Best Practices for Constructing Confidence Intervals for the General Linear Model Under Non-Normality

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dc.contributor.advisorFlora, David B.
dc.creatorAdkins, Mark Christopher
dc.date.accessioned2018-05-28T12:49:13Z
dc.date.available2018-05-28T12:49:13Z
dc.date.copyright2017-11-14
dc.date.issued2018-05-28
dc.date.updated2018-05-28T12:49:13Z
dc.degree.disciplinePsychology (Functional Area: Quantitative Methods)
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractGiven the current climate surrounding the replication crisis facing scientific research, a subsequent call for methodological reform has been issued which explicates the need for a shift from null hypothesis significance testing to reporting of effect sizes and their confidence intervals (CI). However, little is known about the relative performance of CIs constructed following the application of techniques which accommodate for non-normality under the general linear model (GLM). We review these techniques of normalizing data transformations, percentile bootstrapping, bias-corrected and accelerated bootstrapping, and present results from a Monte Carlo simulation designed to evaluate CI performance based on these techniques. The effects of sample size, degree of association among predictors, number of predictors, and different non-normal error distributions were examined. Based on the performance of CIs in terms of coverage, accuracy, and efficiency, general recommendations are made regarding best practice about constructing CIs for the GLM under non-normality.
dc.identifier.urihttp://hdl.handle.net/10315/34511
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectQuantitative psychology
dc.subject.keywordsConfidence intervals
dc.subject.keywordsMonte Carlo simulation
dc.subject.keywordsBest practice
dc.subject.keywordsConfidence interval properties
dc.subject.keywordsNon-normality
dc.subject.keywordsGeneral linear model
dc.titleBest Practices for Constructing Confidence Intervals for the General Linear Model Under Non-Normality
dc.typeElectronic Thesis or Dissertation

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