{"refrec":{"BRefID":203592,"RR":"<b>Qian, S.S.; Craig, J.K.; Baustian, M.M.; Rabalais, N.N.</b> (2009). A Bayesian hierarchical modeling approach for analyzing observational data from marine ecological studies. <i>Mar. Pollut. Bull. 58(12)</i>: 1916-1921. <a href=\"https://dx.doi.org/10.1016/j.marpolbul.2009.09.029\" target=\"_blank\">https://dx.doi.org/10.1016/j.marpolbul.2009.09.029</a>","BEntID":195794,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Mar. Pollut. Bull. 58(12)</i>: 1916-1921. <a href=\"https://dx.doi.org/10.1016/j.marpolbul.2009.09.029\" target=\"_blank\">https://dx.doi.org/10.1016/j.marpolbul.2009.09.029</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Qian, S.S.; Craig, J.K.; Baustian, M.M.; Rabalais, N.N.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Qian, S.S. <i>et al.</i>","Englishabstract":"We introduce the Bayesian hierarchical modeling approach for analyzing observational data from marine ecological studies using a data set intended for inference on the effects of bottom-water hypoxia on macrobenthic communities in the northern Gulf of Mexico off the coast of Louisiana, USA. We illustrate (1) the process of developing a model, (2) the use of the hierarchical model results for statistical inference through innovative graphical presentation, and (3) a comparison to the conventional linear modeling approach (ANOVA). Our results indicate that the Bayesian hierarchical approach is better able to detect a “treatment” effect than classical ANOVA while avoiding several arbitrary assumptions necessary for linear models, and is also more easily interpreted when presented graphically. 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