Comparing the percentage of non-overlapping data approach and the hierarchical linear modeling approach for synthesizing single-case studies in autism research
•We compared two approaches for synthesizing single-case experimental data.•The first was the percentage of non-overlapping data (PND) approach.•The second was the hierarchical linear modeling (HLM) approach.•We analyzed a dataset on behavioral interventions for reducing challenging behavior.•The PN...
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| Published in | Research in autism spectrum disorders Vol. 11; pp. 112 - 125 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.03.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1750-9467 1878-0237 |
| DOI | 10.1016/j.rasd.2014.12.002 |
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| Abstract | •We compared two approaches for synthesizing single-case experimental data.•The first was the percentage of non-overlapping data (PND) approach.•The second was the hierarchical linear modeling (HLM) approach.•We analyzed a dataset on behavioral interventions for reducing challenging behavior.•The PND approach was overly liberal in identifying moderators.
We examined the performance of two approaches for synthesizing single-case experimental data: the percentage of non-overlapping data (PND) approach and the hierarchical linear modeling (HLM) approach. The comparison was performed by analyzing an empirical dataset on behavioral interventions for reducing challenging behavior in persons with autism by means of the two approaches. We compared the findings of both approaches for analyzing the outcomes of the behavioral interventions as well as for identifying moderating variables. With respect to the analysis of the interventions’ outcomes, similar positive results were found based on both approaches. With respect to the moderating variables, Functional analysis/assessment and Availability of follow up data were found to be statistically significant moderators by means of the PND as well as the HLM approach. The variables Intervention type, Availability of generalization attempts, Design type, and Availability of inter-rater reliability data were also found to be statistically significant moderators by means of the PND approach. The PND approach seems overly liberal in identifying statistically significant predictors in comparison to the HLM approach. |
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| AbstractList | •We compared two approaches for synthesizing single-case experimental data.•The first was the percentage of non-overlapping data (PND) approach.•The second was the hierarchical linear modeling (HLM) approach.•We analyzed a dataset on behavioral interventions for reducing challenging behavior.•The PND approach was overly liberal in identifying moderators.
We examined the performance of two approaches for synthesizing single-case experimental data: the percentage of non-overlapping data (PND) approach and the hierarchical linear modeling (HLM) approach. The comparison was performed by analyzing an empirical dataset on behavioral interventions for reducing challenging behavior in persons with autism by means of the two approaches. We compared the findings of both approaches for analyzing the outcomes of the behavioral interventions as well as for identifying moderating variables. With respect to the analysis of the interventions’ outcomes, similar positive results were found based on both approaches. With respect to the moderating variables, Functional analysis/assessment and Availability of follow up data were found to be statistically significant moderators by means of the PND as well as the HLM approach. The variables Intervention type, Availability of generalization attempts, Design type, and Availability of inter-rater reliability data were also found to be statistically significant moderators by means of the PND approach. The PND approach seems overly liberal in identifying statistically significant predictors in comparison to the HLM approach. |
| Author | Saenen, Lore Heyvaert, Mieke Onghena, Patrick Maes, Bea |
| Author_xml | – sequence: 1 givenname: Mieke surname: Heyvaert fullname: Heyvaert, Mieke email: Mieke.Heyvaert@ppw.kuleuven.be organization: Faculty of Psychology and Educational Sciences—KU Leuven, Leuven, Belgium – sequence: 2 givenname: Lore surname: Saenen fullname: Saenen, Lore organization: Faculty of Psychology and Educational Sciences—KU Leuven, Leuven, Belgium – sequence: 3 givenname: Bea surname: Maes fullname: Maes, Bea organization: Faculty of Psychology and Educational Sciences—KU Leuven, Leuven, Belgium – sequence: 4 givenname: Patrick surname: Onghena fullname: Onghena, Patrick organization: Faculty of Psychology and Educational Sciences—KU Leuven, Leuven, Belgium |
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| SubjectTerms | Behavioral interventions Challenging behavior Meta-analysis Single-case research Single-subject experimental designs Systematic review |
| Title | Comparing the percentage of non-overlapping data approach and the hierarchical linear modeling approach for synthesizing single-case studies in autism research |
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