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

A 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...

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Main Author: Xu, Weiwei, active 2013
Format: Others
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/2152/24072
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spelling ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-240722015-09-20T17:22:35ZSoftware implementation of modeling and estimation of effect size in multiple baseline designsXu, Weiwei, active 2013Effect sizeMultiple baseline designsSingle case studyProc mixedRestricted maximum likelihoodHierachical linear modelProc IMLA 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.text2014-04-22T15:41:34Z2013-122013-12-17December 20132014-04-22T15:41:34ZThesisapplication/pdfhttp://hdl.handle.net/2152/24072
collection NDLTD
format Others
sources NDLTD
topic Effect size
Multiple baseline designs
Single case study
Proc mixed
Restricted maximum likelihood
Hierachical linear model
Proc IML
spellingShingle Effect size
Multiple baseline designs
Single case study
Proc mixed
Restricted maximum likelihood
Hierachical linear model
Proc IML
Xu, Weiwei, active 2013
Software implementation of modeling and estimation of effect size in multiple baseline designs
description A 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. === text
author Xu, Weiwei, active 2013
author_facet Xu, Weiwei, active 2013
author_sort Xu, Weiwei, active 2013
title Software implementation of modeling and estimation of effect size in multiple baseline designs
title_short Software implementation of modeling and estimation of effect size in multiple baseline designs
title_full Software implementation of modeling and estimation of effect size in multiple baseline designs
title_fullStr Software implementation of modeling and estimation of effect size in multiple baseline designs
title_full_unstemmed Software implementation of modeling and estimation of effect size in multiple baseline designs
title_sort software implementation of modeling and estimation of effect size in multiple baseline designs
publishDate 2014
url http://hdl.handle.net/2152/24072
work_keys_str_mv AT xuweiweiactive2013 softwareimplementationofmodelingandestimationofeffectsizeinmultiplebaselinedesigns
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