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.

Van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & Van Aken, M.A.G. (2014). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development, 85(3), 842–860.

The data and Mplus/AMOS/BUGS syntax files

The Mplus syntax files for the models described in tables 3 and 4

The LOGIC data are available upon request. For these data, please contact Prof. Jens Asendorpf ( To look into the Neyer & Asendorpf data, contact Prof. Franz Neyer (

  • On page 850 we refer to Table 2 and state that Prior 6B  is closer to the ML estimate, but it should have been Prior 6A (with many thanks to Ingrid Arts for letting me know).
  • A sentence in the third paragraph of page 847 is incorrect: it should be “by decreasing its variance” (with many thanks to Mercy Manyema and Evie Izeboud for letting me know). Hereby the corrected version: Figure 1e shows that we can increase the precision of our prior distribution by decreasing its prior variance.