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The imposition of lockdowns in response to the COVID-19 outbreak has underscored the importance of human behavior in mitigating virus transmission. The scientific study of interventions designed to change behavior (e.g., to promote physical distancing) requires measures of effectiveness that are fast, that can be assessed through experiments, and that can be investigated without actual virus transmission. This paper presents a methodological approach designed to deliver such indicators. We show how behavioral data, obtainable through wearable assessment devices or camera footage, can be used to assess the effect of interventions in experimental research; in addition, the approach can be extended to longitudinal data involving contact tracing apps. Our methodology operates by constructing a contact network: a representation that encodes which individuals have been in physical proximity long enough to transmit the virus. Because behavioral interventions alter the contact network, a comparison of contact networks before and after the intervention can provide information on the effectiveness of the intervention. We coin indicators based on this idea Behavioral Contact Network (BECON) indicators. We examine the performance of three indicators: the Density BECON, based on differences in network density; the Spectral BECON, based on differences in the eigenvector of the adjacency matrix; and the ASPL BECON, based on differences in average shortest path lengths. Using simulations, we show that all three indicators can effectively track the effect of behavioral interventions. Even in conditions with significant amounts of noise, BECON indicators can reliably identify and order effect sizes of interventions. The present paper invites further study of the method as well as practical implementations to test the validity of BECON indicators in real data.

The ability to measure emotional states in daily life using mobile devices has led to a surge of exciting new research on the temporal evolution of emotions. However, much of the potential of these data still remains untapped. In this paper, we re-analyze emotion measurements from seven openly available Experience Sampling Methodology (ESM) studies with a total of 835 individuals to systematically investigate the modality (unimodal, bimodal, more than two modes) and skewness of within-person emotion measurements. We show that both multimodality and skewness are highly prevalent. In addition, we quantify the heterogeneity across items, individuals, and measurement designs. Our analysis reveals that multimodality is more likely in studies using an analogue slider scale than in studies using a Likert scale; negatively valenced items are consistently more skewed than positive valenced items; and longer time series show a higher degree of modality in positive and a higher skew in negative items. We end by discussing the implications of our results for theorizing, measurement, and time series modeling.

Despite decades of scientific warnings, there is insufficient action to prevent climate breakdown. This is true at the government and corporate level, where we see the expansion of fossil fuel infrastructure, and at the individual level, where only a small proportion of the general public and the wider academic community engage in climate advocacy or activism. The latter is starting to change. Indeed, the enormous dangers ahead, coupled with the inadequacy of the political response, call for a reevaluation of the role of scientists and academics on a planet in crisis. In this paper, I (a) provide a concise assessment of our current predicament, (b) detail the ways in which both current as well as aspirational climate policy and mitigation pathways are insufficient to prevent climate breakdown, (c) sketch what climate policy commensurate with the scale of the challenge might look like, and (d) suggest ways in which scientists and academics in general, and social and behavioral scientists in particular, can get (more) involved to help secure a livable and sustainable future for all.

Most theories of emotion suggest that emotions are reactions to situations we encounter in daily life. Process theories of emotion further specify a feedback loop between our environment, attention, emotions, and action that clarifies the adaptive nature of emotions. In principle, such process theories describe how emotions develop in daily life, and consequently, emotion measures collected from individuals many times a day in studies using the Experience Sampling Methodology (ESM) should be highly useful in developing these theories. However, current emotion theories are predominantly verbal theories and therefore do not make clear predictions in such data. In this paper, we take a first step towards a generative model of emotion dynamics by formalizing the link between situations and emotions, which provides us with a basic generative model of emotions in daily life. We show that this incomplete model already reproduces nine stable phenomena in emotion time series related to (temporal) associations between emotions and their distributional form. We then discuss how we can draw on existing (process) theories of emotion to extend our basic model into a complete generative model of emotion dynamics. Finally, we discuss how generative models of emotion dynamics can facilitate theory development and advance measurement, study design, and statistical analysis.

Lockdowns and associated measures imposed in response to the COVID-19 crisis inflict severe damage to society. Across the globe, scientists and policymakers study ways to lift measures while maintaining control of virus spread in circumstances that continuously change due to the evolution of new variants and increasing vaccination coverage. In this process, it has become clear that finding and analysing exit strategies, which are a key aspect of pandemic mitigation in all consecutive waves of infection, is not solely a matter of epidemiological modeling but has many different dimensions that need to be balanced and therefore requires input from many different disciplines. Here, we document an attempt to investigate exit strategies from a multidisciplinary perspective through the Science versus Corona project in the Netherlands. In this project, scientists and laypeople were challenged to submit (components of) exit strategies. A selection of these were implemented in a formal model, and we have evaluated the scenarios from a multidisciplinary perspective, utilizing expertise in epidemiology, economics, psychology, law, mathematics, and history. We argue for the integration of multidisciplinary perspectives on COVID-19 and more generally in pandemic mitigation, highlight open challenges, and present an agenda for further research into exit strategies and their assessment.

One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t-test. However, frequentist t-tests do not quantify evidence and require various assumption tests. Recently popularized Bayesian t-tests do quantify evidence, but these were developed for scenarios where the two populations are assumed to have the same variance. As an alternative to both methods we outline a comprehensive t-test framework based on Bayesian model-averaging. This new t-test framework simultaneously takes into account models that assume equal and unequal variances, and models that use t-likelihoods to improve robustness to outliers. The resulting inference is based on a weighted average across the entire model ensemble, with higher weights assigned to models that predicted the observed data well. This new t-test framework provides an integrated approach to assumption checks and inference by applying a series of pertinent models to the data simultaneously rather than sequentially. The integrated Bayesian model-averaged t-tests achieve robustness without having to commit to a single model following a series of assumption checks. To facilitate practical application we provide user-friendly implementations via the RoBTT package in R.

Bury et al. (2021) present a powerful approach to anticipating tipping points based on deep learning that not only substantially outperforms traditional early warning indicators, but also classifies the type of bifurcation that may lie ahead. However, deep learning methods are notorious for sometimes exhibiting unintended behavior, and we show that this is also the case here. In particular, we find that the method did not extract features generic enough to classify stationary AR(1) processes that have not been detrended (or have been detrended using a Gaussian filter) as approaching no bifurcation. This suggests that using the same type of detrending as done during the training phase of the deep learning method is required for the method to work in practice.

A frequentist confidence interval can be constructed by inverting a hypothesis test, such that the interval contains only parameter values that would not have been rejected by the test. We show how a similar definition can be employed to construct a Bayesian support interval. Consistent with Carnap’s theory of corroboration, the support interval contains only parameter values that receive at least some minimum amount of support from the data. The support interval is not subject to Lindley’s paradox and provides an evidence-based perspective on inference that differs from the belief-based perspective that forms the basis of the standard Bayesian credible interval.

Contains fulltext : 226717.pdf (Publisher’s version ) (Closed access) Despite an ongoing stream of lamentations, many empirical disciplines still treat the p value as the sole arbiter to separate the scientific wheat from the chaff. The continued reign of the p value is arguably due in part to a perceived lack of workable alternatives. In order to be workable, any alternative methodology must be (1) relevant: it has to address the practitioners' research question, which - for better or for worse- most often concerns the test of a hypothesis, and less often concerns the estimation of a parameter; (2) available: it must have a concrete implementation for practitioners' statistical workhorses such as the t test, regression, and ANOVA; and (3) easy to use: methods that demand practitioners switch to the theoreticians' programming tools will face an uphill struggle for adoption. The above desiderata are fulfilled by Harold Jeffreys's Bayes factor methodology as implemented in the open-source software JASP. We explain Jeffreys's methodology and showcase its practical relevance with two examples. 9 p.

Many real-world systems can exhibit tipping points and multiple stable states, creating the potential for sudden and difficult to reverse transitions into a less desirable regime. The theory of dynamical systems points to the existence of generic early warning signals that may precede these so-called critical transitions. Recently, psychologists have begun to conceptualize mental disorders such as depression as an alternative stable state, and suggested that early warning signals based on the phenomenon of critical slowing down might be useful for predicting transitions into depression and other psychiatric disorders. Harnessing the potential of early warning signals requires us to understand their limitations as well as the factors influencing their performance in practice. In this article, we (a) review limitations of early warning signals based on critical slowing down to better understand when they can and cannot occur, and (b) study the conditions under which early warning signals may anticipate critical transitions in online-monitoring settings by simulating from a bistable dynamical system, varying crucial features such as sampling frequency, noise intensity, and speed of approaching the tipping point. We find that, in sharp contrast to their reputation of being generic or model-agnostic, whether early warning signals occur or not strongly depends on the specifics of the system. We also find that they are very sensitive to noise, potentially limiting their utility in practical applications. We discuss the implications of our findings and provide suggestions and recommendations for future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

ObjectivesDespite the paucity of evidence verifying its efficacy and safety, traditional Chinese medicine (TCM) is expanding in popularity and political support. Decisions to include TCM diagnoses in the International Classification of Diseases 11th Revision and campaigns to integrate TCM into national healthcare systems have occurred while public perception and usage of TCM, especially in Europe, remains undetermined. Accordingly, this study investigates TCM’s popularity, usage and perceived scientific support, as well as its relationship to homeopathy and vaccinations.Design/SettingWe performed a cross-sectional survey of the Austrian population. Participants were either recruited on the street (in-person) or online (web-link) via a popular Austrian newspaper.Participants1382 individuals completed our survey. The sample was poststratified according to data derived from Austria’s Federal Statistical Office.Outcome measuresAssociations between sociodemographic factors, opinion towards TCM and usage of complementary medicine (CAM) were investigated using a Bayesian graphical model.ResultsWithin our poststratified sample, TCM was broadly known (89.9% of women, 90.6% of men), with 58.9% of women and 39.5% of men using TCM between 2016 and 2019. Moreover, 66.4% of women and 49.7% of men agreed with TCM being supported by science. We found a positive relationship between perceived scientific support for TCM and trust in TCM-certified medical doctors (ρ=0.59, 95% CI 0.46 to 0.73). Moreover, perceived scientific support for TCM was negatively correlated with proclivity to get vaccinated (ρ=−0.26, 95% CI −0.43 to –0.08). Additionally, our network model yielded associations between TCM-related, homeopathy-related and vaccination-related variables.ConclusionsTCM is widely known within the Austrian general population and used by a substantial proportion. However, a disparity exists between the commonly held public perception that TCM is scientific and findings from evidence-based studies. Emphasis should be placed on supporting the distribution of unbiased, science-driven information.

IntroductionDespite the paucity of evidence verifying its efficacy and safety, traditional Chinese medicine (TCM) is expanding in popularity and political support. Decisions to include TCM diagnoses in the International Classification of Diseases 11th Revision (ICD-11) by the World Health Organization (WHO) and campaigns to integrate TCM into national healthcare systems have occurred whilst the public perception and usage of TCM, especially in Europe, remains undetermined. Accordingly, this study investigates the popularity, usage patterns, perception of scientific support for TCM, and its relationship to homeopathy.MethodsA cross-sectional survey was performed in Austria and data from 1382 participants were analysed. A Bayesian network model retrieved partial correlations indicating distinct associations between sociodemographic determinants, complementary and alternative medicine (CAM) usage patterns, readiness to vaccinate, and TCM related variables.ResultsTCM was broadly known by the Austrian population (89.9% of women, 90.6% of men), with 58.9% of women and 39.5% of men using TCM between 2016 and 2019. 66.4% of women and 49.7% of men agreed with TCM being supported by science. We found a strong positive relationship between the perceived scientific support for TCM and trust in TCM-certified medical doctors. Moreover, perceived scientific support for TCM was negatively correlated with the proclivity to get vaccinated. Additionally, our Bayesian network model yielded distinct associations between TCM-, homeopathy-, and vaccination-related variables.ConclusionTCM is widely known within the Austrian general population and actively used by a substantial proportion. However, a crucial disparity exists between the commonly held public perception that TCM is scientific and findings from evidence-based studies. As public opinion towards TCM, and the proclivity to use it, are promoted through institutionalisation and official acknowledgement, it would be critical to sustain and support the distribution of unbiased, science-driven information by governmental institutions and policymakers to encourage informed patient-driven decision-making.Strengths and limitations of this studyThis is the first study to comprehensively explore the usage patterns and sociodemographic associations of TCM in a European population, not based on data deriving from the seventh round of the European Social Survey.We are the first study on CAM usage patterns to graphically explore and report data using a Bayesian Gaussian copula graphical model—thereby, retrieving distinct partial correlations.We provide an up-to-date summary of TCM, set forth our findings at a geopolitical scale and highlight that the discrepancy between the paucity of evidence underpinning most TCM modalities and the international promotion of TCM is also reflected in the widely-held public perception that TCM is supported by science.Due to the retrospective character of our cross-sectional survey, answers are naturally prone to recall and response bias.Our sample is skewed towards the young, people with higher levels of education, and shows a relative underrepresentation of males. Therefore, we post-stratified our sample using representative data from Austria’s federal statistical office “Statistik Austria” as a robustness check.

Researchers are often interested in comparing statistical network models estimated from groups that are defined by the sum-score of the modeled variables. A prominent example is an analysis that compares networks of individuals with and without a diagnosis of a certain disorder. Recently, several authors suggested that this practice may lead to invalid inferences by introducing Berkson's bias. In this article, we show that whether bias is present or not depends on which research question one aims to answer. We review five possible research questions one may have in mind when separately estimating network models in groups that are based on sum-scores. For each research question, we provide an illustration with a simulated bivariate example and discuss the nature of the bias, if present. We show that if one is indeed interested in the network models of the groups defined by the sum-score, no bias is introduced. However, if one is interested in differences across groups defined by a variable other than the sum-score, detecting population heterogeneity, the network model in the general population, or inferring causal relations, then bias will be introduced in most situations. Finally, we discuss for each research question how bias can be avoided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In the wake of the COVID-19 pandemic, physical distancing behavior turned out to be key to mitigating the virus spread. Therefore, it is crucial that we understand how we can successfully alter our behavior and promote physical distancing. We present a framework to systematically assess the effectiveness of behavioral interventions to stimulate physical distancing. In addition, we demonstrate the feasibility of this framework in a large-scale natural experiment (N = 639) conducted during an art fair. In an experimental design, we varied interventions to evaluate the effect of face masks, walking directions, and immediate feedback on visitors’ contacts. We represent visitors as nodes, and their contacts as links in a contact network. Subsequently, we used network modelling to test for differences in these contact networks. We find no evidence that face masks influence physical distancing, while unidirectional walking directions and buzzer feedback do positively impact physical distancing. This study offers a feasible way to optimize physical distancing interventions through scientific research. As such, the presented framework provides society with the means to directly evaluate interventions, so that policy can be based on evidence rather than conjecture.

Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generallydecreasedrather thanincreasedprior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple time scales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of time scale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.

In the absence of a vaccine, social distancing behaviour is pivotal to mitigate COVID-19 virus spread. In this large-scale behavioural experiment, we gathered data during Smart Distance Lab: The Art Fair (n = 839) between August 28 and 30, 2020 in Amsterdam, the Netherlands. We varied walking directions (bidirectional, unidirectional, and no directions) and supplementary interventions (face mask and buzzer to alert visitors of 1.5 metres distance). We captured visitors’ movements using cameras, registered their contacts (defined as within 1.5 metres) using wearable sensors, and assessed their attitudes toward COVID-19 as well as their experience during the event using questionnaires. We also registered environmental measures (e.g., humidity). In this paper, we describe this unprecedented, multi-modal experimental data set on social distancing, including psychological, behavioural, and environmental measures. The data set is available on figshare and in a MySQL database. It can be used to gain insight into (attitudes toward) behavioural interventions promoting social distancing, to calibrate pedestrian models, and to inform new studies on behavioural interventions., Measurement(s) Proximity • Movement • Attitudes and beliefs relating to COVID-19 • Indoor environment measures Technology Type(s) Ultra-wideband technology • Camera Device • questionnaire • environment sensor Factor Type(s) Walking direction • Wearing of face masks • Proximity buzzer Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment public exhibition Sample Characteristic - Location Kingdom of the Netherlands Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14312180

Contains fulltext : 212472.pdf (Publisher’s version ) (Open Access) Network models have become a valuable tool in making sense of a diverse range of social, biological, and information systems. These models marry graph and probability theory to visualize, understand, and interpret variables and their relations as nodes and edges in a graph. Many applications of network models rely on undirected graphs in which the absence of an edge between two nodes encodes conditional independence between the corresponding variables. To gauge the importance of nodes in such a network, various node centrality measures have become widely used, especially in psychology and neuroscience. It is intuitive to interpret nodes with high centrality measures as being important in a causal sense. Using the causal framework based on directed acyclic graphs (DAGs), we show that the relation between causal influence and node centrality measures is not straightforward. In particular, the correlation between causal influence and several node centrality measures is weak, except for eigenvector centrality. Our results provide a cautionary tale: if the underlying real-world system can be modeled as a DAG, but researchers interpret nodes with high centrality as causally important, then this may result in sub-optimal interventions. 13 p.

We propose to use the squared multiple correlation coefficient as an effect size measure for experimental analysis‐of‐variance designs and to use Bayesian methods to estimate its posterior distribution. We provide the expressions for the squared multiple, semipartial, and partial correlation coefficients corresponding to four commonly used analysis‐of‐variance designs and illustrate our contribution with two worked examples.

Empirical scientists cannot do without statistics. This fact is reflected by the pervasiveness of statistics courses in the curricula of essentially all scientific disciplines. Unfortunately, many students exhibit statistics anxiety, that is, ''feelings of anxiety [...] when taking a statistics course or doing statistical analyses'' (Cruise, Cash, & Bolton, 1985, p. 92). In a recent publication, Siew, McCartney, & Vitevitch (2019) aim to shed new light on this highly relevant topic by using data analysis tools from the field of network science. However, just as with any other statistical model, one has to carefully assess the adequacy and robustness of a network model. In this commentary, we point to a number of shortcomings in the article by Siew et al. (2019) with respect to this goal that question their main conclusions. We explain each problem and suggest ways to address it. We hope that these suggestions help to put future investigation of statistics anxiety using network models on a firm methodological basis.

Testing the equality of two proportions is a common procedure in science, especially in medicine and public health. In these domains it is crucial to be able to quantify evidence for the absence of a treatment effect. Bayesian hypothesis testing by means of the Bayes factor provides one avenue to do so, requiring the specification of prior distributions for parameters. The most popular analysis approach views the comparison of proportions from a contingency table perspective, assigning prior distributions directly to the two proportions. Another, less popular approach views the problem from a logistic regression perspective, assigning prior distributions to logit-transformed parameters. Reanalyzing 39 null results from the New England Journal of Medicine with both approaches, we find that they can lead to markedly different conclusions, especially when the observed proportions are at the extremes (i.e., very low or very high). We explain these stark differences and provide recommendations for researchers interested in testing the equality of two proportions and users of Bayes factors more generally. The test that assigns prior distributions to logit-transformed parameters creates prior dependence between the two proportions and yields weaker evidence when the observations are at the extremes. When comparing two proportions, we argue that this test should become the new default., Comment: 16 pages, 7 figures

In the absence of a vaccine, social distancing behaviour is pivotal to mitigate COVID-19 virus spread. In this large-scale behavioural experiment, we gathered data during Smart Distance Lab: The Art Fair (n = 787) between August 28 and 30, 2020 in Amsterdam, the Netherlands. We varied walking directions (bidirectional, unidirectional, and no directions) and supplementary interventions (face mask and buzzer to alert visitors of 1.5 metres distance). We captured visitors' movements using cameras, registered their contacts (defined as within 1.5 metres) using wearable sensors, and assessed their attitudes toward COVID-19 as well as their experience during the event using questionnaires. We also registered environmental measures (e.g., humidity). In this paper, we describe this unprecedented, multi-modal experimental data set on social distancing, including psychological, behavioural, and environmental measures. The data set is available on Figshare and in a MySQL database. It can be used to gain insight into (attitudes toward) behavioural interventions promoting social distancing, to calibrate pedestrian models, and to inform new studies on behavioural interventions.

In the wake of the COVID-19 pandemic, the central importance of human behaviour in mitigating the spread of the virus has become universally recognized. We present a methodology to systematically assess the effectiveness of behavioural interventions to stimulate social distancing. In addition, we demonstrate the feasibility of this framework in a large-scale natural experiment. In an experimental design, we varied behavioural interventions to evaluate the effect of face masks, walking directions, and immediate feedback on visitors’ contacts. We represent visitors as nodes, and their contacts as links in a contact network. Subsequently, we used network modelling to test for differences in these contact networks. We find no evidence that face masks influence social distancing, while unidirectional walking directions and buzzer feedback do positively impact social distancing. The presented methodology represents a practically feasible way to optimize social distancing interventions through scientific research and may directly inform policy.

Time series of individual subjects have become a common data type in psychological research. The Vector Autoregressive (VAR) model, which predicts each variable by all variables including itself at previous time points, has become a popular modeling choice for these data. However, the number of observations in typical psychological applications is often small, which puts the reliability of VAR coefficients into question. In such situations it is possible that the simpler AR model, which only predicts each variable by itself at previous time points, is more appropriate. Bulteel, Mestdagh, Tuerlinckx, and Ceulemans (2018) investigated in which situations the AR or VAR models are more appropriate, suggest a rule to choose between the two models in practice, and ultimately conclude that VAR models are not meaningful for typical psychological applications. We identify shortcomings in their analysis and re-investigate the issue using an extensive simulation study. This allows us to (1) directly investigate the relative performance of AR and VAR models in typical psychological applications, (2) show how the relative performance depends both on n and characteristics of the true model, (3) quantify the uncertainty in selecting between the two models, and (4) assess the relative performance of different model selection strategies. We thereby provide a clear picture for applied researchers about when the VAR model is appropriate in typical psychological applications, and how to select between AR and VAR models in practice.

Many real-world systems can exhibit tipping points and multiple stable states, creating the potential for sudden and difficult to reverse transitions into a less desirable regime. The theory of dynamical systems points to the existence of generic early warning signals that may precede these so-called critical transitions. Recently, psychologists have begun to conceptualize mental disorders such as depression as an alternative stable state, and suggested that early warning signals based on the phenomenon of critical slowing down might be useful for predicting transitions into depression or other psychiatric disorders. Harnessing the potential of early warning signals requires us to understand their limitations as well as the factors influencing their performance in practice. In this paper, we (a) review limitations of early warning signals based on critical slowing down to better understand when they can and cannot occur, and (b) study the conditions under which early warning signals may anticipate critical transitions in online-monitoring settings by simulating from a bistable dynamical system, varying crucial features such as sampling frequency, noise intensity, and speed of approaching the tipping point. We find that, in sharp contrast to their reputation of being generic or model-agnostic, whether early warning signals occur or not strongly depends on the specifics of the system. We also find that they are very sensitive to noise, potentially limiting their utility in practical applications. We discuss the implications of our findings and provide suggestions and recommendations for future research.

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

How confident are researchers in their own claims? Augustus De Morgan suggested that researchers may initially present their conclusions modestly, but afterwards use them as if they were a “moral certainty”. To prevent this from happening, De Morgan proposed that whenever researchers make a claim, they accompany it with a number that reflects their degree of confidence. Current reporting procedures in academia, however, usually present claims without the authors’ assessment of confidence. Here, we report the partial results from an anonymous questionnaire on the concept of evidence.

The imposition of lockdowns in response to the COVID-19 outbreak has underscored the importance of human behavior in mitigating virus transmission. Scientific study ofinterventions designed to change behavior (e.g., to promote social distancing) requiresmeasures of effectiveness that are fast, that can be assessed through experiments, and that can be investigated without actual virus transmission. This paper presents a methodological approach designed to deliver such indicators. We show that behavioral data, obtainable through tracing apps currently in development, can be used to assess a central concept in epidemiology known as the contact network: a network representation that encodes which individuals have been in physical proximity long enough to transmit the virus. Because behavioral interventions alter the contact network, a comparison of contact networks before and after the intervention can provide information on the effectiveness of the intervention. We coin indicators based on this idea Behavioral Contact Network (BECON) indicators. We examine the performance of three indicators: the Density BECON (based on differences in network density), the Spectral BECON (based on differences in the eigenvector of the adjacency matrix), and the ASPL BECON (based on differences in average shortest path lengths). Using simulations, we show that all three indicators can effectively track the effect of behavioral interventions. Even in conditions with significant amounts of noise, BECON indicators can reliably identify and order effect sizes of interventions. The present paper invites further study of the method as well as practical implementations to test the validity of BECON indicators in real data.

Causal inference goes beyond prediction by modeling the outcome of interventions and formalizing counterfactual reasoning. Instead of restricting causal conclusions to experiments, causal inference explicates the conditions under which it is possible to draw causal conclusions even from observational data. In this paper, I provide a concise introduction to the graphical approach to causal inference, which uses Directed Acyclic Graphs (DAGs) to visualize, and Structural Causal Models (SCMs) to relate probabilistic and causal relationships. Successively, we climb what Judea Pearl calls the "causal hierarchy" --- moving from association to intervention to counterfactuals. I explain how DAGs can help us reason about associations between variables as well as interventions; how the do-calculus leads to a satisfactory definition of confounding, thereby clarifying, among other things, Simpson's paradox; and how SCMs enable us to reason about what could have been. Lastly, I discuss a number of challenges in applying causal inference in practice.

Contains fulltext : 226716.pdf (Publisher’s version ) (Closed access) Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using frequentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to the p-value. However, the broad adoption of Bayesian statistics - and Bayesian ANOVA in particular - is frustrated by the fact that Bayesian concepts are rarely taught in applied statistics courses. Consequently, practitioners may be unsure how to conduct a Bayesian ANOVA and interpret the results. Here we provide a guide for executing and interpreting a Bayesian ANOVA with JASP, an open-source statistical software program with a graphical user interface. We explain the key concepts of the Bayesian ANOVA using two empirical examples. 24 p.

Educational Psychology Review, 31 (1), ISSN:1040-726X, ISSN:1573-336X

The Harry Potter series describes the adventures of a boy and his peers in a fictional world at the “Hogwarts School of Witchcraft and Wizardry”. In the series, pupils get appointed to one of four groups (Houses) at the beginning of their education based on their personality traits. The author of the books has constructed an online questionnaire that allows fans to find out their House affiliation. Crysel, Cook, Schember, and Webster (2015) argued that being sorted into a particular Hogwarts House through the Sorting Hat Quiz is related to empirically established personality traits. We replicated their study while improving on sample size, methods, and analysis. Although our results are similar, effect sizes are small overall, which attenuates the claims by Crysel et al. The effect vanishes when restricting the analysis to participants who desired, but were not sorted into a particular House. On a theoretical level, we extend previous research by also analysing the relation of the Hogwarts Houses to Schwartz’s Basic Human Values but find only moderate or no relations.

The pre-registration of research via registered reports is a recent development in the field of psychology. The aim of pre-registration is to encourage research that presents sound hypotheses and methodology (Chambers, 2014) in order to counter undesirable but prevalent research practices such as cherry-picking and p-hacking. In this Letter from the Editors, we wish to echo calls for registered reports and outline how we, the Editors at the Journal of European Psychology Students (JEPS), plan to introduce registered reports for student research. We address the issues necessitating the introduction of registered reports and outline the approach needed for implementing this initiative in a student journal.

Lindley (1997) argued that Bayes factors possess an unsatisfactory, even paradoxical property. In this short comment, we illustrate and clarify Lindley’s point with an example. We conclude that said property is not paradoxical, but intuitive.

Psychonomic Bulletin & Review, 25 (1), ISSN:1531-5320, ISSN:1069-9384

Social media is radically altering the human social landscape. Before the internet era, human interaction consisted chiefly of direct and reciprocal contact, yet with the rise of social media, the passive consumption of other users’ information is becoming an increasingly popular pastime. Passive consumption occurs when a user reads the posts of another user without interacting with them in any way. Previous studies suggest that people feel more connected to an artificial person after passively consuming their Facebook posts. This finding could help explain how relationships develop during passive consumption and what motivates this kind of social media use. This protocol proposes two studies that would make both a methodological and a theoretical contribution to the field of social media research. Both studies investigate the influence of passive consumption on changes in interpersonal attraction. The first study tests whether screenshots, which are widely used in present research, can be used as a proxy for real Facebook use. It measures the changes in interpersonal attraction after passive consumption of either a screenshot, an artificial in situ profile, or an acquaintance’s real Facebook profile. The second study relies on traditional theories of relationship formation and motivation to investigate which variables (perceived intimacy, perceived frequency of posts, perceived variety of post topics, attributional confidence, and homophily) moderate the link between interpersonal attraction before and after passive consumption. The results of the first study provide insights into the generalizability of the effect by using different stimuli, while also providing a valuable investigation into a commonly used method in the research field. The results of the second study supplement researchers’ understanding of the pathways linking passive use and interpersonal attraction, giving the field further insight into whether theories about offline relationship formation can be used in an online context. Taken together, this protocol aims to shed light on the intricate relation between passive consumption and interpersonal attraction, and variables moderating this effect.