BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20251210T210515EST-3997HfUt5h@132.216.98.100
DTSTAMP:20251211T020515Z
DESCRIPTION:Title: Bayesian Mediation Analysis\n\nAbstract: Mediation analy
 sis is used to study intermediate variables (M) that transmit the effect o
 f an independent variable (X) on a dependent variable (Y). For example\, a
 n 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 (fre
 quentist) and Bayesian approaches. In recent years social science research
 ers 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). The distribut
 ion of the mediated effect is often asymmetric (Craig\, 1936\; Lomnicki\, 
 1967\; Springer & Thompson\, 1966)\, and the best classical methods for ev
 aluating the significance of the mediated effect either take the asymmetri
 c distribution of the product into account or make no distributional assum
 ptions at all (Cheung 2007\, 2009\; MacKinnon\, Fritz\, Williams\, & Lockw
 ood 2007\; MacKinnon\, Lockwood\, & Williams\, 2004\; MacKinnon\, Lockwood
 \, Hoffmann\, West\, & Sheets\, 2002\; MacKinnon\, et al.\, 1995\; Shrout 
 & Bolger\, 2002\; Tofighi & MacKinnon\, 2011\; Valente\, Gonzalez\, Miočev
 ić\, & MacKinnon\, 2016\; Yuan & MacKinnon\, 2009). Bayesian methods can e
 asily accommodate the asymmetric distributions of the mediated effect and 
 other functions of the mediated effect\, e.g. effect size measures and cau
 sal estimates of indirect and direct effects. Furthermore\, Bayesian metho
 ds provide an intuitive framework for the inclusion of relevant prior info
 rmation into the statistical analysis. In this talk I will discuss the adv
 antages of Bayesian mediation analysis\, summarize recommendations that ca
 n be made for applied researchers based on the methodological literature o
 n Bayesian mediation analysis thus far\, and conclude with future directio
 ns for this line of research.\n
DTSTART:20191120T203000Z
DTEND:20191120T213000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Milica Miocevic (9I���������)
URL:/mathstat/channels/event/milica-miocevic-mcgill-un
 iversity-302631
END:VEVENT
END:VCALENDAR