Heterogeneous heterogeneity by default: Testing categorical moderators in mixed-effects meta-analysis.

Categorical moderators are often included in mixed-effects meta-analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between-study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should instead default to assuming unequal between-study variances when analysing categorical moderators. To achieve this, we suggest using a mixed-effects location-scale model (MELSM) to allow group-specific estimates for the between-study variance. In two extensive simulation studies, we show that in terms of Type I error and statistical power, little is lost by using the MELSM for moderator tests, but there can be serious costs when an equal variance mixed-effects model (MEM) is used. Most notably, in scenarios with balanced sample sizes or equal between-study variance, the Type I error and power rates are nearly identical between the MEM and the MELSM. On the other hand, with imbalanced sample sizes and unequal variances, the Type I error rate under the MEM can be grossly inflated or overly conservative, whereas the MELSM does comparatively well in controlling the Type I error across the majority of cases. A notable exception where the MELSM did not clearly outperform the MEM was in the case of few studies (e.g., 5). With respect to power, the MELSM had similar or higher power than the MEM in conditions where the latter produced non-inflated Type 1 error rates. Together, our results support the idea that assuming unequal between-study variances is preferred as a default strategy when testing categorical moderators.

Bayesian Uncertainty Estimation for Gaussian Graphical Models and Centrality Indices.

In the network approach to psychopathology, psychological constructs are conceptualized as networks of interacting components (e.g., the symptoms of a disorder). In this network view, interest is on the degree to which symptoms influence each other, both directly and indirectly. These direct and indirect influences are often captured with centrality indices, however, the estimation method often used with these networks, the frequentist graphical LASSO (GLASSO), has difficulty estimating (uncertainty in) these measures. Bayesian estimation might provide a solution, as it is better suited to deal with bias in the sampling distribution of centrality indices. This study therefore compares estimation of symptom networks with Bayesian GLASSO- and Horseshoe priors to estimation using the frequentist GLASSO using extensive simulations. Results showed that the Bayesian GLASSO performed better than the Horseshoe, and that the Bayesian GLASSO outperformed the frequentist GLASSO with respect to bias in edge weights, centrality measures, correlation between estimated and true partial correlations, and specificity. Sensitivity was better for the frequentist GLASSO, but performance of the Bayesian GLASSO is usually close. With respect to uncertainty in the centrality measures, the Bayesian GLASSO shows good coverage for strength and closeness centrality, but uncertainty in betweenness centrality is estimated less well.

A review of applications of the Bayes factor in psychological research.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A network perspective on abnormal child behavior in primary school students.

Abnormal child behavior is a complex entity including among others disorders such as ADHD, a neuropsychiatric disorder interfering with the normal development of the child, and combining many comorbidities. There are no specific tools designed to assess abnormal child behavior, although well-known ADHD screening tools such as the CTRS-R:S go beyond the disorder itself and cover the wider construct. This can be understood as the complex system of mutually influencing entities, that is why the aim of this study was to investigate the items of the CTRS-R:S as a psychological network. We analyzed a data set of 525 children in French-speaking primary schools from Belgium, and estimated a Bayesian Gaussian Graphical Model with a Bayes Factor of 30. We identified highly connected nodes from each community of the CTRS-R:S to build an additional network of communities. In the domain network, Oppositional and Cognitive skills share the strongest connection in the network. The 28-item network was overall positively connected albeit the presence of some negative connections. The spinglass algorithm identified eight domains in the CTRS-R:S network that causally influence each other. The network approach is a useful framework to investigate the domains of abnormal child behavior as well as the interplay between individual symptoms.

Who is and is not "average"? Random effects selection with spike-and-slab priors.

Mixed-effects models are often employed to study individual differences in psychological science. Such analyses commonly entail testing whether between-subjects variability exists and including covariates to explain that variability. We argue that researchers have much to gain by explicitly focusing on the individual in individual differences research. To this end, we propose the spike-and-slab prior distribution for random effect selection in (generalized) mixed-effects models as a means to gain a more nuanced perspective of individual differences. The prior for each random effect is a two-component mixture consisting of a point-mass "spike" centered at zero and a diffuse "slab" capturing nonzero values. Effectively, such an approach allows researchers to answer questions about particular individuals; specifically, "Who is average?", in the sense of deviating from an average effect, such as the population-averaged slope. We begin with an illustrative example, where the spike-and-slab formulation is used to select random intercepts in logistic regression. This demonstrates the utility of the proposed methodology in a simple setting while also highlighting its flexibility in fitting different kinds of models. We then extend the approach to random slopes that capture experimental effects. In two cognitive tasks, we show that despite there being little variability in the slopes, there were many individual differences in performance. In two simulation studies, we assess the ability of the proposed method to correctly identify (non)average individuals without compromising the mixed-effects estimates. We conclude with future directions for the presented methodology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Why overfitting is not (usually) a problem in partial correlation networks.

Network psychometrics is undergoing a time of methodological reflection. In part, this was spurred by the revelation that ℓ1-regularization does not reduce spurious associations in partial correlation networks. In this work, we address another motivation for the widespread use of regularized estimation: the thought that it is needed to mitigate overfitting. We first clarify important aspects of overfitting and the bias-variance tradeoff that are especially relevant for the network literature, where the number of nodes or items in a psychometric scale are not large compared to the number of observations (i.e., a low p/n ratio). This revealed that bias and especially variance are most problematic in p/n ratios rarely encountered. We then introduce a nonregularized method, based on classical hypothesis testing, that fulfills two desiderata: (a) reducing or controlling the false positives rate and (b) quelling concerns of overfitting by providing accurate predictions. These were the primary motivations for initially adopting the graphical lasso (glasso). In several simulation studies, our nonregularized method provided more than competitive predictive performance, and, in many cases, outperformed glasso. It appears to be nonregularized, as opposed to regularized estimation, that best satisfies these desiderata. We then provide insights into using our methodology. Here we discuss the multiple comparisons problem in relation to prediction: stringent alpha levels, resulting in a sparse network, can deteriorate predictive accuracy. We end by emphasizing key advantages of our approach that make it ideal for both inference and prediction in network analysis. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Mortality Rates Among Hospitalized Patients With COVID-19 Infection Treated With Tocilizumab and Corticosteroids: A Bayesian Reanalysis of a Previous Meta-analysis.

Importance: A World Health Organization (WHO) meta-analysis found that tocilizumab was associated with reduced mortality in hospitalized patients with COVID-19. However, uncertainty remains concerning the magnitude of tocilizumab's benefits and whether its association with mortality benefit is similar across respiratory subgroups. Objective: To use bayesian methods to assess the magnitude of mortality benefit associated with tocilizumab and the differences between respiratory support subgroups in hospitalized patients with COVID-19. Design, Setting, and Participants: A bayesian hierarchical reanalysis of the WHO meta-analysis of tocilizumab studies published in 2020 and 2021 was performed. Main results were estimated using weakly informative priors to exert little influence on the observed data. The robustness of these results was evaluated using vague and informative priors. The studies featured in the meta-analysis were randomized clinical tocilizumab trials of hospitalized patients with COVID-19. Only patients receiving corticosteroids were included. Interventions: Usual care plus tocilizumab in comparison with usual care or placebo. Main Outcomes and Measures: All-cause mortality at 28 days after randomization. Results: Among the 5339 patients included in this analysis, most were men, with mean ages between 56 and 66 years. There were 2117 patients receiving simple oxygen only, 2505 receiving noninvasive ventilation (NIV), and 717 receiving invasive mechanical ventilation (IMV) in 15 studies from multiple countries and continents. Assuming weakly informative priors, the overall odds ratios (ORs) for survival were 0.70 (95% credible interval [CrI], 0.50-0.91) for patients receiving simple oxygen only, 0.81 (95% CrI, 0.63-1.03) for patients receiving NIV, and 0.89 (95% CrI, 0.61-1.22) for patients receiving IMV, respectively. The posterior probabilities of any benefit (OR <1) were notably different between patients receiving simple oxygen only (98.9%), NIV (95.5%), and IMV (75.4%). The posterior probabilities of a clinically meaningful association (absolute mortality risk difference >1%) were greater than 95% in patients receiving simple oxygen only and greater than 90% in patients receiving NIV. In contrast, the posterior probability of this clinically meaningful association was only approximately 67% in patients receiving IMV. The probabilities of tocilizumab superiority in the simple oxygen only subgroup compared with the NIV and IMV subgroups were 85% and 90%, respectively. Predictive intervals highlighted that only 72.1% of future tocilizumab IMV studies would show benefit. The conclusions did not change with different prior distributions. Conclusions and Relevance: In this bayesian reanalysis of a previous meta-analysis of 15 studies of hospitalized patients with COVID-19 treated with tocilizumab and corticosteroids, use of simple oxygen only and NIV was associated with a probability of a clinically meaningful mortality benefit from tocilizumab. Future research should clarify whether patients receiving IMV also benefit from tocilizumab.

Learning to live with sampling variability: Expected replicability in partial correlation networks.

The topic of replicability has recently captivated the emerging field of network psychometrics. Although methodological practice (e.g., p-hacking) has been identified as a root cause of unreliable research findings in psychological science, the statistical model itself has come under attack in the partial correlation network literature. In a motivating example, I first describe how sampling variability inherent to partial correlations can merely give the appearance of unreliability. For example, when going from zero-order to partial correlations there is necessarily more sampling variability that translates into reduced statistical power. I then introduce novel methodology for deriving expected network replicability (ENR), wherein replication is modeled with the Poisson-binomial distribution. This analytic solution can be used with the Pearson, Spearman, Kendall, and polychoric partial correlation coefficient. I first employed the method to estimate ENR for a variety of data sets from the network literature. Here it was determined that partial correlation networks do not have inherent limitations, given current estimates of replicability were consistent with ENR. I then highlighted sources that can reduce replicability, that is, when going from continuous to ordinal data with few categories and employing a multiple comparisons correction. To address these challenges, I described a strategy for using the proposed method to plan for network replication. I end with recommendations that include the importance of the network literature repositioning itself with gold-standard approaches for assessing replication, including explicit consideration of Type I and Type II error rates. The method for computing ENR is implemented in the R package GGMnonreg. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Putting the individual into reliability: Bayesian testing of homogeneous within-person variance in hierarchical models.

Measurement reliability is a fundamental concept in psychology. It is traditionally considered a stable property of a questionnaire, measurement device, or experimental task. Although intraclass correlation coefficients (ICC) are often used to assess reliability in repeated measure designs, their descriptive nature depends upon the assumption of a common within-person variance. This work focuses on the presumption that each individual is adequately described by the average within-person variance in hierarchical models. And thus whether reliability generalizes to the individual level, which leads directly into the notion of individually varying ICCs. In particular, we introduce a novel approach, using the Bayes factor, wherein a researcher can directly test for homogeneous within-person variance in hierarchical models. Additionally, we introduce a membership model that allows for classifying which (and how many) individuals belong to the common variance model. The utility of our methodology is demonstrated on cognitive inhibition tasks. We find that heterogeneous within-person variance is a defining feature of these tasks, and in one case, the ratio between the largest to smallest within-person variance exceeded 20. This translates into a tenfold difference in person-specific reliability! We also find that few individuals belong to the common variance model, and thus traditional reliability indices are potentially masking important individual variation. We discuss the implications of our findings and possible future directions. The methods are implemented in the R package vICC.

Bayesian Network Structure and Predictability of Autistic Traits.

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

A new frontier for studying within-person variability: Bayesian multivariate generalized autoregressive conditional heteroskedasticity models.

Research on individual variation has received increased attention. The bulk of the models discussed in psychological research so far, focus mainly on the temporal development of the mean structure. We expand the view on within-person residual variability and present a new model parameterization derived from classic multivariate GARCH models used to predict and forecast volatility in financial time-series. We propose a new pdBEKK and a modified dynamic conditional correlation (DCC) model that accommodate external time-varying predictors for the within-person variance. The main goal of this work is to evaluate the potential usefulness of MGARCH models for research in within-person variability. MGARCH models partition the within-person variance into, at least, 3 components: An overall constant and unconditional baseline variance, a process that introduces variance conditional on previous innovations, or random shocks, and a process that governs the carry-over effects of previous conditional variance, similar to an AR model. These models allow for variance spillover effects from one time-series to another. We illustrate the pdBEKK- and the DCC-MGARCH on two individuals who have rated their daily positive and negative affect over 100 consecutive days. The full models comprised a multivariate ARMA(1,1) model for the means and included physical activity as moderator of the overall baseline variance. Overall, the pdBEKK seems to result in a more straightforward psychological interpretation, but the DCC is generally easier to estimate and can accommodate more simultaneous time-series. Both models require rather large amounts of datapoints to detect nonzero parameters. We provide an R-package bmgarch that facilitates the estimation of these types of models. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Bayesian Estimation for Gaussian Graphical Models: Structure Learning, Predictability, and Network Comparisons.

Gaussian graphical models (GGM; "networks") allow for estimating conditional dependence structures that are encoded by partial correlations. This is accomplished by identifying non-zero relations in the inverse of the covariance matrix. In psychology the default estimation method uses ℓ1-regularization, where the accompanying inferences are restricted to frequentist objectives. Bayesian methods remain relatively uncommon in practice and methodological literatures. To date, they have not yet been used for estimation and inference in the psychological network literature. In this work, I introduce Bayesian methodology that is specifically designed for the most common psychological applications. The graphical structure is determined with posterior probabilities that can be used to assess conditional dependent and independent relations. Additional methods are provided for extending inference to specific aspects within- and between-networks, including partial correlation differences and Bayesian methodology to quantify network predictability. I first demonstrate that the decision rule based on posterior probabilities can be calibrated to the desired level of specificity. The proposed techniques are then demonstrated in several illustrative examples. The methods have been implemented in the R package BGGM.

Sampling Variability Is Not Nonreplication: A Bayesian Reanalysis of Forbes, Wright, Markon, and Krueger.

Forbes, Wright, Markon, and Krueger claim that psychopathology network characteristics have "limited" or "poor" replicability, supporting their argument primarily with data from two waves of an observational study on depression and anxiety. They developed "direct metrics" to gauge change across networks (e.g., change in edge sign), and used these results to support their conclusion. Three key flaws undermine their critique. First, nonreplication across empirical datasets does not provide evidence against a method; such evaluations of methods are possible only in controlled simulations when the data-generating model is known. Second, they assert that the removal of shared variance necessarily decreases reliability. This is not true. Depending on the causal model, it can either increase or decrease reliability. Third, their direct metrics do not account for normal sampling variability, leaving open the possibility that the direct differences between samples are due to normal, unproblematic fluctuations. As an alternative to their direct metrics, we provide a Bayesian re-analysis that quantifies uncertainty and compares relative evidence for replication (i.e., equivalence) versus nonreplication (i.e., nonequivalence) for each network edge. This approach provides a principled roadmap for future assessments of network replicability. Our analysis indicated substantial evidence for replication and scant evidence for nonreplication.

Beneath the surface: Unearthing within-person variability and mean relations with Bayesian mixed models.

Mixed-effects models are becoming common in psychological science. Although they have many desirable features, there is still untapped potential. It is customary to view homogeneous variance as an assumption to satisfy. We argue to move beyond that perspective, and to view modeling within-person variance as an opportunity to gain a richer understanding of psychological processes. The technique to do so is based on the mixed-effects location scale model that can simultaneously estimate mixed-effects submodels to both the mean (location) and within-person variance (scale). We develop a framework that goes beyond assessing the submodels in isolation of one another and introduce a novel Bayesian hypothesis test for mean-variance correlations in the distribution of random effects. We first present a motivating example, which makes clear how the model can characterize mean-variance relations. We then apply the method to reaction times (RTs) gathered from 2 cognitive inhibition tasks. We find there are more individual differences in the within-person variance than the mean structure, as well as a complex web of structural mean-variance relations. This stands in contrast to the dominant view of within-person variance (i.e., "noise"). The results also point toward paradoxical within-person, as opposed to between-person, effects: several people had slower and less variable incongruent responses. This contradicts the typical pattern, wherein larger means tend to be associated with more variability. We conclude with future directions, spanning from methodological to theoretical inquires, that can be answered with the presented methodology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection.

Gaussian graphical models are commonly used to characterize conditional (in)dependence structures (i.e., partial correlation networks) of psychological constructs. Recently attention has shifted from estimating single networks to those from various subpopulations. The focus is primarily to detect differences or demonstrate replicability. We introduce two novel Bayesian methods for comparing networks that explicitly address these aims. The first is based on the posterior predictive distribution, with a symmetric version of Kullback-Leibler divergence as the discrepancy measure, that tests differences between two (or more) multivariate normal distributions. The second approach makes use of Bayesian model comparison, with the Bayes factor, and allows for gaining evidence for invariant network structures. This overcomes limitations of current approaches in the literature that use classical hypothesis testing, where it is only possible to determine whether groups are significantly different from each other. With simulation we show the posterior predictive method is approximately calibrated under the null hypothesis (α = .05) and has more power to detect differences than alternative approaches. We then examine the necessary sample sizes for detecting invariant network structures with Bayesian hypothesis testing, in addition to how this is influenced by the choice of prior distribution. The methods are applied to posttraumatic stress disorder symptoms that were measured in 4 groups. We end by summarizing our major contribution, that is proposing 2 novel methods for comparing Gaussian graphical models (GGMs), which extends beyond the social-behavioral sciences. The methods have been implemented in the R package BGGM. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations Among Affective Traits, States, and Physical Activity.

Intensive longitudinal studies and experience sampling methods are becoming more common in psychology. While they provide a unique opportunity to ask novel questions about within-person processes relating to personality, there is a lack of methods specifically built to characterize the interplay between traits and states. We thus introduce a Bayesian multivariate mixed-effects location scale model (M-MELSM). The formulation can simultaneously model both personality traits (the location) and states (the scale) for multivariate data common to personality research. Variables can be included to predict either (or both) the traits and states, in addition to estimating random effects therein. This provides correlations between location and scale random effects, both across and within each outcome, which allows for characterizing relations between any number of personality traits and the corresponding states. We take a fully Bayesian approach, not only to make estimation possible, but also because it provides the necessary information for use in psychological applications such as hypothesis testing. To illustrate the model we use data from 194 individuals that provided daily ratings of negative and positive affect, as well as their physical activity in the form of step counts over 100 consecutive days. We describe the fitted model, where we emphasize, with visualization, the richness of information provided by the M-MELSM. We demonstrate Bayesian hypothesis testing for the correlations between the random effects. We conclude by discussing limitations of the MELSM in general and extensions to the M-MELSM specifically for personality research.

Back to the basics: Rethinking partial correlation network methodology.

The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to characterize relationships among observed variables. These relationships are represented as elements in the precision matrix. Standardizing the precision matrix and reversing the sign yields corresponding partial correlations that imply pairwise dependencies in which the effects of all other variables have been controlled for. The graphical lasso (glasso) has emerged as the default estimation method, which uses ℓ1 -based regularization. The glasso was developed and optimized for high-dimensional settings where the number of variables (p) exceeds the number of observations (n), which is uncommon in psychological applications. Here we propose to go 'back to the basics', wherein the precision matrix is first estimated with non-regularized maximum likelihood and then Fisher Z transformed confidence intervals are used to determine non-zero relationships. We first show the exact correspondence between the confidence level and specificity, which is due to 1 minus specificity denoting the false positive rate (i.e., α). With simulations in low-dimensional settings (p â‰ª n), we then demonstrate superior performance compared to the glasso for detecting the non-zero effects. Further, our results indicate that the glasso is inconsistent for the purpose of model selection and does not control the false discovery rate, whereas the proposed method converges on the true model and directly controls error rates. We end by discussing implications for estimating GGMs in psychology.

A Bayesian nonlinear mixed-effects location scale model for learning.

We present a Bayesian nonlinear mixed-effects location scale model (NL-MELSM). The NL-MELSM allows for fitting nonlinear functions to the location, or individual means, and the scale, or within-person variance. Specifically, in the context of learning, this model allows the within-person variance to follow a nonlinear trajectory, where it can be determined whether variability reduces during learning. It incorporates a sub-model that can predict nonlinear parameters for both the location and scale. This specification estimates random effects for all nonlinear location and scale parameters that are drawn from a common multivariate distribution. This allows estimation of covariances among the random effects, within and across the location and the scale. These covariances offer new insights into the interplay between individual mean structures and intra-individual variability in nonlinear parameters. We take a fully Bayesian approach, not only for ease of estimation but also for inference because it provides the necessary and consistent information for use in psychological applications, such as model selection and hypothesis testing. To illustrate the model, we use data from 333 individuals, consisting of three age groups, who participated in five learning trials that assessed verbal memory. In an exploratory context, we demonstrate that fitting a nonlinear function to the within-person variance, and allowing for individual variation therein, improves predictive accuracy compared to customary modeling techniques (e.g., assuming constant variance). We conclude by discussing the usefulness, limitations, and future directions of the NL-MELSM.

On Nonregularized Estimation of Psychological Networks.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg.

Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP.

BACKGROUND: Despite its popularity as an inferential framework, classical null hypothesis significance testing (NHST) has several restrictions. Bayesian analysis can be used to complement NHST, however, this approach has been underutilized largely due to a dearth of accessible software options. JASP is a recently developed open-source statistical package that facilitates both Bayesian and NHST analysis using a graphical interface. This article provides an applied introduction to Bayesian inference with Bayes factors using JASP. METHODS: We use JASP to compare and contrast Bayesian alternatives for several common classical null hypothesis significance tests: correlations, frequency distributions, t-tests, ANCOVAs, and ANOVAs. These examples are also used to illustrate the strengths and limitations of both NHST and Bayesian hypothesis testing. RESULTS: A comparison of NHST and Bayesian inferential frameworks demonstrates that Bayes factors can complement p-values by providing additional information for hypothesis testing. Namely, Bayes factors can quantify relative evidence for both alternative and null hypotheses. Moreover, the magnitude of this evidence can be presented as an easy-to-interpret odds ratio. CONCLUSIONS: While Bayesian analysis is by no means a new method, this type of statistical inference has been largely inaccessible for most psychiatry researchers. JASP provides a straightforward means of performing reproducible Bayesian hypothesis tests using a graphical "point and click" environment that will be familiar to researchers conversant with other graphical statistical packages, such as SPSS.