Comparison of visual inspection and statistical analysis of single-subject data in rehabilitation research.
Single-subject designs are being advocated to conduct outcome research in rehabilitation environments. The methods provide an alternative to traditional designs based on statistical comparisons across groups. Data analysis in single subject research does not rely on statistical hypothesis testing of...
| Publicado en: | American Journal of Physical Medicine & Rehabilitation Vol. 77; no. 2; pp. 94 - 103 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
1998 Mar-Apr
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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=107278571&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107278571 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08949115 44N jtl: American Journal of Physical Medicine & Rehabilitation issn: 08949115 maglogo: N pubinfo: dt: 1998 Mar-Apr vid: 77 iid: 2 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 107278571 107278571 1998051969 NLM9558008 107278571 ppf: 94 ppct: 9 formats: tig: atl: Comparison of visual inspection and statistical analysis of single-subject data in rehabilitation research. aug: au: Bobrovitz CD Ottenbacher KJ affil: State University of New York at Buffalo, Buffalo, New York sug: subj: Graphics Data Analysis, Statistical Study Design Outcomes (Health Care) Evaluation Rehabilitation Sensitivity and Specificity Paired T-Tests Descriptive Statistics Human ab: Single-subject designs are being advocated to conduct outcome research in rehabilitation environments. The methods provide an alternative to traditional designs based on statistical comparisons across groups. Data analysis in single subject research does not rely on statistical hypothesis testing of responses collected from a sample of subjects. Instead, visual inspection of patient responses graphed over time is the usual method of data analysis in single-subject research. This study examined the agreement between visual analysis and statistical tests of single-subject data for 42 hypothetical single-subject graphs. Specially constructed graphs allowed the systematic manipulation of different treatment effect sizes across a commonly used single-subject design. Thirty-two rehabilitation and health care providers rated each of the 42 graphs to determine whether a clinically significant treatment effect existed across the phases of the designs. Data analysis focused on two questions: (1) How much agreement was there between visual judgments and the results of statistical tests? and (2) What level of treatment effect was required to produce a finding of visual versus statistical significance? The agreement between visual analysis and statistical significance was high (86%). The sensitivity of visual inferences compared with statistical test results was 0.84, specificity was 0.88, and positive predictive value was 0.91. Both visual and statistical procedures were sensitive to medium and large treatment effects in the 42 single-subject graphs examined in this study. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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