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.
Het op een correcte manier opzetten, uitvoeren en documenteren van een statistische steekproef is geen eenvoudige taak. In dit artikel presenteren we JASP for Audit; open-source en ...gebruiksvriendelijke software die specifiek is ontwikkeld voor zowel interne als externe auditors om de statistische onderdelen van een controle gemakkelijker te maken. Allereerst wordt een overzicht van de functionaliteit van JASP for Audit gegeven. Vervolgens wordt er ingegaan op vier onderscheidende kenmerken van de software en de voordelen daarvan voor de controlepraktijk. Daarna wordt de software gedemonstreerd middels drie voorbeelden over respectievelijk controls testing, gegevensgerichte controle en fiscale controle. Afsluitend worden er aanbevelingen gegeven voor het gebruik van JASP for Audit in de praktijk.
Bayesiaanse statistiek is een manier om efficiëntie en transparantie bij gestratificeerde steekproeven te vergroten. De reden hiervoor is dat Bayesiaanse statistiek de auditor dwingt om expliciet te ...maken welke informatie en kennis gebruikt wordt bij de evaluatie van de steekproef. In dit artikel worden twee belangrijke vormen van voorkennis beschreven die de auditor moet valideren. Vervolgens zijn deze vormen van voorkennis vertaald naar statistische modellen, die worden gebruikt bij het doen van een gestratificeerde steekproef. Afsluitend wordt gedemonstreerd hoe sommige van die modellen kunnen leiden tot nauwkeurigere foutschattingen en een transparantere audit.
•Digital technologies will further impact the finance function and controllers.•Senior controllers feel that their current digital competency levels are too low.•Advancing task-specific digital ...competencies is required to stay relevant.•At any current competency level, knowledge drives the competency growth they expect to be required from them.•Digital competency growth can be self-started by deepening knowledge first.
Prior research foresees that advancing digital technologies call for increasing competency levels of controllers. Competency theory predicts that achieving this will require increasing knowledge of these technologies and the ability to task-specifically use it. Empirical evidence of the recognition of these necessary conditions is missing. Drawing on competency literature and extant research on influences of nine technologies, we survey 453 senior controllers. We find for all technologies that they perceive their current knowledge and competency levels lower than required and that their expectations of the required competency growth correlate positively with perceived current knowledge at any current competency level, even for task-specific technologies that have the highest current and future competency scores (big data, analytics, visualization). However, their expectations may underestimate the future digital competency levels required for staying relevant. Our evidence urges controllers to work on their digital competencies and put task-specific knowledge first for each new competency.
Bayesian hypothesis testing presents an attractive alternative to
p
value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability ...to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the
t
-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (
http://www.jasp-stats.org
), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.
SUMMARY Statistical methods play an important role in auditors’ analyses of their clients’ data. A key component of the statistical approach to auditing is assessing the strength of evidence for or ...against a hypothesis. We argue that the frequentist statistical methods often used by auditors cannot provide the statistical evidence that audit standards advocate. In this article, we discuss an alternative approach that can provide this evidence: Bayesian inference. First, we explore the philosophical differences between frequentist and Bayesian inference. Second, we discuss misconceptions in the interpretation of frequentist statistical evidence. Finally, we show (as an alternative to the frequentist p-value) how the Bayes factor allows the auditor to obtain and interpret statistical evidence in line with audit standards. Thus, we contribute to audit theory and practice by showing how Bayesian inference can quantify audit evidence. Data Availability: The data supporting the findings in this article are available in the OSF repository at https://doi.org/10.17605/OSF.IO/WTN9G.1
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.
Samenvatting Bayesiaanse statistiek is een manier om efficiëntie en transparantie bij gestratificeerde steekproeven te vergroten. De reden hiervoor is dat Bayesiaanse statistiek de auditor dwingt ...om expliciet te maken welke informatie en kennis gebruikt wordt bij de evaluatie van de steekproef. In dit artikel worden twee belangrijke vormen van voorkennis beschreven die de auditor moet valideren. Vervolgens zijn deze vormen van voorkennis vertaald naar statistische modellen, die worden gebruikt bij het doen van een gestratificeerde steekproef. Afsluitend wordt gedemonstreerd hoe sommige van die modellen kunnen leiden tot nauwkeurigere foutschattingen en een transparantere audit. Keywords: Audit, Bayesiaanse statistiek, stratificatie
To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original ...research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then randomly assigned to complete 1 version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: Materials from different teams rendered statistically significant effects in opposite directions for 4 of 5 hypotheses, with the narrowest range in estimates being d = −0.37 to + 0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for 2 hypotheses and a lack of support for 3 hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, whereas considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.
Public Significance Statement
Research in the social sciences often has implications for public policies as well as individual decisions-for good reason, the robustness of research findings is therefore of widespread interest both inside and outside academia. Yet, even findings that directly replicate-emerging again when the same methodology is repeated-may not always prove conceptually robust to different methodological approaches. The present initiative suggests that crowdsourcing study designs using many research teams can help reveal the conceptual robustness of the effects, better informing the public about the state of the empirical evidence.
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