Multilevel modeling is commonly used with clustered data, and much emphasis has been placed specifically on the multilevel linear model (MLM). When modeling clustered ordinal data, a multilevel ordinal model with cumulative logit link assuming proportional odds (i.e., multilevel cumulative logit model) is typically used. Depending on the research questions and inferences a researcher would like to draw from his/her findings, generalized estimating equations (GEE), a type of population average model, may be used as an alternative to multilevel cumulative logit model. Despite their usability, GEE for ordinal data and multilevel cumulative logit models are not often encountered in the applied literature. One of the reasons for underuse of these models may be that researchers are not familiar with the theory and specification of different approaches for analyzing clustered ordinal data. The goal of this dissertation was to investigate appropriateness of methods for modeling clustered ordinal data under different study conditions, and clarify interpretation differences between multilevel cumulative logit models and GEE approaches. In the current study, a simulation study was conducted to systematically examine the performance of multilevel cumulative logit models and GEE for modeling clustered ordinal data across different study conditions. Overall, two modeling approaches performed similarly regarding fixed effects biases and statistical power under different study conditions. However, when there was a smaller number of clusters (i.e., 10 or 30 clusters), the performance of GEE method was inferior to multilevel cumulative logit models in terms of 95% confidence interval coverage rates. Using multilevel cumulative logit models are useful to examine the contextual effects and the variation between clusters. In order to obtain reliable estimates of fixed and random effects, at least 50 clusters or more are needed, assuming all the assumptions of multilevel cumulative logit models are met. GEE can be used if the between-cluster variation and contextual effects are not of interest but the clustering effect should be taken into account. However, GEE should be used "only" when 50 clusters or more are available in the data when working with ordinal outcomes; if the number of clusters were less than 50 for GEE, the researchers should use the small sample size standard error correction. [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://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]