Evaluation of five effect size measures of measurement non-invariance for continuous outcomes To make meaningful comparisons on a construct of interest across groups or over time, measurement invariance needs to exist for at least a subset of the observed variables that define the construct. Often, chi-square difference tests are used to test for measurement invariance. However, these statistics are affected by sample size such that larger sample sizes are associated with a greater prevalence of significant tests. Thus, using other measures of non-invariance to aid in the decision process would be beneficial. For this dissertation project, I proposed four new effect size measures of measurement non-invariance and analyzed a Monte Carlo simulation study to evaluate their properties and behavior in addition to the properties and behavior of an already existing effect size measure of non-invariance. The effect size measures were evaluated based on bias, variability, and consistency. Additionally, the factors that affected the value of the effect size measures were analyzed. All studied effect sizes were consistent, but three were biased under certain conditions. Further work is needed to establish benchmarks for the unbiased effect sizes.autGunn, Heather JthsGrimm, Kevin J.thsEdwards, Michael CdgcTein, Jenn-YundgcAnderson, Samantha F.pblArizona State UniversityengPartial requirement for: Ph.D., Arizona State University, 2019Includes bibliographical references (pages 60-68)Field of study: Psychologyby Heather J. Gunnhttps://hdl.handle.net/2286/R.I.5345800Doctoral DissertationAcademic thesesvii, 100 pages : color illustrations115579410351630032421157034systemIn Copyright2019TextQuantitative psychologyeffect sizemeasurement invariancenon-invarianceSimulationpsychometricsEffect sizes (Statistics)