author_facet Valentine, Jeffrey C.
Tanner‐Smith, Emily E.
Pustejovsky, James E.
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Valentine, Jeffrey C.
Tanner‐Smith, Emily E.
Pustejovsky, James E.
Lau, T. S.
author Valentine, Jeffrey C.
Tanner‐Smith, Emily E.
Pustejovsky, James E.
Lau, T. S.
spellingShingle Valentine, Jeffrey C.
Tanner‐Smith, Emily E.
Pustejovsky, James E.
Lau, T. S.
Campbell Systematic Reviews
Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
General Social Sciences
author_sort valentine, jeffrey c.
spelling Valentine, Jeffrey C. Tanner‐Smith, Emily E. Pustejovsky, James E. Lau, T. S. 1891-1803 1891-1803 Wiley General Social Sciences http://dx.doi.org/10.4073/cmdp.2016.1 <jats:title>Executive summary</jats:title><jats:p>Single‐case research designs are critically important for understanding the effectiveness of interventions that target individuals with low incidence disabilities (e.g., physical disabilities, autism spectrum disorders). These designs comprise an important part of the evidence base in fields such as special education and school psychology, and can provide credible and persuasive evidence for guiding practice and policy decisions. In this paper we discuss the development and use of between‐case standardized mean difference effect sizes for two popular single‐case research designs (the treatment reversal design and the multiple baseline design), and discuss how they might be used in meta‐analyses either with other single‐case research designs or in conjunction with between‐group research designs. Effect size computation is carried out using a user‐friendly web application, scdhlm, powered by the free statistical program R; no knowledge of R programming is needed to use this web application.</jats:p> Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application Campbell Systematic Reviews
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series Campbell Systematic Reviews
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title Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_unstemmed Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_full Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_fullStr Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_full_unstemmed Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_short Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_sort between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
topic General Social Sciences
url http://dx.doi.org/10.4073/cmdp.2016.1
publishDate 2016
physical 1-31
description <jats:title>Executive summary</jats:title><jats:p>Single‐case research designs are critically important for understanding the effectiveness of interventions that target individuals with low incidence disabilities (e.g., physical disabilities, autism spectrum disorders). These designs comprise an important part of the evidence base in fields such as special education and school psychology, and can provide credible and persuasive evidence for guiding practice and policy decisions. In this paper we discuss the development and use of between‐case standardized mean difference effect sizes for two popular single‐case research designs (the treatment reversal design and the multiple baseline design), and discuss how they might be used in meta‐analyses either with other single‐case research designs or in conjunction with between‐group research designs. Effect size computation is carried out using a user‐friendly web application, scdhlm, powered by the free statistical program R; no knowledge of R programming is needed to use this web application.</jats:p>
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author_facet Valentine, Jeffrey C., Tanner‐Smith, Emily E., Pustejovsky, James E., Lau, T. S., Valentine, Jeffrey C., Tanner‐Smith, Emily E., Pustejovsky, James E., Lau, T. S.
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description <jats:title>Executive summary</jats:title><jats:p>Single‐case research designs are critically important for understanding the effectiveness of interventions that target individuals with low incidence disabilities (e.g., physical disabilities, autism spectrum disorders). These designs comprise an important part of the evidence base in fields such as special education and school psychology, and can provide credible and persuasive evidence for guiding practice and policy decisions. In this paper we discuss the development and use of between‐case standardized mean difference effect sizes for two popular single‐case research designs (the treatment reversal design and the multiple baseline design), and discuss how they might be used in meta‐analyses either with other single‐case research designs or in conjunction with between‐group research designs. Effect size computation is carried out using a user‐friendly web application, scdhlm, powered by the free statistical program R; no knowledge of R programming is needed to use this web application.</jats:p>
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spelling Valentine, Jeffrey C. Tanner‐Smith, Emily E. Pustejovsky, James E. Lau, T. S. 1891-1803 1891-1803 Wiley General Social Sciences http://dx.doi.org/10.4073/cmdp.2016.1 <jats:title>Executive summary</jats:title><jats:p>Single‐case research designs are critically important for understanding the effectiveness of interventions that target individuals with low incidence disabilities (e.g., physical disabilities, autism spectrum disorders). These designs comprise an important part of the evidence base in fields such as special education and school psychology, and can provide credible and persuasive evidence for guiding practice and policy decisions. In this paper we discuss the development and use of between‐case standardized mean difference effect sizes for two popular single‐case research designs (the treatment reversal design and the multiple baseline design), and discuss how they might be used in meta‐analyses either with other single‐case research designs or in conjunction with between‐group research designs. Effect size computation is carried out using a user‐friendly web application, scdhlm, powered by the free statistical program R; no knowledge of R programming is needed to use this web application.</jats:p> Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application Campbell Systematic Reviews
spellingShingle Valentine, Jeffrey C., Tanner‐Smith, Emily E., Pustejovsky, James E., Lau, T. S., Campbell Systematic Reviews, Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application, General Social Sciences
title Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_full Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_fullStr Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_full_unstemmed Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_short Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_sort between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
title_unstemmed Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application
topic General Social Sciences
url http://dx.doi.org/10.4073/cmdp.2016.1