Psychologists have become increasingly interested in the intra-individual variability of psychological measures as a meaningful distinguishing characteristic of persons. Assessments of intra-individual variability are frequently based on the repeated administration of self-report rating scale instruments, and extreme response style (ERS) has the potential to bias the measurement of intra-individual variability in psychological constructs (Baird, Lucas & Donnellan, 2017). The current study proposes a multilevel extension of multidimensional nominal response model (ML-MNRM) to explore such bias for modeling extreme response styles applied to repeated measures rating scale data. For the real data analyses, modeling responses to multi-item scales of positive and negative affect collected from a smoking cessation study by ML-MNRM revealed considerable ERS bias in the intra-individual sum score variances. In addition, simulation studies based on the parameter estimates from real data suggest the magnitude and direction of bias due to ERS are heavily dependent on the mean affect level, supporting a model-based approach to the study and control of the nonlinear and disordinal biasing effects of ERS. Application of the proposed model-based correction is found to improve intra-individual variability as a predictor of smoking cessation. Moreover, simulation analyses in both non-trend and linear trend conditions further validate the effectiveness of such model-based approach in controlling and correcting for potential non-linear ERS effects on the respondent-level latent traits. Simulation analyses also investigate whether the psychometric characteristics of the rating scales, in particular the item parallelism plays a role in the measurement of latent traits such as ERS, latent mean and intra-individual variability. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]