Between-case standardized mean difference effect sizes for single-case designs: A primer and tutorial using the scdhlm web application

Authors

Jeffrey C. Valentine

Emily E. Tanner-Smith

James E. Pustejovsky

Timothy S. Lau

Published

December 19, 2016

Journal R package Web app

We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them. Even so, this d-statistic is better founded statistically than other effect size measures for single-case design, and unlike many general linear model approaches such as multilevel modelling or generalised additive models, it produces a standardised effect size that can be integrated over studies with different outcome measures. SPSS macros for both effect size computation and power analysis are available.

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Citation

BibTeX citation:
@article{valentine2016,
  author = {Valentine, Jeffrey C. and Tanner-Smith, Emily E. and
    Pustejovsky, James E. and Lau, Timothy S.},
  title = {Between-Case Standardized Mean Difference Effect Sizes for
    Single-Case Designs: {A} Primer and Tutorial Using the Scdhlm Web
    Application},
  journal = {Campbell Systematic Reviews},
  volume = {12},
  number = {1},
  pages = {1-31},
  date = {2016-12-19},
  url = {https://doi.org/10.4073/cmdp.2016.1},
  doi = {10.1016/j.jsp.2018.02.003},
  langid = {en}
}
For attribution, please cite this work as:
Valentine, J. C., Tanner-Smith, E. E., Pustejovsky, J. E., & Lau, T. S. (2016). Between-case standardized mean difference effect sizes for single-case designs: A primer and tutorial using the scdhlm web application. Campbell Systematic Reviews, 12(1), 1–31. https://doi.org/10.1016/j.jsp.2018.02.003