scholarly journals Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application

2016 ◽  
Vol 12 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Jeffrey C. Valentine ◽  
Emily E. Tanner‐Smith ◽  
James E. Pustejovsky ◽  
T. S. Lau
Keyword(s):  
Web Application ◽  
Single Case ◽  
Mean Difference ◽  
Effect Sizes ◽  
Standardized Mean Difference ◽  
Single Case Designs ◽  
Difference Effect

A standardized mean difference effect size for single case designs

2012 ◽  
Vol 3 (3) ◽  
pp. 224-239 ◽  
Author(s):  
Larry V. Hedges ◽  
James E. Pustejovsky ◽  
William R. Shadish
Keyword(s):  
Effect Size ◽  
Single Case ◽  
Mean Difference ◽  
Standardized Mean Difference ◽  
Single Case Designs ◽  
Difference Effect

scholarly journals Procedural sensitivities of effect sizes for single-case designs with directly observed behavioral outcome measures

2018 ◽  
Author(s):  
James E Pustejovsky
Keyword(s):  
Direct Observation ◽  
Effect Size ◽  
Single Case ◽  
Mean Difference ◽  
Effect Sizes ◽  
Observation Data ◽  
Improvement Rate ◽  
Systematic Direct Observation ◽  
Standardized Mean Difference ◽  
Single Case Designs

A wide variety of effect size indices have been proposed for quantifying the magnitude of treatment effects in single-case designs. Commonly used measures include parametric indices such as the standardized mean difference, as well as non-overlap measures such as the percentage of non-overlapping data, improvement rate difference, and non-overlap of all pairs. Currently, little is known about the properties of these indices when applied to behavioral data collected by systematic direct observation, even though systematic direct observation is the most common method for outcome measurement in single-case research. This study uses Monte Carlo simulation to investigate the properties of several widely used single-case effect size measures when applied to systematic direct observation data. Results indicate that the magnitude of the non-overlap measures and of the standardized mean difference can be strongly influenced by procedural details of the study's design, which is a significant limitation to using these indices as effect sizes for meta-analysis of single-case designs. A less widely used parametric index, the log-response ratio, has the advantage of being insensitive to sample size and observation session length, although its magnitude is influenced by the use of partial interval recording.

Are Challenge (Ropes) Courses an Effective Tool? A Meta-Analysis

2008 ◽  
Vol 31 (2) ◽  
pp. 111-135 ◽  
Author(s):  
H. Lee Gillis ◽  
Elizabeth Speelman
Keyword(s):  
Meta Analysis ◽  
Team Building ◽  
Effect Sizes ◽  
Ropes Course ◽  
Challenge Course ◽  
Study Characteristics ◽  
Standardized Mean Difference ◽  
Ropes Courses ◽  
Difference Effect

This study reports the results of a meta-analysis of 44 studies that examined the impacts of participation in challenge (ropes) course activities. Overall, a medium standardized mean difference effect size was found (d = 0.43). Effect sizes were calculated for various study characteristics, including demographics and outcome. Higher effects were found for adult groups (d = 0.80) and for studies measuring family functioning (d = 0.67). Studies with therapeutic (d = 0.53) or developmental foci (d = 0.47) had higher effect sizes than those with educational foci (d = 0.17). Higher effect sizes for group effectiveness (d = 0.62) affirmed the use of challenge course experiences for team-building purposes. Implications for further research include the importance of recording detailed program design information, selecting appropriate instrumentation, and including follow-up data.

scholarly journals Has the time come to stop using the “standardised mean difference”?

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Pim Cuijpers
Keyword(s):  
Effect Size ◽  
Mean Difference ◽  
Effect Sizes ◽  
Binary Outcomes ◽  
Number Needed To Treat ◽  
Statistical Concept ◽  
Clinical Meaning ◽  
Meta Analyses ◽  
Size Display ◽  
Difference Effect

Background Most meta-analyses use the ‘standardised mean difference’ (effect size) to summarise the outcomes of studies. However, the effect size has important limitations that need to be considered. Method After a brief explanation of the standardized mean difference, limitations are discussed and possible solutions in the context of meta-analyses are suggested. Results When using the effect size, three major limitations have to be considered. First, the effect size is still a statistical concept and small effect sizes may have considerable clinical meaning while large effect sizes may not. Second, specific assumptions of the effect size may not be correct. Third, and most importantly, it is very difficult to explain what the meaning of the effect size is to non-researchers. As possible solutions, the use of the ‘binomial effect size display’ and the number-needed-to-treat are discussed. Furthermore, I suggest the use of binary outcomes, which are often easier to understand. However, it is not clear what the best binary outcome is for continuous outcomes. Conclusion The effect size is still useful, as long as the limitations are understood and also binary outcomes are given.

Standing in the Gaps: Examining the Effects of Early Gifted Education on Black Girl Achievement in STEM

2017 ◽  
Vol 28 (4) ◽  
pp. 290-312 ◽  
Author(s):  
Jemimah L. Young ◽  
Jamaal R. Young ◽  
Donna Y. Ford
Keyword(s):  
Gifted Education ◽  
Science Achievement ◽  
Comparison Group ◽  
Black Girls ◽  
Mean Difference ◽  
Effect Sizes ◽  
Group Differences ◽  
White Girls ◽  
Girls In Science ◽  
Difference Effect

The purpose of this study was to explore the differential effects of access to gifted education on the mathematics and science achievement of fourth-grade Black girls. This study utilized mean difference effect sizes to examine the magnitude of differences between groups. By convention, White girls were included as a comparison group. Girls receiving gifted instruction and girls not receiving gifted instruction were the populations of interest ( N = 13,868). The mathematics results suggest that Black girls participating in gifted education statistically significantly outperform Black girls in the comparison group. The mean difference effect sizes for within-group differences were almost twice as large for Black girls compared with White girls. The science results indicate that Black girls receiving gifted instruction outperformed Black girls in the comparison group. White girls, regardless of access to gifted instruction, statistically significantly outperformed Black girls in science. These results inform the recommendations provided.

A standardized mean difference effect size for multiple baseline designs across individuals

2013 ◽  
Vol 4 (4) ◽  
pp. 324-341 ◽  
Author(s):  
Larry V. Hedges ◽  
James E. Pustejovsky ◽  
William R. Shadish
Keyword(s):  
Effect Size ◽  
Mean Difference ◽  
Multiple Baseline ◽  
Standardized Mean Difference ◽  
Difference Effect
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