Improved analyses of single cases: Dynamic multilevel analysis.
This project identifies some difficulties when analyzing single-case data and showcases a new method, dynamic multilevel analysis (DMA). We re-analyze a published, meta-analysis of single-case interventions for participants with autism. Analytic difficulties include missing data, nested data, baseli...
| Publicado en: | Developmental Neurorehabilitation Vol. 21; no. 4; pp. 253 - 266 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
May2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129811859&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129811859 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17518423 2Y75 jtl: Developmental Neurorehabilitation issn: 17518423 maglogo: Y pubinfo: dt: May2018 vid: 21 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 129811859 129811859 129811859 10.3109/17518423.2015.1119904 129811859 ppf: 253 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improved analyses of single cases: Dynamic multilevel analysis. aug: au: Chiu, Ming Ming Roberts, Carly A. affil: Department of Educational Studies, Purdue University, West Lafayette, IN, USA sug: subj: Autism Spectrum Disorder Therapy Human Hypothesis False Positive Results Independent Variable False Negative Results Sampling Error Time Series Statistics Experimental Studies Meta Analysis ab: This project identifies some difficulties when analyzing single-case data and showcases a new method, dynamic multilevel analysis (DMA). We re-analyze a published, meta-analysis of single-case interventions for participants with autism. Analytic difficulties include missing data, nested data, baseline trends, time periods, recency effects, many hypotheses’ false positives, interactions among explanatory variables, indirect effects (including false negatives), and sampling errors. Furthermore, non-overlapping analyses can yield contested results, overvalue data near overlap boundaries, lose statistical power, and lack estimates of explained variance or unexplained residuals. To address these difficulties, DMA integrates several methods, including multilevel and time-series analyses. DMA re-analysis not only showed robust intervention effects, but also time-, outcome-, and intervention component-specific effects. Moreover, DMA informs the suitability of time hypotheses or meta-analysis, and DMA’s components can be used separately, notably its time-series analyses for small samples (e.g., one participant). Hence, DMA can help researchers analyze single-case data more accurately. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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