Examining Estimates of Effect Using Non-Overlap of All Pairs in Multiple Baseline Studies of Academic Intervention

Petersen‐Brown, Shawna; Karich, Abbey C.; Symons, Frank J.

doi:10.1007/s10864-012-9154-0

Cited by 27 publications

(22 citation statements)

References 87 publications

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“…Research is necessary though to identify such benchmarks. A promising approach to developing SCED benchmarks involves the combined use of visual and statistical analyses Petersen-Brown, Karich, & Symons, 2012) to obtain evidence on what values of the numerical indicators can reasonably be labelled "small" or "large", using visual analysis results as the reference standard. A related challenge is how to compare intervention effects quantified with different primary indicators, for instance, a standardized mean difference arising from generalised least squares regression (Maggin et al, 2011) and the Nonoverlap of all pairs ).…”

Section: Analysing Sced Data: Issues For Further Research or Discussionmentioning

confidence: 99%

Single-case experimental designs: Reflections on conduct and analysis

Manolov

Gast

Perdices

et al. 2014

Neuropsychological Rehabilitation

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In this editorial discussion we reflect on the issues addressed by, and arising from, the papers in this Special Issue on Single Case Experimental Design (SCED) study methodology. We identify areas of consensus and disagreement regarding the conduct and analysis of SCED studies.Despite the long history of application of SCEDs in studies of interventions in clinical and educational settings, the field is still developing. There is an emerging consensus on methodological quality criteria for many aspects of SCEDs, but disagreement on what are the most appropriate methods of SCED data analysis. Our aim is to stimulate this ongoing debate and highlight issues requiring further attention from applied researchers and methodologists. In addition we offer tentative criteria to support decision making in relation to selection of analytical techniques in SCED studies. Finally, we stress that large-scale interdisciplinary collaborations, such as the current Special Issue, are necessary if SCEDs are going to play a significant role in the development of the evidence base for clinical practice.3

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Section: Analysing Sced Data: Issues For Further Research or Discussionmentioning

confidence: 99%

Single-case experimental designs: Reflections on conduct and analysis

Manolov

Gast

Perdices

et al. 2014

Neuropsychological Rehabilitation

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“…Both raters had completed doctoral-level specialized coursework on SCD research methods and had collaborated on several SCD studies. Procedures for visual analysis implemented by Petersen-Brown, Karich, and Symons (2012) were replicated in the current investigation. Specifically, four indicators were adopted to make visual analysis judgments and if changes in at least two out of four indicators were detected in a phase or condition contrast, an intervention effect was coded for the specific contrast.…”

Section: Methodsmentioning

confidence: 99%

“…For phase contrasts, we utilized the same operational definitions for the four indicators as those described in Petersen-Brown et al (2012). Immediacy was defined as whether there was a difference between last three data points in Phase A versus the first three data points in Phase B.…”

Section: Methodsmentioning

confidence: 99%

Comparing Single Case Design Overlap-Based Effect Size Metrics From Studies Examining Speech Generating Device Interventions

Chen

Hyppa-Martin

Reichle

et al. 2016

American Journal on Intellectual and Developmental Disabilities

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Meaningfully synthesizing single case experimental data from intervention studies comprised of individuals with low incidence conditions and generating effect size estimates remains challenging. Seven effect size metrics were compared for single case design (SCD) data focused on teaching speech generating device use to individuals with intellectual and developmental disabilities (IDD) with moderate to profound levels of impairment. The effect size metrics included percent of data points exceeding the median (PEM), percent of nonoverlapping data (PND), improvement rate difference (IRD), percent of all nonoverlapping data (PAND), Phi, nonoverlap of all pairs (NAP), and Taunovlap. Results showed that among the seven effect size metrics, PAND, Phi, IRD, and PND were more effective in quantifying intervention effects for the data sample (N = 285 phase or condition contrasts). Results are discussed with respect to issues concerning extracting and calculating effect sizes, visual analysis, and SCD intervention research in IDD.

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“…Thus, probabilities may constitute an aid in labeling an effect as "large" or "small," although such an interpretation will necessarily be subjected to the practical importance of the results and can also be guided via visual analysis (e.g., Petersen-Brown et al, 2012).…”

Section: Methodological Implicationsmentioning

confidence: 99%

Assigning and combining probabilities in single-case studies: A second study

Manolov

Solanas

2013

Behav Res

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The present study builds on a previous proposal for assigning probabilities to the outcomes computed using different primary indicators in single-case studies. These probabilities are obtained by comparing the outcome to previously tabulated reference values, and they reflect the likelihood of the results in the case that no intervention effect is present. In the present study, we explored how well different metrics are translated into p values in the context of simulation data. Furthermore, two published multiple-baseline data sets were used to illustrate how well the probabilities might reflect the intervention effectiveness, as assessed by the original authors. Finally, the importance of which primary indicator would be used in each data set to be integrated was explored; two ways of combining probabilities were used: a weighted average and a binomial test. The results indicated that the translation into p values worked well for the two nonoverlap procedures, with the results for the regression-based procedure diverging due to some undesirable features of its performance. These p values, when either taken individually or combined, were well aligned with effectiveness for the real-life data. These results suggest that assigning probabilities can be useful for translating the primary measure into the same metric, using these probabilities as additional evidence of the importance of behavioral change, complementing visual analysis and professionals' judgments.

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Examining Estimates of Effect Using Non-Overlap of All Pairs in Multiple Baseline Studies of Academic Intervention

Cited by 27 publications

References 87 publications

Single-case experimental designs: Reflections on conduct and analysis

Single-case experimental designs: Reflections on conduct and analysis

Comparing Single Case Design Overlap-Based Effect Size Metrics From Studies Examining Speech Generating Device Interventions

Assigning and combining probabilities in single-case studies: A second study

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