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This article outlines a novel Bayesian approach to the testing and estimation of Pearson partial correlations. By generalizing a Bayesian inference procedure for Pearson's correlation coefficient we obtain analytic expressions for the Bayes factor and for the (marginal) posterior distribution of a partial correlation coefficient. Full Bayesian inference can be achieved using only the sample size, the number of controlling variables and the relevant summary statistics, that is, the sample partial correlation. The present approach is illustrated with two empirical examples.

Running developmental experiments, particularly with infants, isoften time consuming and intensive, and recruitment of participantsis hard and expensive. Thus, an important goal for developmentalresearchers is to optimize sampling plans such that neither toomany nor too few participants are tested given the hypothesis ofinterest. One approach that enables such optimization is the useof Bayesian sequential designs. The use of such sequential designsallows data collection to be terminated as soon as the evidence isdeemed su ciently strong, without compromising the interpretabilityof the test outcome. In this tutorial, we illustrate how to plana Bayesian sequential testing design prior to data collection by themethod of Bayes factor design analysis - the Bayesian equivalent ofpower analysis - and discuss the relevance of this for developmentalpsychologists. The tutorial provides a step-by-step guide to performsuch analyses, and the methods are illustrated using commonly usedstatistics in a typical infant looking time paradigm such that researcherscan easily adapt these methods for their studies.

Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh et al., Cognitive Science 35(2):211–250, 2010). Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, an integration of hidden Markov models with evidence accumulation models has still remained elusive, even though such models would allow researchers to capture potential dependencies between response times and accuracy within the states, while concomitantly capturing different behavioral modes during cognitive processing. This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model’s implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh et al. (Cognitive Science 35(2):211–250, 2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.

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.

In large, complex societies, assorting with others with similar social norms or behaviors can facilitate successful coordination and cooperation. The ability to recognize others with shared norms or behaviors is thus assumed to be under selection. As a medium of communication, human art might reflect fitness-relevant information on shared norms and behaviors of other individuals thus facilitating successful coordination and cooperation. Distinctive styles or patterns of artistic design could signify migration history, different groups with a shared interaction history due to spatial proximity, as well as individual-level expertise and preferences. In addition, cultural boundaries may be even more pronounced in a highly diverse and socially stratified society. In the current study, we focus on a large corpus of an artistic tradition called kolam that is produced by women from Tamil Nadu in South India (N = 3, 139 kolam drawings from 192 women) to test whether stylistic variations in art can be mapped onto caste boundaries, migration and neighborhoods. Since the kolam art system with its sequential drawing decisions can be described by a Markov process, we characterize variation in styles of art due to different facets of an artist's identity and the group affiliations, via hierarchical Bayesian statistical models. Our results reveal that stylistic variations in kolam art only weakly map onto caste boundaries, neighborhoods, and regional origin. In fact, stylistic variations or patterns in art are dominated by artist-level variation and artist expertise. Our results illustrate that although art can be a medium of communication, it is not necessarily marked by group affiliation. Rather, artistic behavior in this context seems to be primarily a behavioral domain within which individuals carve out a unique niche for themselves to differentiate themselves from others. Our findings inform discussions on the evolutionary role of art for group coordination by encouraging researchers to use systematic methods to measure the mapping between specific objects or styles onto groups.

Describing, analyzing, and explaining patterns in eye movement behavior is crucial for understanding visual perception. Further, eye movements are increasingly used in informing cognitive process models. In this article, we start by reviewing basic characteristics and desiderata for models of eye movements. Specifically, we argue that there is a need for models combining spatial and temporal aspects of eye-tracking data (i.e., fixation durations and fixation locations), that formal models derived from concrete theoretical assumptions are needed to inform our empirical research, and custom statistical models are useful for detecting specific empirical phenomena that are to be explained by said theory. In this article, we develop a conceptual model of eye movements, or specifically, fixation durations and fixation locations, and from it derive a formal statistical model-meeting our goal of crafting a model useful in both the theoretical and empirical research cycle. We demonstrate the use of the model on an example of infant natural scene viewing, to show that the model is able to explain different features of the eye movement data, and to showcase how to identify that the model needs to be adapted if it does not agree with the data. We conclude with discussion of potential future avenues for formal eye movement models. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

Towards discovering more effective means to promote code sharing as an open science practice, this study explores the current state of code sharing within the field of psychological methods and statistics and examines the association between this practice and conventional and alternative research metrics. Towards this purpose, a total of 815 articles from three major journals within the field of psychological methods and statistics were manually screened and encoded based on code sharing practices and general article characteristics. In addition, data on conventional (citation counts) and alternative research metrics (Altmetric Attention Score) was retrieved through online scientific databases (Web of Science and Altmetric.com). This input was then analysed using descriptive statistics and regression models suitable for count data, and robustness of the findings were assessed using multiverse analysis. The findings of this study suggest that the sharing of scientific computer code is not (yet) extensively practiced within the field of psychological methods and statistics. In the majority of academic articles included in this study, scientific computer code was not shared (66 %). Moreover, if such code was shared, it was frequently found to be improperly annotated (70 %) and/or incomplete (52 %). Nevertheless, the findings of this study also suggest a hopeful prospect, as the sharing of scientific computer code has increased between 2010 and 2017. The study revealed a robust positive connection with alternative research metrics. This study did not find robust positive connection between code sharing and citation counts.

Contains fulltext : 214941.pdf (Publisher’s version ) (Open Access) We present a consensus-based checklist to improve and document the transparency of research reports in social and behavioural research. An accompanying online application allows users to complete the form and generate a report that they can submit with their manuscript or post to a public repository. 3 p.

Author(s): Aczel, Balazs; Szaszi, Barnabas; Sarafoglou, Alexandra; Kekecs, Zoltan; Kucharský, Simon; Benjamin, Daniel; Chambers, Christopher D; Fisher, Agneta; Gelman, Andrew; Gernsbacher, Morton A; Ioannidis, John P; Johnson, Eric; Jonas, Kai; Kousta, Stavroula; Lilienfeld, Scott O; Lindsay, D Stephen; Morey, Candice C; Munafo, Marcus; Newell, Benjamin R; Pashler, Harold; Shanks, David R; Simons, Daniel J; Wicherts, Jelte M; Albarracin, Dolores; Anderson, Nicole D; Antonakis, John; Arkes, Hal R; Back, Mitja D; Banks, George C; Beevers, Christopher; Bennett, Andrew A; Bleidorn, Wiebke; Boyer, Ty W; Cacciari, Cristina; Carter, Alice S; Cesario, Joseph; Clifton, Charles; Conroy, Ronan M; Cortese, Mike; Cosci, Fiammetta; Cowan, Nelson; Crawford, Jarret; Crone, Eveline A; Curtin, John; Engle, Randall; Farrell, Simon; Fearon, Pasco; Fichman, Mark; Frankenhuis, Willem; Freund, Alexandra M; Gaskell, M Gareth; Giner-Sorolla, Roger; Green, Don P; Greene, Robert L; Harlow, Lisa L; de la Guardia, Fernando Hoces; Isaacowitz, Derek; Kolodner, Janet; Lieberman, Debra; Logan, Gordon D; Mendes, Wendy B; Moersdorf, Lea; Nyhan, Brendan; Pollack, Jeffrey; Sullivan, Christopher; Vazire, Simine; Wagenmakers, Eric-Jan | Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

We outline a network method to synthesize a literature overview from search results obtained by multiple team members. Several network statistics are used to create a single representativeness ranking. We illustrate the method with the dispersed literature on a common misinterpretation of analysis of covariance (ANCOVA). The network method yields a top ten list of the most relevant articles that students and researchers can take as a point of departure for a more detailed study on this topic. The proposed methodology is implemented in Shiny, an open-source R package.