Call for papers special issue “Bayesian statistics in the field of psychotraumatology”

Call for Papers

Theme: Bayesian statistics in the field of psychotraumatology

Already over 250 years ago, Thomas Bayes published on inverse probability. The ideas of inverse probability and Bayes’ theorem have been longstanding and become more popular in fields like epidemiology, spatial modeling, clinical trials, and molecular genetics. However, in the field of posttraumatic stress research in the 21st century the statistical tools most often used by researchers are based on frequentist statistics, i.e., p-values and null hypothesis testing, or model selection using AIC/BIC. So while statisticians increasingly prefer Bayesian over frequentist statistics, these tools are only slowly entering the field of post-traumatic stress research. For this special issue we invite researchers to submit Bayesian papers, with a special focus on applications of Bayesian statistics to the analysis of empirical data.

European Journal of Psychotraumatology invites original research papers, Bayesian meta-analysis or systematic review articles on Bayesian approaches as well as short communications in which Bayesian analyses are used to replicate results. We will use a 2-step approach where we invite authors to submit their abstracts by December 1st 2015, and their full papers by April 1st 2016.

Some examples of where Bayesian statistics could be used:

  • Bayes Factors can be preferred over reporting p-values (Love et al., 2015; Wetzels et al., 2011).
  • Informative hypothesis are tested instead of testing the null hypothesis (e.g., Hoijtink, 2011).
  • Bayesian statistics is not based on large samples (i.e., the central limit theorem) and hence may produce reasonable results even with small to moderate sample sizes, especially when strong and defensible prior knowledge is available (Lee & Song, 2004; Hox et al., 2012; van de Schoot et al, 2015; Zhang et al., 2007).
  • Many scholars prefer Bayesian statistics because they believe population parameters should be viewed as random, or they prefer the Bayesian paradigm over the frequentist paradigm (see e.g., Dienes, 2011).
  • With Bayesian statistics one can incorporate (un)certainty about a parameter and update this knowledge through the prior distribution.
  • Expert opinions can be updated or confronted with data (O’Hagan et al., 2006).
  • Many complex models simply cannot be estimated using conventional statistics.
  • Some models (e.g., mixture or multilevel models) require Bayesian methods to improve convergence issues, aid in model identification, produce more accurate parameter estimates.

For an introduction to Bayesian modeling, we refer the novice reader to van de Schoot, Kaplan, et al. (2014). We recommend researchers to adhere to available checklists (Sung et al., 2005; Depaoli & Van de Schoot, in press).

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How to submit: Please go to the journal website, read the Author Instructions carefully and then click the Submit manuscript button in the upper right-hand corner and follow the instructions. The section to select is “Bayesian statistics”.

Publication fees: Please see here.

Timeline: The submission deadline for abstracts is December 1st 2015; for full papers (if abstract is accepted) the deadline is April 1st 2016. Your abstract will be reviewed at the latest two weeks after the submission deadline.

Guest Editor: Rens van de Schoot

Inquiries: Dr. Rens van de Schoot, or Professor Dr. Miranda Olff, Chief Editor of European Journal of Psychotraumatology.

 

Some References

Depaoli, S. & Van de Schoot (in press). Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist. Psychological Methods

Dienes, Z. (2011). Bayesian versus orthodox statistics: Which side are you on? Perspectives on Psychological Science, 6, 274-290.

Hoijtink, H. (2011). Informative hypotheses: Theory and practice for behavioral and social scientists. Chapman and Hall/CRC.

Hox, J., van de Schoot. R., & Matthijsse, S. (2012). How few countries will do? Comparative survey analysis from a Bayesian perspective. Survey Research Methods, 6, 87-93.

Lee, S.-Y., & Song, X.-Y. (2004). Evaluation of the Bayesian and maximum likelihood approaches in analyzing structural equation models with small sample sizes. Multivariate Behavioral Research, 39, 653 – 686.

Love, J., Selker, R., Marsman, M., Jamil, T., Verhagen, A. J., … & Wagenmakers, E.-J. (2015). JASP (Version 0.6.6)[Computer software].

O’Hagan, A., Buck, C. E., Daneshkhah, A., Richard Eiser, J., Garthwaite, P. H., Jenkinson, D. J., . . . & Rakow, T. (2006). Uncertain judgements: Eliciting experts’ probabilities. Chichester, UK: John Wiley & Sons.

Sung L, Hayden J, Greenberg ML, et al. (2005). Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study. Journal of Clinical Epidemiology, 58, 261-268.

van de Schoot, R., Broere, J., Perryck, K., Zondervan-Zwijnenburg, M., & van Loey, N. (2015). Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European Journal Of Psychotraumatology, 6. doi: http://dx.doi.org/10.3402/ejpt.v6.25216

Van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & Van Aken, M. A. (2014). A gentle introduction to Bayesian analysis: Applications to research in child development. Child Development, 85, 842-860.

Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E. J. (2011). Statistical evidence in experimental psychology an empirical comparison using 855 t tests. Perspectives on Psychological Science, 6, 291-298.

Zhang, Z., Hamagami, F., Wang, L., Grimm, K. J., & Nesselroade, J. R. (2007). Bayesian analysis of longitudinal data using growth curve models. International Journal of Behavioral Development, 31, 374-383.

 

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