The Effect of Autocorrelation on the Results of Visually Analyzing Data From Single-Subject Designs.
Objective. Single-subject research designs are used to conduct clinical research and outcome evaluation in occupational therapy. Confusion exists regarding the best method to analyze and interpret single-subject data. Method. One hundred graphs displaying the results of published single-subject rese...
| Publicado en: | American Journal of Occupational Therapy Vol. 52; no. 8; pp. 650 - 656 |
|---|---|
| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Occupational Therapy Association
Sep1998
|
| 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=86134211&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 86134211 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02729490 44I jtl: American Journal of Occupational Therapy issn: 02729490 maglogo: N pubinfo: dt: Sep1998 vid: 52 iid: 8 pid: 6680 pub: American Occupational Therapy Association place: North Bethesda, Maryland artinfo: ui: 86134211 86134211 107950360 10.5014/ajot.52.8.650 86134211 ppf: 650 ppct: 6 formats: fmt: @attributes: type: P tig: atl: The Effect of Autocorrelation on the Results of Visually Analyzing Data From Single-Subject Designs. aug: au: Bengali, Minoo K. Ottenbacher, Kenneth J. affil: Regional Manager, Central Jersey Rehabilitation Services, Toms River, New Jersey sug: subj: Data Analysis, Statistical Study Design Statistics Methods Research, Rehabilitation Random Sample kappa Statistic P-Value Chi Square Test Bias (Research) Pearson's Correlation Coefficient Human ab: Objective. Single-subject research designs are used to conduct clinical research and outcome evaluation in occupational therapy. Confusion exists regarding the best method to analyze and interpret single-subject data. Method. One hundred graphs displaying the results of published single-subject research were examined to determine the influence of autocorrelation on the visual inferences made by the original investigators. The graphs were selected from 20 articles published over 10 years in seven rehabilitation journals. The data were extrapolated and lag 1 autocorrelation coefficients computed for both the baseline and treatment phases. Results. Data analysis focused on two issues: (a) whether a relationship existed between the amount of autocorrelation present in a graph and the conclusion on the basis of visual analysis and (b) whether the amount of autocorrelation varied across different phases of the single-subject graphs. When a significant degree of autocorrelation was present, researchers using visual analysis were more likely to conclude that there was no clinically significant change in performance. Autocorrelation values were significantly higher in the treatment phases of the single-subject designs. Conclusion. Additional research is needed to establish a set of decision rules to assist clinicians in using visual analysis to evaluate the results of single-subject research. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|