False positives and other statistical errors in standard analyses of eye movements in reading.
In research on eye movements in reading, it is common to analyze a number of canonical dependent measures to study how the effects of a manipulation unfold over time. Although this gives rise to the well-known multiple comparisons problem, i.e. an inflated probability that the null hypothesis is inc...
| Publicado en: | Journal of Memory & Language Vol. 94; pp. 119 - 134 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Jun2017
|
| 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=121938525&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 121938525 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0749596X JMM jtl: Journal of Memory & Language issn: 0749596X maglogo: N pubinfo: dt: Jun2017 vid: 94 pid: 467 pub: Elsevier B.V. artinfo: ui: 121938525 10.1016/j.jml.2016.10.003 ppf: 119 ppct: 15 formats: tig: atl: False positives and other statistical errors in standard analyses of eye movements in reading. aug: au: von der Malsburg, Titus Angele, Bernhard affil: Department of Linguistics, University of Potsdam, Germany Department of Psychology, Bournemouth University, United Kingdom su: Computer simulation Reading Diagnostic errors Statistics Data analysis Measurement errors Eye movement measurements sug: subj: Computer simulation Reading Diagnostic errors Statistics Data analysis Measurement errors Eye movement measurements keyword: Eye-tracking False positives Null-hypothesis testing Sentence processing Eye-tracking False positives Null-hypothesis testing Sentence processing ab: In research on eye movements in reading, it is common to analyze a number of canonical dependent measures to study how the effects of a manipulation unfold over time. Although this gives rise to the well-known multiple comparisons problem, i.e. an inflated probability that the null hypothesis is incorrectly rejected (Type I error), it is accepted standard practice not to apply any correction procedures. Instead, there appears to be a widespread belief that corrections are not necessary because the increase in false positives is too small to matter. To our knowledge, no formal argument has ever been presented to justify this assumption. Here, we report a computational investigation of this issue using Monte Carlo simulations. Our results show that, contrary to conventional wisdom, false positives are increased to unacceptable levels when no corrections are applied. Our simulations also show that counter-measures like the Bonferroni correction keep false positives in check while reducing statistical power only moderately. Hence, there is little reason why such corrections should not be made a standard requirement. Further, we discuss three statistical illusions that can arise when statistical power is low, and we show how power can be improved to prevent these illusions. In sum, our work renders a detailed picture of the various types of statistical errors than can occur in studies of reading behavior and we provide concrete guidance about how these errors can be avoided. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|