Dear all,
We are happy to announce the next CRAM (Cognitive Research at McGill) session of this term on Friday, November 8th. Dr. Milica Miocevic will be speaking to us about Bayesian Mediation analysis.
The talk will span from 12pm-1pm in room 1552 of 2001 McGill College Avenue. Coffee and light snacks will be provided. Please BYOM (bring your own mug). All are welcome!
---- Title: Bayesian Mediation analysis Dr. Milica Miocevic, McGill University ----
Abstract: Mediation analysis is used to study intermediate variables (M) that transmit the effect of an independent variable (X) on a dependent variable (Y). For example, an intervention designed to reduce unhealthy habits (X) might affect fruit and vegetable consumption (M), which in turn might affect general health (Y). In this hypothetical study, the quantity of interest is the indirect effect of the intervention on general health through fruit and vegetable consumption.
Mediation analysis can be performed using both classical (frequentist) and Bayesian approaches (Yuan & MacKinnon, 2009). In recent years social science researchers have turned to Bayesian methods when they encounter convergence issues (Chen, Choi, Weiss, & Stapleton, 2014), issues due to small samples (Lee & Song, 2004), and when they wish to report the probability that a parameter lies within a certain interval (Rindskopf, 2012).
Bayesian methods can easily accommodate the asymmetric distributions of the mediated effect and other functions of the mediated effect, e.g. effect size measures and causal estimates of indirect and direct effects. Furthermore, Bayesian methods provide an intuitive framework for the inclusion of relevant prior information into the statistical analysis. In this talk I will discuss the advantages of Bayesian mediation analysis, summarize recommendations that can be made for applied researchers based on the methodological literature on Bayesian mediation analysis thus far, and conclude with future directions for this line of research.
Warm regards, The CRAM Team (Kevin da Silva Castanheira, Anna Mini Jos, & Azara Lalla)
Hi everyone,
This is a friendly reminder that Dr. Milica Miocevic will be presenting at CRAM today in room 1552, 2001 McGill College at 12PM. See below for title and abstract. Coffee and light snacks will be served. Please BYOM (bring your own mug).
Hope to see you there! The CRAM Team
---------- Forwarded Message ----------- From:"Cognitive Research at McGill" cram@psych.mcgill.ca To:info@crblm.ca, coggroup@psych.mcgill.ca, grad@psych.mcgill.ca, postdoc@psych.mcgill.ca, faculty@psych.mcgill.ca Sent:Mon, 4 Nov 2019 12:00:06 -0500 Subject:CRAM - This Friday - November 8th
Dear all,
We are happy to announce the next CRAM (Cognitive Research at McGill) session of this term on Friday, November 8th. Dr. Milica Miocevic will be speaking to us about Bayesian Mediation analysis.
The talk will span from 12pm-1pm in room 1552 of 2001 McGill College Avenue. Coffee and light snacks will be provided. Please BYOM (bring your own mug). All are welcome!
---- Title: Bayesian Mediation analysis Dr. Milica Miocevic, McGill University ----
Abstract: Mediation analysis is used to study intermediate variables (M) that transmit the effect of an independent variable (X) on a dependent variable (Y). For example, an intervention designed to reduce unhealthy habits (X) might affect fruit and vegetable consumption (M), which in turn might affect general health (Y). In this hypothetical study, the quantity of interest is the indirect effect of the intervention on general health through fruit and vegetable consumption.
Mediation analysis can be performed using both classical (frequentist) and Bayesian approaches (Yuan & MacKinnon, 2009). In recent years social science researchers have turned to Bayesian methods when they encounter convergence issues (Chen, Choi, Weiss, & Stapleton, 2014), issues due to small samples (Lee & Song, 2004), and when they wish to report the probability that a parameter lies within a certain interval (Rindskopf, 2012).
Bayesian methods can easily accommodate the asymmetric distributions of the mediated effect and other functions of the mediated effect, e.g. effect size measures and causal estimates of indirect and direct effects. Furthermore, Bayesian methods provide an intuitive framework for the inclusion of relevant prior information into the statistical analysis. In this talk I will discuss the advantages of Bayesian mediation analysis, summarize recommendations that can be made for applied researchers based on the methodological literature on Bayesian mediation analysis thus far, and conclude with future directions for this line of research.
Warm regards, The CRAM Team (Kevin da Silva Castanheira, Anna Mini Jos, & Azara Lalla) ------- End of Forwarded Message -------