdowns have deprived people of major sources of social support and coping, with others representing threats. Hence, a major stressor during the pandemic has been a sense of social disconnection and loneliness. This study explores how people's experience of compassion and feeling socially safe and connected, in contrast to feeling socially disconnected, lonely and fearful of compassion, effects the impact of perceived threat of COVID-19 on post-traumatic growth and post-traumatic stress.

METHODS

Adult participants from the general population (N = 4057) across 21 countries worldwide, completed self-report measures of social connection (compassion for self, from others, for others; social safeness), social disconnection (fears of compassion for self, from others, for others; loneliness), perceived threat of COVID-19, post-traumatic growth and traumatic stress.

RESULTS

Perceived threat of COVID-19 predicted increased post-traumatic growth and traumatic stress. Social connection (compassion and social safeness) predicted higher post-traumatic growth and traumatic stress, whereas social disconnection (fears of compassion and loneliness) predicted increased traumatic symptoms only. Social connection heightened the impact of perceived threat of COVID-19 on post-traumatic growth, while social disconnection weakened this impact. Social disconnection magnified the impact of the perceived threat of COVID-19 on traumatic stress. These effects were consistent across all countries.

CONCLUSIONS

Social connection is key to how people adapt and cope with the worldwide COVID-19 crisis and may facilitate post-traumatic growth in the context of the threat experienced during the pandemic. In contrast, social disconnection increases vulnerability to develop post-traumatic stress in this threatening context. Public health and Government organizations could implement interventions to foster compassion and feelings of social safeness and reduce experiences of social disconnection, thus promoting growth, resilience and mental wellbeing during and following the pandemic.

Clustering Individuals on Limited Features of a Vector Autoregressive Model

Dynamical interplays in emotions have been investigated using vector autoregressive (VAR) models, whose estimates can be used to cluster participants into unknown groups. The present study evaluated a clustering algorithm, the alternating least square (ALS) algorithm, for accuracy in predicting individual group membership. We systematically manipulated (a) the number of variables in a model, (b) the size of group differences in regression coefficients, and (c) the number of regression coefficients that vary across the groups (i.e., effective features). The ALS algorithm works reliably when there are at least 5 effective features with very large group differences in a 5-variable model; and 9 effective features with very large group differences in a 10-variable model. These findings suggest that the ALS algorithm is sensitive to group differences that are present only in several coefficients of a VAR model, but that the group differences have to be large. We also found that the ALS algorithm outperforms another clustering method, Gaussian mixture modeling. The ALS algorithm was further evaluated with unbalanced sample sizes between groups and with a greater number of groups in data (Study 2). A real data application was provided to illustrate how to interpret the detected group differences (Study 3).

Emotional Experiences of Cybersecurity Breach Victims

This study investigated emotional reactions to cybersecurity breaches. Based on prior research, a context-specific instrument was developed. This new instrument covered all five emotion components identified by the componential emotion approach. In total, 145 participants that experienced a cybersecurity breach reported on their appraisals, action tendencies, bodily reactions, expressions, subjective feelings, and regulation attempts. A principal component analysis on a total of 75 emotion reactions revealed a clear three-dimensional structure. The first dimension represented the extent to which the person was generally emotionally affected. The second dimension revealed constructive action tendencies and subjective feelings that were opposed to unconstructive action tendencies, expressions, and bodily reactions. The third dimension revealed cognitive motivational reactions that were opposed to affective reactions. This study clearly indicated that cybersecurity breaches do not only form a challenge for engineers, but also have important psychological ramifications that need to be addressed. Although some people have a tendency to react with constructive and proactive actions that are likely to limit the negative consequences of the cybersecurity breach, others experience a strong negative affective stress reaction and are unlikely to take the appropriate steps to deal with the security breach situation. These people, especially, can be expected to be vulnerable to psychological complaints and possibly psychopathology. The newly developed instrument uses a comprehensive approach to assess emotional reactions to cybersecurity threats and provides an efficient way to identify potentially problematic reactions.

Insight Into Individual Differences in Emotion Dynamics With Clustering

Studying emotion dynamics through time series models is becoming increasingly popular in the social sciences. Across individuals, dynamics can be rather heterogeneous. To enable comparisons and generalizations of dynamics across groups of individuals, one needs sophisticated tools that express the essential similarities and differences. A way to proceed is to identify subgroups of people who are characterized by qualitatively similar emotion dynamics through dynamic clustering. So far, these methods assume equal generating processes for individuals per cluster. To avoid this overly restrictive assumption, we outline a probabilistic clustering approach based on a mixture model that clusters on individuals' vector autoregressive coefficients. We evaluate the performance of the method and compare it with a nonprobabilistic method in a simulation study. The usefulness of the methods is illustrated using 366 ecological momentary assessment time series with external measures of depression and anxiety.

REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

We present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

Deterministic blockmodelling of signed and two-mode networks: A tutorial with software and psychological examples

Deterministic blockmodelling is a well-established clustering method for both exploratory and confirmatory social network analysis seeking partitions of a set of actors so that actors within each cluster are similar with respect to their patterns of ties to other actors (or, in some cases, other objects when considering two-mode networks). Even though some of the historical foundations for certain types of blockmodelling stem from the psychological literature, applications of deterministic blockmodelling in psychological research are relatively rare. This scarcity is potentially attributable to three factors: a general unfamiliarity with relevant blockmodelling methods and applications; a lack of awareness of the value of partitioning network data for understanding group structures and processes; and the unavailability of such methods on software platforms familiar to most psychological researchers. To tackle the first two items, we provide a tutorial presenting a general framework for blockmodelling and describe two of the most important types of deterministic blockmodelling applications relevant to psychological research: structural balance partitioning and two-mode partitioning based on structural equivalence. To address the third problem, we developed a suite of software programs that are available as both Fortran executable files and compiled Fortran dynamic-link libraries that can be implemented in the R software system. We demonstrate these software programs using networks from the literature.

PCovR2: A flexible principal covariates regression approach to parsimoniously handle multiple criterion variables

Principal covariates regression (PCovR) allows one to deal with the interpretational and technical problems associated with running ordinary regression using many predictor variables. In PCovR, the predictor variables are reduced to a limited number of components, and simultaneously, criterion variables are regressed on these components. By means of a weighting parameter, users can flexibly choose how much they want to emphasize reconstruction and prediction. However, when datasets contain many criterion variables, PCovR users face new interpretational problems, because many regression weights will be obtained and because some criteria might be unrelated to the predictors. We therefore propose PCovR2, which extends PCovR by also reducing the criteria to a few components. These criterion components are predicted based on the predictor components. The PCovR2 weighting parameter can again be flexibly used to focus on the reconstruction of the predictors and criteria, or on filtering out relevant predictor components and predictable criterion components. We compare PCovR2 to two other approaches, based on partial least squares (PLS) and principal components regression (PCR), that also reduce the criteria and are therefore called PLS2 and PCR2. By means of a simulated example, we show that PCovR2 outperforms PLS2 and PCR2 when one aims to recover all relevant predictor components and predictable criterion components. Moreover, we conduct a simulation study to evaluate how well PCovR2, PLS2 and PCR2 succeed in finding (1) all underlying components and (2) the subset of relevant predictor and predictable criterion components. Finally, we illustrate the use of PCovR2 by means of empirical data.

Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

In this work, we propose a new method for modeling human reasoning about objects’ similarities. We assume that similarity depends on perceived intensities of objects’ attributes expressed by natural language expressions such as low, medium, and high. We show how to find the underlying structure of the matrix with intensities of objects’ similarities in the factor-analysis-like manner. The demonstrated approach is based on fuzzy logic and set theory principles, and it uses only maximum and minimum operators. Similarly to classic eigenvector decomposition, we aim at representing the initial linguistic ordinal-scale (LOS) matrix as a max–min product of other LOS matrix and its transpose. We call this reconstructing matrix a neuromatrix because we assume that such a process takes place at the neural level in our brain. We show and discuss on simple, illustrative examples, how the presented way of modeling grasps natural way of reasoning about similarities. The unique characteristics of our approach are treating smaller attribute intensities as less important in making decisions about similarities. This feature is consistent with how the human brain is functioning at a biological level. A neuron fires and passes information further only if input signals are strong enough. The proposal of the heuristic algorithm for finding the decomposition in practice is also introduced and applied to exemplary data from classic psychological studies on perceived similarities between colors and between nations. Finally, we perform a series of simulation experiments showing the effectiveness of the proposed heuristic.

Detecting which variables alter component interpretation across multiple groups: A resampling-based method

In psychology, many studies measure the same variables in different groups. In the case of a large number of variables when a strong a priori idea about the underlying latent construct is lacking, researchers often start by reducing the variables to a few principal components in an exploratory way. Herewith, one often wants to evaluate whether the components represent the same construct in the different groups. To this end, it makes sense to remove outlying variables that have significantly different loadings on the extracted components across the groups, hampering equivalent interpretations of the components. Moreover, identifying such outlying variables is important when testing theories about which variables behave similarly or differently across groups. In this article, we first scrutinize the lower bound congruence method (LBCM; De Roover, Timmerman, & Ceulemans in Behavior Research Methods, 49, 216-229, 2017), which was recently proposed for solving the outlying-variable detection problem. LBCM investigates how Tucker's congruence between the loadings of the obtained cluster-loading matrices improves when specific variables are discarded. We show that LBCM has the tendency to output outlying variables that either are false positives or concern very small, and thus practically insignificant, loading differences. To address this issue, we present a new heuristic: the lower and resampled upper bound congruence method (LRUBCM). This method uses a resampling technique to obtain a sampling distribution for the congruence coefficient, under the hypothesis that no outlying variable is present. In a simulation study, we show that LRUBCM outperforms LBCM. Finally, we illustrate the use of the method by means of empirical data.

Perceptual errors are related to shifts in generalization of conditioned responding

Studies of perceptual generalization have recently demonstrated a close relationship between stimulus perception and conditioned responding, suggesting that incorrect stimulus perception might account for certain characteristics of generalization gradients. In this study, we investigated whether common phenomena, such as the area and peak shift in conditioned responding, relate to perceptual errors. After a differential conditioning procedure, in which one circle was paired with the presentation of an aversive picture whereas a different-sized circle was not, we combined a generalization test with a three-alternative forced-choice perceptual categorization task where participants had to indicate on every trial whether the presented circle was one of the two circles from the conditioning phase or a different one, after which US-expectancy ratings were collected. The typical peak and area shift were observed when conditioned responses were plotted on a physical dimension. However, when stimulus perception was incorporated generalization gradients diverged from the typical gradient. Both the area and peak shift largely disappeared when accounting for perceptual errors. These findings demonstrate the need to incorporate perceptual mechanisms in associative models.

Investigating the interplay between parenting dimensions and styles, and the association with adolescent outcomes

Research has indicated that a strictly dimensional or parental style approach does not capture the full complexity of parenting. To better understand this complexity, the current study combined these two approaches using a novel statistical technique, i.e., subspace K-means clustering. Four objectives were addressed. First, the study tried to identify meaningful groups of parents in longitudinal adolescent reports on parenting behaviour. Second, the dimensional structure of every cluster was inspected to uncover differences in parenting between and within clusters. Third, the parenting styles were compared on several adolescent characteristics. Fourth, to examine the impact of change in parenting style over time, we looked at the cluster membership over time. Longitudinal questionnaire data were collected at three annual waves, with 1,116 adolescents (mean age = 13.79 years) at wave 1. Based on five parenting dimensions (support and proactive, punitive, psychological and harsh control), subspace K-means clustering, analysed per wave separately, identified two clusters (authoritative and authoritarian parenting) in which parenting dimensions were interrelated differently. Authoritative parenting seemed to be beneficial for adolescent development (less externalising problem behaviour and higher self-concept). Longitudinal data revealed several parenting group trajectories which showed differential relations with adolescent outcomes. Change in membership from the authoritative cluster to the authoritarian cluster was associated with a decrease in self-concept and an increase in externalising problem behaviour, whereas changes from the authoritarian cluster to the authoritative cluster were associated with an increase in self-concept and a decrease in externalising problem behaviour.

Revealing the Joint Mechanisms in Traditional Data Linked With Big Data

Recent technological advances have made it possible to study human behavior by linking novel types of data to more traditional types of psychological data, for example, linking psychological questionnaire data with genetic risk scores. Revealing the variables that are linked throughout these traditional and novel types of data gives crucial insight into the complex interplay between the multiple factors that determine human behavior, for example, the concerted action of genes and environment in the emergence of depression. Little or no theory is available on the link between such traditional and novel types of data, the latter usually consisting of a huge number of variables. The challenge is to select - in an automated way - those variables that are linked throughout the different blocks, and this eludes currently available methods for data analysis. To fill the methodological gap, we here present a novel data integration method.

Direct and indirect effects of perception on generalization gradients

For more than a century, researchers have attempted to understand why organisms behave similarly across situations. Despite the robust character of generalization, considerable variation in conditioned responding both between and within humans remains a challenge for contemporary generalization models. The current study aims to investigate the extent to which variation in behavior in a context of generalization can be attributed to differences in perception. We combined a fear conditioning and generalization procedure with a perceptual decision task in humans. We found that the failure to perceive a novel stimulus as different from the trained fear-evoking stimulus led to increased conditioned responding. Furthermore, perceptual errors yielded perceived stimulus-outcome contingencies that differed substantially from the objective contingencies. Final, the impact of a perceptual error was dependent upon these perceived contingencies. These findings suggest that generalization across a perceptual dimension is to a large extent driven by perceptual errors that directly affect behavior but also indirectly as they yield different learning experiences between individuals.

Parenting Styles: A Closer Look at a Well-Known Concept

Although parenting styles constitute a well-known concept in parenting research, two issues have largely been overlooked in existing studies. In particular, the psychological control dimension has rarely been explicitly modelled and there is limited insight into joint parenting styles that simultaneously characterize maternal and paternal practices and their impact on child development. Using data from a sample of 600 Flemish families raising an 8-to-10 year old child, we identified naturally occurring joint parenting styles. A cluster analysis based on two parenting dimensions (parental support and behavioral control) revealed four congruent parenting styles: an authoritative, positive authoritative, authoritarian and uninvolved parenting style. A subsequent cluster analysis comprising three parenting dimensions (parental support, behavioral and psychological control) yielded similar cluster profiles for the congruent (positive) authoritative and authoritarian parenting styles, while the fourth parenting style was relabeled as a congruent intrusive parenting style. ANOVAs demonstrated that having (positive) authoritative parents associated with the most favorable outcomes, while having authoritarian parents coincided with the least favorable outcomes. Although less pronounced than for the authoritarian style, having intrusive parents also associated with poorer child outcomes. Results demonstrated that accounting for parental psychological control did not yield additional parenting styles, but enhanced our understanding of the pattern among the three parenting dimensions within each parenting style and their association with child outcomes. More similarities than dissimilarities in the parenting of both parents emerged, although adding psychological control slightly enlarged the differences between the scores of mothers and fathers.

Improved Insight into and Prediction of Network Dynamics by Combining VAR and Dimension Reduction

To understand within-person psychological processes, one may fit VAR(1) models (or continuous-time variants thereof) to multivariate time series and display the VAR(1) coefficients as a network. This approach has two major problems. First, the contemporaneous correlations between the variables will frequently be substantial, yielding multicollinearity issues. In addition, the shared effects of the variables are not included in the network. Consequently, VAR(1) networks can be hard to interpret. Second, crossvalidation results show that the highly parametrized VAR(1) model is prone to overfitting. In this article, we compare the pros and cons of two potential solutions to both problems. The first is to impose a lasso penalty on the VAR(1) coefficients, setting some of them to zero. The second, which has not yet been pursued in psychological network analysis, uses principal component VAR(1) (termed PC-VAR(1)). In this approach, the variables are first reduced to a few principal components, which are rotated toward simple structure; then VAR(1) analysis (or a continuous-time analog) is applied to the rotated components. Reanalyzing the data of a single participant of the COGITO study, we show that PC-VAR(1) has the better predictive performance and that networks based on PC-VAR(1) clearly represent both the lagged and the contemporaneous variable relations.

The Readiness to Reconcile Inventory

Abstract. Although awareness of the importance of reconciliation in post-conflict societies has grown in recent decades, validated measures assessing victims’ attitudes toward reconciliation are lacking. To fill this gap, the Readiness to Reconcile Inventory (RRI) was developed and its factor structure and aspects of construct validity were psychometrically tested in two independent samples of survivors of the Khmer Rouge regime in Cambodia. Exploratory factor analysis in a sample of N = 247 survivors identified a 13-item, three-factor internal structure of the RRI that was confirmed by confirmatory factor analysis in an independent sample of N = 830 survivors of the Khmer Rouge regime. All RRI subscales showed good internal consistency (Cronbach’s αs from .80 to .83). Multiple-group analysis established configural, metric, and scalar invariance across sex. The RRI thus demonstrated good reliability and fulfilled some aspects of construct validity. It is a time-effective and easy-to-administer instrument for assessing readiness to reconcile in victims of war and conflict.

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record

Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View

Horn's parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy-Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy-Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy-Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy-Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.

Clustering Vector Autoregressive Models: Capturing Qualitative Differences in Within-Person Dynamics

In psychology, studying multivariate dynamical processes within a person is gaining ground. An increasingly often used method is vector autoregressive (VAR) modeling, in which each variable is regressed on all variables (including itself) at the previous time points. This approach reveals the temporal dynamics of a system of related variables across time. A follow-up question is how to analyze data of multiple persons in order to grasp similarities and individual differences in within-person dynamics. We focus on the case where these differences are qualitative in nature, implying that subgroups of persons can be identified. We present a method that clusters persons according to their VAR regression weights, and simultaneously fits a shared VAR model to all persons within a cluster. The performance of the algorithm is evaluated in a simulation study. Moreover, the method is illustrated by applying it to multivariate time series data on depression-related symptoms of young women.

The Italian version of Perfectionistic Self-Presentation Scale: psychometric proprieties and its associations with pathological narcissism and adult attachment in an adult non clinical sample

The present study aims to evaluate the psychometric properties of the Italian version of the Perfectionistic Self-Presentation Scale (PSPS) in 447 nonclinical adult volunteers (63.5% female; mean age = 36.89 years). In our sample the PSPS total score and PSPS scales showed adequate internal consistency reliability estimates, and both the dimensionality analyses and WLSMV exploratory structural model supported the original three factors structure for PSPS items. We found a significant correlation between perfectionistic self-presentation and pathological narcissism and a significant role of attachment patterns in explaining the differences between these two constructs. Perfectionistic participants were characterized by avoidant and anxiety attachment styles, while narcissistic participants reported an anxiety style only. As a whole, our findings support the hypothesis that the Italian version of the PSPS is a reliable measure of perfectionist self-presentation in an adult community sample. Copyright © 2016 John Wiley & Sons, Ltd.

The Personality Inventory for DSM-5 Brief Form: Evidence for Reliability and Construct Validity in a Sample of Community-Dwelling Italian Adolescents

To assess the reliability and construct validity of the Personality Inventory for DSM-5 Brief Form (PID-5-BF) among adolescents, 877 Italian high school students were administered the PID-5-BF. Participants were administered also the Measure of Disordered Personality Functioning (MDPF) as a criterion measure. In the full sample, Cronbach's alpha values for the PID-5-BF scales ranged from .59 (Detachment) to .77 (Psychoticism); in addition, all PID-5-BF scales showed mean interitem correlation values in the .22 to .40 range. Cronbach's alpha values for the PID-5-BF total score was .83 (mean interitem r = .16). Although 2-month test-retest reliability could be assessed only in a small ( n = 42) subsample of participants, all PID-5-BF scale scores showed adequate temporal stability, as indexed by intraclass r values ranging from .78 (Negative Affectivity) to .97 (Detachment), all ps <.001. Exploratory structural equation modeling analyses provided at least moderate support for the a priori model of PID-5-BF items. Multiple regression analyses showed that PID-5-BF scales predicted a nonnegligible amount of variance in MDPF Non-Cooperativeness, adjusted R² = .17, p < .001, and Non-Coping scales, adjusted R² = .32, p < .001. Similarly, the PID-5-BF total score was a significant predictor of both MDPF Non-Coping, and Non-Cooperativeness scales.

Biclustering methods for one-mode asymmetric matrices

An asymmetric one-mode data matrix has rows and columns that correspond to the same set of objects. However, the roles of the objects frequently differ for the rows and the columns. For example, in a visual alphabetic confusion matrix from an experimental psychology study, both the rows and columns pertain to letters of the alphabet. Yet the rows correspond to the presented stimulus letter, whereas the columns refer to the letter provided as the response. Other examples abound in psychology, including applications related to interpersonal interactions (friendship, trust, information sharing) in social and developmental psychology, brand switching in consumer psychology, journal citation analysis in any discipline (including quantitative psychology), and free association tasks in any subarea of psychology. When seeking to establish a partition of the objects in such applications, it is overly restrictive to require the partitions of the row and column objects to be identical, or even the numbers of clusters for the row and column objects to be the same. This suggests the need for a biclustering approach that simultaneously establishes separate partitions of the row and column objects. We present and compare several approaches for the biclustering of one-mode matrices using data sets from the empirical literature. A suite of MATLAB m-files for implementing the procedures is provided as a Web supplement with this article.

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data

The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype-phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain-SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated the findings of this data set.

Revealing interdyad differences in naturally occurring staff reactions to challenging behaviour of clients with severe or profound intellectual disabilities by means of Clusterwise Hierarchical Classes Analysis (HICLAS)

BACKGROUND

Investigating interdyad (i.e. couples of a client and their usual caregiver) differences in naturally occurring patterns of staff reactions to challenging behaviour (e.g. self-injurious, stereotyped and aggressive/destructive behaviour) of clients with severe or profound intellectual disabilities is important to optimise client-staff interactions. Most studies, however, fail to combine a naturalistic setup with a person-level analysis, in that they do not involve a careful inspection of the interdyad differences and similarities.

METHOD

In this study, the recently proposed Clusterwise Hierarchical Classes Analysis (HICLAS) method is adopted and applied to data of in which video fragments (recorded in a naturalistic setting) of a client showing challenging behaviour and the staff reacting to it were analysed. In a Clusterwise HICLAS analysis, the staff-client dyads are grouped into a number of clusters and the prototypical behaviour-reaction patterns that are specific for each cluster (i.e. interdyad differences and similarities) are revealed.

RESULTS

Clusterwise HICLAS discloses clear interdyad differences (and similarities) in the prototypical patterns of clients' challenging behaviour and the associated staff reactions, complementing and qualifying the results of earlier studies in which only general patterns were disclosed.

CONCLUSIONS

The usefulness and clinical relevance of Clusterwise HICLAS is demonstrated. In particular, Clusterwise HICLAS may capture idiosyncratic aspects of staff-client interactions, which may stimulate direct support workers to adopt person-centred support practices that take the specific abilities of the client into account.

Model selection for minimum-diameter partitioning

The minimum-diameter partitioning problem (MDPP) seeks to produce compact clusters, as measured by an overall goodness-of-fit measure known as the partition diameter, which represents the maximum dissimilarity between any two objects placed in the same cluster. Complete-linkage hierarchical clustering is perhaps the best-known heuristic method for the MDPP and has an extensive history of applications in psychological research. Unfortunately, this method has several inherent shortcomings that impede the model selection process, such as: (1) sensitivity to the input order of the objects, (2) failure to obtain a globally optimal minimum-diameter partition when cutting the tree at K clusters, and (3) the propensity for a large number of alternative minimum-diameter partitions for a given K. We propose that each of these problems can be addressed by applying an algorithm that finds all of the minimum-diameter partitions for different values of K. Model selection is then facilitated by considering, for each value of K, the reduction in the partition diameter, the number of alternative optima, and the partition agreement among the alternative optima. Using five examples from the empirical literature, we show the practical value of the proposed process for facilitating model selection for the MDPP.

The three-factor structure of the Levenson self-report psychopathy scale: fool's gold or true gold? A study in a sample of Italian adult non-clinical participants

The major aim of this study was to evaluate the factor structure of the Italian translation of the Levenson Self-Report Psychopathy Scale (LSRP) in a sample of 740 community dwelling adult participants. Hull method, minimum average partial analysis and quasi-inferential parallel analysis techniques were used to identify a three-factor solution that appeared broadly consistent with previous work. The three factors exhibited reliability coefficients >0.70, and the three-factor structure was adequately reproduced across gender, educational level and civil status strata (median congruence coefficients = 0.94, 0.93 and 0.95 respectively) and remained largely unchanged when the effect of participants' age was controlled for (median factor score correlation = 0.99). Although Factor 3 in our study was demarcated mainly by reverse-keyed items, the LSRP factors yielded meaningful relations with retrospective measures of antisocial behaviour in adolescence and HEXACO personality traits and were conceptually consistent with the triarchic model of psychopathy of Patrick, Fowles and Krueger (2009).

What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis

The issue of measurement invariance is ubiquitous in the behavioral sciences nowadays as more and more studies yield multivariate multigroup data. When measurement invariance cannot be established across groups, this is often due to different loadings on only a few items. Within the multigroup CFA framework, methods have been proposed to trace such non-invariant items, but these methods have some disadvantages in that they require researchers to run a multitude of analyses and in that they imply assumptions that are often questionable. In this paper, we propose an alternative strategy which builds on clusterwise simultaneous component analysis (SCA). Clusterwise SCA, being an exploratory technique, assigns the groups under study to a few clusters based on differences and similarities in the component structure of the items, and thus based on the covariance matrices. Non-invariant items can then be traced by comparing the cluster-specific component loadings via congruence coefficients, which is far more parsimonious than comparing the component structure of all separate groups. In this paper we present a heuristic for this procedure. Afterwards, one can return to the multigroup CFA framework and check whether removing the non-invariant items or removing some of the equality restrictions for these items, yields satisfactory invariance test results. An empirical application concerning cross-cultural emotion data is used to demonstrate that this novel approach is useful and can co-exist with the traditional CFA approaches.

DeCon: a tool to detect emotional concordance in multivariate time series data of emotional responding

The occurrence of concordance among different response components during an emotional episode is a key feature of several contemporary accounts and definitions of emotion. Yet, capturing such response concordance in empirical data has proven to be elusive, in large part because of a lack of appropriate statistical tools that are tailored to measure the intricacies of response concordance in the context of data on emotional responding. In this article, we present a tool we developed to detect two different forms of response concordance-response patterning and synchronization-in multivariate time series data of emotional responding, and apply this tool to data concerning physiological responding to emotional stimuli. While the findings provide partial evidence for both response patterning and synchronization, they also show that the presence and nature of such patterning and synchronization is strongly person-dependent.

CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers

Mixture analysis is commonly used for clustering objects on the basis of multivariate data. When the data contain a large number of variables, regular mixture analysis may become problematic, because a large number of parameters need to be estimated for each cluster. To tackle this problem, the mixtures-of-factor-analyzers (MFA) model was proposed, which combines clustering with exploratory factor analysis. MFA model selection is rather intricate, as both the number of clusters and the number of underlying factors have to be determined. To this end, the Akaike (AIC) and Bayesian (BIC) information criteria are often used. AIC and BIC try to identify a model that optimally balances model fit and model complexity. In this article, the CHull (Ceulemans & Kiers, 2006) method, which also balances model fit and complexity, is presented as an interesting alternative model selection strategy for MFA. In an extensive simulation study, the performances of AIC, BIC, and CHull were compared. AIC performs poorly and systematically selects overly complex models, whereas BIC performs slightly better than CHull when considering the best model only. However, when taking model selection uncertainty into account by looking at the first three models retained, CHull outperforms BIC. This especially holds in more complex, and thus more realistic, situations (e.g., more clusters, factors, noise in the data, and overlap among clusters).