Software implementation of modeling and estimation of effect size in multiple baseline designs

Simple item page
dc.contributor.advisorBeretvas, Susan Natasha
dc.creatorXu, Weiwei, active 2013en
dc.date.accessioned2014-04-22T15:41:34Zen
dc.date.accessioned2017-05-11T22:58:13Z
dc.date.available2017-05-11T22:58:13Z
dc.date.issued2013-12en
dc.date.submittedDecember 2013en
dc.date.updated2014-04-22T15:41:34Zen
dc.descriptiontexten
dc.description.abstractA generalized design-comparable effect size modeling and estimation for multiple baseline designs across individuals has been proposed and evaluated by Restricted Maximum Likelihood method in a hierarchical linear model using R. This report evaluates the exact approach of the modeling and estimation by SAS. Three models (MB3, MB4 and MB5) with same fixed effects and different random effects are estimated by PROC MIXED procedure with REML method. The unadjusted size and adjusted effect size are then calculated by matrix operation package PROC IML. The estimations for the fixed effects of the three models are similar to each other and to that of R. The variance components estimated by the two software packages are fairly close for MB3 and MB4, but the results are different for MB5 which exhibits boundary conditions for variance-covariance matrix. This result suggests that the nlme library in R works differently than the PROC MIXEDREML method in SAS under extreme conditions.en
dc.description.departmentStatisticsen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/24072en
dc.subjectEffect sizeen
dc.subjectMultiple baseline designsen
dc.subjectSingle case studyen
dc.subjectProc mixeden
dc.subjectRestricted maximum likelihooden
dc.subjectHierachical linear modelen
dc.subjectProc IMLen
dc.titleSoftware implementation of modeling and estimation of effect size in multiple baseline designsen
dc.typeThesisen

Files

Collections

University of Texas at Austin