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© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Background: Visual Schema Displacement Therapy (VSDT) is a novel therapy for the treatment of fears and trauma-related mental health problems including PTSD. VSDT proved to be effective in reducing emotionality of aversive memories in healthy individuals in two previous randomized controlled trials and outperformed both a non-active control condition (CC) and an abbreviated version of EMDR therapy, a well-established first-line treatment for posttraumatic stress disorder. Objectives: In an effort to enhance the understanding concerning the efficacy of VSDT, and to determine its active components, a dismantling study was conducted in individuals with disturbing memories in which the effects of VSDT were tested against EMDR therapy, a non-active CC and three different VSDT-protocols, each excluding or altering a hypothesized active component. Method: Participants (N = 144) were asked to recall an emotional aversive event and were randomly assigned to one of these six interventions, each lasting 8 minutes. Emotional disturbance and vividness of participants’ memories were rated before and after the intervention and at one and four-week follow-up. Results: Replicatory Bayesian analyses supported hypotheses in which VSDT was superior to the CC and the EMDR condition in reducing emotionality, both directly after the intervention and at one week follow-up. However, at four-week follow-up, VSDT proved equal to EMDR while both treatments were superior to the CC. Concerning vividness the data also showed support for hypotheses predicting VSDT being equal to EMDR and both being superior to the CC in vividness reduction. Further analyses specifying differences between the abbreviated VSDT protocols detected no differences between these conditions. Conclusion: It remains unclear how VSDT yields its positive effects. Because VSDT appears to be unique and effective in decreasing emotionality of aversive memories, replication of the results in clinical samples is needed.

Data analysis in psychopathology research typically entails multiple stages of data preprocessing (e.g., coding of physiological measures), statistical decisions (e.g., inclusion of covariates), and reporting (e.g., selecting which variables best answer the research questions). The complexity and lack of transparency of these procedures have resulted in two troubling trends: the central hypotheses and analytical approaches are often selected after observing the data, and the research data are often not properly indexed. These practices are particularly problematic for (experimental) psychopathology research because the data are often hard to gather due to the target populations (e.g., individuals with mental disorders), and because the standard methodological approaches are challenging and time consuming (e.g., longitudinal studies). Here, we present a workflow that covers study preregistration, data anonymization, and the easy sharing of data and experimental material with the rest of the research community. This workflow is tailored to both original studies and secondary statistical analyses of archival data sets. In order to facilitate the implementation of the described workflow, we have developed a free and open-source software program. We argue that this workflow will result in more transparent and easily shareable psychopathology research, eventually increasing and replicability reproducibility in our research field.

Background : In eye movement desensitization and reprocessing (EMDR), patients make eye movements (EM) while recalling traumatic memories. Making EM taxes working memory (WM), which leaves less resources available for imagery of the memory. This reduces memory vividness and emotionality during future recalls. WM theory predicts that individuals with small working memory capacities (WMCs) benefit more from low levels of taxing (i.e., slow EM) whereas individuals with large WMC benefit more from high levels of taxing (i.e., fast EM). Objective : We experimentally examined and tested four prespecified hypotheses regarding the role of WMC and EM speed in reducing emotionality and vividness ratings: 1) EM—regardless of WMC and EM speed—are more effective compared to no dual task, 2) increasing EM speed only affects the decrease in memory ratings irrespective of WMC, 3) low-WMC individuals—compared to high-WMC individuals—benefit more from making either type of EM, 4) the EM intervention is most effective when—as predicted by WM theory—EM are adjusted to WMC. Method : Undergraduates with low ( n =31) or high ( n =35) WMC recalled three emotional memories and rated vividness and emotionality before and after each condition (recall only, recall + slow EM, and recall + fast EM). Results : Contrary to the theory, the data do not support the hypothesis that EM speed should be adjusted to WMC (hypothesis 4). However, the data show that a dual task in general is more effective in reducing memory ratings than no dual task (hypothesis 1), and that a more cognitively demanding dual task increases the intervention’s effectiveness (hypothesis 2). Conclusions : Although adjusting EM speed to an individual’s WMC seems a straightforward clinical implication, the data do not show any indication that such a titration is helpful. Keywords: Visual imagery; dual taxation; working memory capacity; EMDR (Published: 4 July 2016) Responsible Editor: Mirjam Nijdam, Academic Medical Center at the University of Amsterdam, Netherlands. This paper is part of the Special Issue: Eye Movement Desensitization and Reprocessing therapy research. More papers from this issue can be found at www.ejpt.net For the abstract or full text in other languages, please see Supplementary files in the column to the right (under ‘Article Tools’) Citation: European Journal of Psychotraumatology 2016, 7 : 29476 - http://dx.doi.org/10.3402/ejpt.v7.29476

Circular data is different from linear data and its analysis also requires methods different from conventional methods. In this study a Bayesian embedding approach to estimating circular regression models is investigated, by means of simulation studies, in terms of performance, efficiency, and flexibility. A new Markov chain Monte Carlo (MCMC) sampling method is proposed and contrasted to an existing method. An empirical example of a regression model predicting teachers’ scores on the interpersonal circumplex will be used throughout. Performance and efficiency are better for the newly proposed sampler and reasonable to good in most situations. Furthermore, the method in general is deemed very flexible. Additional research should be done that provides an overview of what circular data looks like in practice, investigates the interpretation of the circular effects and examines how we might conduct a way of hypothesis testing or model checking for the embedding approach.

This paper presents an introduction into Bayesian evaluation of informative hypotheses, that is, hypotheses representing explicit expectations about multiple group means (Hoijtink, 2011; Hoijtink, Klugkist and Boelen, 2008). The authors begin by discussing some limits of exploratory methods before presenting a non-technical overview of the Bayesian approach. References are provided for the technical details. A particular effort is made to illustrate the method with an example from psychology. References to software, more elaborate textbooks and tutorials enable researchers to apply this novel method to their own data.

When analyzing repeated measurements data, researchers often have expectations about the relations between the measurement means. The expectations can often be formalized using equality and inequality constraints between (i) the measurement means over time, (ii) the measurement means between groups, (iii) the means adjusted for time-invariant covariates, and (iv) the means adjusted for time-varying covariates. The result is a set of informative hypotheses. In this paper, the Bayes factor is used to determine which hypothesis receives most support from the data. A pivotal element in the Bayesian framework is the specification of the prior. To avoid subjective prior specification, training data in combination with restrictions on the measurement means are used to obtain so-called constrained posterior priors. A simulation study and an empirical example from developmental psychology show that this prior results in Bayes factors with desirable properties.

Psychological researchers in different fields sometimes encounter circular or directional data. Circular data are data measured in the form of angles or two-dimensional orientations. As an example, experiments investigating the development of spatial memory and the influence of visual experience on haptic orientation perception are presented. Three permutation tests are proposed for the evaluation of ordered hypotheses. The quality of the permutation tests is investigated by means of several simulation studies. The results of these studies show the expected increase in power when the permutation tests for ordered hypotheses are compared to a common non-directional test for circular data. The differences in power between the three tests for ordered alternatives are small.

In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this article is therefore to develop a Bayesian model in which a linear regression analysis on current data is augmented with the reported regression coefficients (and standard errors) of previous studies. Two versions of this model are presented. The first version incorporates previous studies through the prior density and is applicable when the current and all previous studies are exchangeable. The second version models all studies in a hierarchical structure and is applicable when studies are not exchangeable. Both versions of the model are assessed using simulation studies. Performance for each in estimating the regression coefficients is consistently superior to using current data alone and is close to that of an equivalent model that uses the data from previous studies rather than reported regression coefficients. Overall the results show that augmenting data with results from previous studies is viable and yields significant improvements in the parameter estimation.

When analyzing repeated measurements data, researchers often have expectations about the relations between the measurement means. The expectations can often be formalized using equality and inequality constraints between (i) the measurement means over time, (ii) the measurement means between groups, (iii) the means adjusted for time-invariant covariates, and (iv) the means adjusted for time-varying covariates. The result is a set of informative hypotheses. In this paper, the Bayes factor is used to determine which hypothesis receives most support from the data. A pivotal element in the Bayesian framework is the specification of the prior. To avoid subjective prior specification, training data in combination with restrictions on the measurement means are used to obtain so-called constrained posterior priors. A simulation study and an empirical example from developmental psychology show that this prior results in Bayes factors with desirable properties.

In objective Bayesian model selection, a well-known problem is that standard non-informative prior distributions cannot be used to obtain a sensible outcome of the Bayes factor because these priors are improper. The use of a small part of the data, i.e., a training sample, to obtain a proper posterior prior distribution has become a popular method to resolve this issue and seems to result in reasonable outcomes of default Bayes factors, such as the intrinsic Bayes factor or a Bayes factor based on the empirical expected-posterior prior. In this paper, it will be illustrated that such default methods may not result in sensible outcomes when evaluating inequality constrained models that are supported by the data. To resolve this issue, a default method is proposed for constructing so-called constrained posterior priors, which are inspired by the symmetrical intrinsic priors discussed by Berger and Mortera (1999) for a simple inequality constrained model selection problem. The resulting Bayes factors can be called “balanced” because model complexity of inequality constrained models is incorporated according to a specific definition that is presented in this paper.

The randomized response model is a misclassification design that is used to protect the privacy of respondents with respect to sensitive questions. Conditional misclassification probabilities are specified by the researcher and are therefore considered to be known. It is to be expected that some of the respondents do not complywith respect to the misclassification design. These respondents induce extra perturbation, which is not accounted for in the standard randomized response model. An extension of the randomized response model is presented that takes into account assumptions with respect to non-compliance under simple random sampling. The extended model is investigated using Bayesian inference. The research is motivated by randomized response data concerning violations of regulations for social benefit.

This dissertation deals with normal linear models with inequality constraints among model parameters. It consists of an introduction and four chapters that are papers submitted for publication. The first chapter introduces the use of inequality constraints. Scientists often have one or more theories or expectations with respect to the outcome of their empirical research. To evaluate these theories they have to be translated into statistical models. When scientists talk about the expected relations between variables if a certain theory is correct, their statements are often in terms of one or more parameters expected to be larger or smaller than one or more other parameters. Stated otherwise, their statements are often formulated using inequality constraints. In the second chapter, (frequentist) null hypothesis testing with inequality constrained alternatives is investigated. In the literature, tests for univariate normal models are available, however, the possibilities for multivariate constrained testing are limited. In this chapter a computational method to sample the null distribution of any test statistic in the context of both univariate and multivariate constrained alternative hypotheses is presented. The remainder of this thesis discusses Bayesian estimation and model selection in the context of (competing) inequality constrained normal linear models. It is very natural to include the inequality constraints in the prior distribution of the model parameters. Each theory constitutes it's own prior knowledge and therefore for each model the appropriate prior is defined. In Chapter 3, a motivation for Bayesian model selection as opposed to null hypothesis testing is provided. Subsequently, the Bayesian approach is explained in a non-technical way (the technical details can however be found in the two appendices). In this chapter, the methods that are used for the specification of the prior distributions and for the estimation of Bayes factors, are based on results from the fourth chapter. In Chapter 4, the idea of encompassing priors is introduced and examined. Since inequality constrained models are all nested in one unconstrained, encompassing model just one prior for this encompassing model is specified. The prior distributions for the constrained models follow from the so-called encompassing prior by truncation of the parameters space. Sensitivity to the prior distribution in this application is twofold. The differences between the theories are incorporated in the model specific priors by truncation of the parameter space. Sensitivity to the prior in this case is desirable. It provides information about the fit of each of the models, that is, of each of the theories. However, sensitivity to the specification of the encompassing prior is not desirable. In this chapter sensitivity to encompassing priors is examined and it is shown that for specific classes of models the selection is virtually objective, that is, independent of the encompassing prior. The encompassing prior also leads to a nice interpretation of Bayes factors. The Bayes factor for any constrained model with the encompassing model reduces to the ratio of two proportions, namely the proportion of respectively the encompassing prior and posterior, in agreement with the constraints. This enables easy and straightforward estimation of the Bayes factor and its standard error. It is also rather efficient because with only one sample from the encompassing prior and one sample from the encompassing posterior, Bayes factors for all pairs of models are obtained. The goal of the final chapter, is to show the potential of posterior model probabilities and model selection as an alternative for the use of p-values in traditional hypothesis testing.

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