The different faces of (high) sensitivity, toward a more comprehensive measurement instrument: development and validation of the sensory processing sensitivity questionnaire (SPSQ).

Health and self-regulatio

Deriving optimal data-analytic regimes from benchmarking studies.

In benchmarking studies with simulated data sets in which two or more statistical methods are compared, over and above the search of a universally winning method, one may investigate how the winning method may vary over patterns of characteristics of the data or the data-generating mechanism. Interestingly, this problem bears strong formal similarities to the problem of looking for optimal treatment regimes in biostatistics when two or more treatment alternatives are available for the same medical problem or disease. It is outlined how optimal data-analytic regimes, that is to say, rules for optimally calling in statistical methods, can be derived from benchmarking studies with simulated data by means of supervised classification methods (e.g., classification trees). The approach is illustrated by means of analyses of data from a benchmarking study to compare two different algorithms for the estimation of a two-mode additive clustering model.Multivariate analysis of psychological dat

Assessing the effects of a real-life contact intervention on prejudice toward LGBT people

Prejudice against sexual and gender minorities (e.g., LGBT people) is quite prevalent and is harmful. We examined an existing-and often-used-contact intervention in pre-existing groups in an educational setting and assessed its effectiveness in reducing different forms of LGBT negativity. We focused particularly on modern LGBT negativity: a relatively subtle form of prejudice, involving ambivalence, denial, and/or the belief that there is too much attention for LGBT prejudice. We used a mixed design in which condition (experimental vs. control group) was the between-participants factor, which was randomized at the group level, and time (pretest vs. posttest vs. follow-up) was the within-participants factor (N = 117). Interventions were video recorded and the behavior of LGBT educators and participants was coded. Participants responded positively to the intervention, especially to the LGBT educator's "coming-out story." Exploratory analysis of the video data indicated that the perceived effectiveness of the intervention was higher in groups where participants were more engaged, although caution is necessary in interpreting this finding. The most important measure indicated that modern LGBT negativity decreased in the intervention groups directly after the intervention, but returned to baseline levels one week later. However, in the control condition, modern LGBT negativity had increased over time. Taken together, this suggests that an actual reduction in modern LGBT negativity was short-lived (i.e., the intervention effect disappeared within 7 days).Social decision makin

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 neuro-functional 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.Multivariate analysis of psychological dat

Exploring Mediators of a Guided Web-Based Self-Help Intervention for People With HIV and Depressive Symptoms: Randomized Controlled Trial

Multivariate analysis of psychological dat

Principal covariates clusterwise regression (PCCR): Accounting for multicollinearity and population heterogeneity in hierarchically organized data

In the behavioral sciences, many research questions pertain to a regression problem in that one wants to predict a criterion on the basis of a number of predictors. Although in many cases ordinary least squares regression will suffice, sometimes the prediction problem is more challenging, for three reasons: First, multiple highly collinear predictors can be available, making it difficult to grasp their mutual relations as well as their relations to the criterion. In that case, it may be very useful to reduce the predictors to a few summary variables, on which one regresses the criterion and which at the same time yields insight into the predictor structure. Second, the population under study may consist of a few unknown subgroups that are characterized by different regression models. Third, the obtained data are often hierarchically structured, with for instance, observations being nested into persons or participants within groups or countries. Although some methods have been developed that partially meet these challenges (i.e., Principal Covariates Regression –PCovR–, clusterwise regression –CR–, and structural equation models), none of these methods adequately deals with all of them simultaneously. To fill this gap, we propose the Principal Covariates Clusterwise Regression (PCCR) method, which combines the key idea’s behind PCovR (de Jong & Kiers, 1992) and CR (Späth, 1979). The PCCR method is validated by means of a simulation study and by applying it to cross-cultural data regarding satisfaction with life.Multivariate analysis of psychological dat

Prospective associations between home practice and depressive symptoms in mindfulness-based cognitive therapy for recurrent depression: a 15-months follow-up study

Stress and Psychopatholog

Interplay between self-compassion and affect during Mindfulness-Based Compassionate Living for recurrent depression: an Autoregressive Latent Trajectory analysis

Background: The current study aimed to investigate the possible interplay between self-compassion and affect during Mindfulness-Based Compassionate Living (MBCL) in recurrently depressed individuals.Methods: Data was used from a subsample of a parallel-group randomized controlled trial investigating the efficacy of MBCL in recurrently depressed adults (n = 104). Self-reports of self-compassion and positive/negative affect were obtained at the start of each of the eight MBCL sessions.Results: Bivariate Autoregressive Latent Trajectory (ALT) modeling showed that, when looking at the interplay between self-compassion and positive/negative affect on a session-to-session basis, no significant reciprocal cross-lagged effects between self-compassion and positive affect were found. Although there were no cross-lagged effects from negative affect to self-compassion, higher levels of self-compassion at each session did predict lower levels of negative affect at the subsequent session (b(SC(t-1),NA(t)) = -0.182, s.e. = 0.076, p =.017).Conclusions: The current study shows that increases in self-compassion are followed by decreases in negative affect in MBCL for depression.Stress-related psychiatric disorders across the life spa