Depression not only involves disturbances in prevailing affect, but also in how affect fluctuates over time. Yet, precisely which patterns of affect dynamics are associated with depressive symptoms remains unclear; depression has been linked with increased affective variability and instability, but also with greater resistance to affective change (inertia). In this paper, we argue that these paradoxical findings stem from a number of neglected methodological/analytical factors, which we address using a novel paradigm and analytic approach. Participants (N = 99), preselected to represent a wide range of depressive symptoms, watched a series of emotional film clips and rated their affect at baseline and following each film clip. We also assessed participants' affect in daily life over 1 week using experience sampling. When controlling for overlap between different measures of affect dynamics, depressive symptoms were independently associated with higher inertia of negative affect in the lab, and with greater negative affect variability both in the lab and in daily life. In contrast, depressive symptoms were not independently related to higher affective instability either in daily life or in the lab.

When analyzing data, researchers are often confronted with a model selection problem (e.g., determining the number of components/factors in principal components analysis [PCA]/factor analysis or identifying the most important predictors in a regression analysis). To tackle such a problem, researchers may apply some objective procedure, like parallel analysis in PCA/factor analysis or stepwise selection methods in regression analysis. A drawback of these procedures is that they can only be applied to the model selection problem at hand. An interesting alternative is the CHull model selection procedure, which was originally developed for multiway analysis (e.g., multimode partitioning). However, the key idea behind the CHull procedure-identifying a model that optimally balances model goodness of fit/misfit and model complexity-is quite generic. Therefore, the procedure may also be used when applying many other analysis techniques. The aim of this article is twofold. First, we demonstrate the wide applicability of the CHull method by showing how it can be used to solve various model selection problems in the context of PCA, reduced Kmeans, best-subset regression, and partial least squares regression. Moreover, a comparison of CHull with standard model selection methods for these problems is performed. Second, we present the CHULL software, which may be downloaded from http://ppw.kuleuven.be/okp/software/CHULL/, to assist the user in applying the CHull procedure.Keywords Model selection . CHULL . Graphical user interface . PCA . Regression . PLS When analyzing data, researchers very often face a (complex) model selection problem. Take, as a first example, a clinical psychologist who wants to study the dimensionality of a particular psychological construct, such as alexithymia (i.e., having difficulties distinguishing between and expressing emotions). To this end, the researcher administers to a sample of subjects a questionnaire that measures the construct in question. Next, the researcher may examine the internal structure of the questionnaire and the dimensionality of the underlying construct by performing a principal components analysis (PCA) or an exploratory factor analysis (EFA) on the collected data. In doing so, the researcher needs to determine the optimal number of components or factors, and thus has to solve a model selection problem. As a second example, consider an economist who wants to assess which "factors" affect the selling price of a house in a particular neighborhood. To study this, the researcher may collect data (e.g., price and size of the house or the number of rooms) about a sample of houses from the neighborhood under study and may perform a regression analysis. In this case, again a model selection problem arises, which consists of selecting the (sub)set of predictors (optionally also including interactions between the predictors) that optimally predicts the selling price of a house.In general, model selection boils down to selecting, out of a set of models, one that yields a good...

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In this paper, we introduce mobileQ, which is a free, open-source software that our lab has developed to use in experience sampling studies. Experience sampling studies have several strengths and are becoming more widely conducted, but there are few free software options. To address this gap, mobileQ has freely available servers, a web interface, and an Android app. To reduce the barrier to entry, it requires no high-level programming, and uses an easy point-andclick interface. It is designed to be used on dedicated research phones, allowing for experimenter control and eliminating selection bias. In this article, we introduce setting up a study in mobileQ, outline the set of help resources available for new users, and highlight the success with which mobileQ has been used in our lab.

In various fields, statistical models of interest are analytically intractable and inference is usually performed using a simulation-based method. However elegant these methods are, they are often painstakingly slow and convergence is difficult to assess. As a result, statistical inference is greatly hampered by computational constraints. However, for a given statistical model, different users, even with different data, are likely to perform similar computations. Computations done by one user are potentially useful for other users with different data sets. We propose a pooling of resources across researchers to capitalize on this. More specifically, we preemptively chart out the entire space of possible model outcomes in a prepaid database. Using advanced interpolation techniques, any individual estimation problem can now be solved on the spot. The prepaid method can easily accommodate different priors as well as constraints on the parameters. We created prepaid databases for three challenging models and demonstrate how they can be distributed through an online parameter estimation service. Our method outperforms state-of-the-art estimation techniques in both speed (with a 23,000 to 100,000-fold speed up) and accuracy, and is able to handle previously quasi inestimable models.