A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

Abstract

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided.

Psychological processes in young bullies versus bully‐victims

“…When considering each process separately, the results yielded mixed evidence; however, when using Bayesian model selection to compare our two hypotheses as coherent models, the data clearly favored the shared processes hypothesis. This is one important illustration of why researchers in social science have recommended the use of Bayesian statistics (Van de Schoot et al, 2014). …”

“…Children's theory of mind skills, hostile intent attributions, and happy victimizer emotions were assessed using standard vignettes and false‐belief tasks; and their reactive and proactive motives for aggression using teacher‐reports. Our hypotheses were tested using Bayesian model selection; an upcoming statistical approach that in recent years has increasingly been used by researchers in child psychology (Van de Schoot et al, 2014). The advantage of this approach is that it quantifies the amount of support from the data for each hypothesis as a coherent model (instead of testing group differences for each variable separately, as would be the case with multivariate analyses).…”

How to become a Bayesian in eight easy steps: An annotated reading list

“…Ideally, readers who have read through all eight of our highlighted sources, and perhaps some of the supplementary reading, may now feel comfortable with the fundamental ideas in Bayesian data analysis, from basic principles (Kruschke, 2015;Lindley, 1993) to prior distribution selection (Lee & Vanpaemel, this issue), and with the interpretation of a variety of analyses, including Bayesian analogs of classical statistical tests (e.g., t-tests; Rouder et al, 2009), estimation in a Bayesian framework (van de Schoot et al, 2014), Bayes factors and other methods for hypothesis testing (Dienes, 2011;Vandekerckhove et al, 2015), and Bayesian cognitive models (Lee, 2008).…”

“…Our first source provides a broad overview of the most common methods of model comparison, including the Bayes factor, with a heavy emphasis on its proper interpretation (Vandekerckhove, Matzke, & Wagenmakers, 2015). The next source begins by demonstrating Bayesian estimation techniques in the context of developmental research, then provides some guidelines for reporting Bayesian analyses (van de Schoot et al, 2014). Our final two sources discuss issues in Bayesian cognitive modeling, such as the selection of appropriate priors (Lee & Vanpaemel, this issue), and the use of cognitive models for theory testing (Lee, 2008).…”

Bayesian hypothesis testing for human threat conditioning research: an introduction and the condir R package

“…d  = −.45). This piece of information can be taken into account when determining the prior distribution for the (see also van de Schoot et al, 2014, on how to determine prior knowledge in general). For our example, we used a Cauchy distribution for the with a scale parameter to be equal to .707.…”