A multiple randomization testing procedure for level, trend, variability, overlap, immediacy, and consistency in single-case phase designs.
We present an approach to draw multiple and powerful inferences for each data aspect of single-case ABAB phase designs: level, trend, variability, overlap, immediacy, and consistency of data patterns. We show step-by-step how effect size measures can be calculated for each data aspect and subsequent...
| Publicado en: | Behaviour Research & Therapy Vol. 119; pp. 103414 - 103415 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Aug2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=137073396&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 137073396 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00057967 BHT jtl: Behaviour Research & Therapy issn: 00057967 maglogo: N pubinfo: dt: Aug2019 vid: 119 pid: 2410 pub: Elsevier B.V. artinfo: ui: 137073396 10.1016/j.brat.2019.103414 ppf: 103414 ppct: 1 formats: tig: atl: A multiple randomization testing procedure for level, trend, variability, overlap, immediacy, and consistency in single-case phase designs. aug: au: Tanious, René De, Tamal Kumar Onghena, Patrick affil: Faculty of Psychology and Educational Sciences, Methodology of Educational Sciences Research Group, KU Leuven – University of Leuven, Leuven, Belgium su: False positive error False discovery rate Randomization (Statistics) Computer software sug: subj: Software publishers (except video game publishers) Computer and software stores Computer, computer peripheral and pre-packaged software merchant wholesalers Computer and Computer Peripheral Equipment and Software Merchant Wholesalers False positive error False discovery rate Randomization (Statistics) Computer software keyword: ABAB phase design Effect size measures Randomization tests Single-case experimental designs Visual analysis ABAB phase design Effect size measures Randomization tests Single-case experimental designs Visual analysis ab: We present an approach to draw multiple and powerful inferences for each data aspect of single-case ABAB phase designs: level, trend, variability, overlap, immediacy, and consistency of data patterns. We show step-by-step how effect size measures can be calculated for each data aspect and subsequently integrated as test statistics in multiple randomization tests. To control for Type I errors, we discuss three methods for adjusting the obtained p -values based on the false discovery rate: the multiple testing correction proposed by Benjamini and Hochberg (1995), the adaptive correction suggested by Benjamini and Hochberg (2000), and the correction taking into account the dependency between the tests (Benjamini & Yekutieli, 2001). We apply this approach to a published data set and compare the results to the conclusions drawn by the authors based on visual analysis. The multiple randomization testing procedure can give more detailed information about which data aspects are affected by the single-case intervention. We provide generic R-code to execute the presented analyses. • Standards for analyzing single-case experiments urge analyzing level, trend, variability, overlap, immediacy, and consistency • Randomization tests are valid statistical tests for each data aspect in single-case experiments. • Multiple randomization tests can be performed using quantifications for each data aspect as test statistics. • When performing multiple tests simultaneously, the obtained p -values should be adjusted to control the false discovery rate. • R computer programs are provided to execute all presented analyses. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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