Multilevel data often exist in educational studies. The focus of this study is to consider differential item functioning (DIF) for dichotomous items from a multilevel perspective. One of the most often used methods for detecting DIF in dichotomously scored items is the Mantel-Haenszel log odds-ratio. However, the Mantel-Haenszel reduces the analyses to one level, thus ignoring the natural nesting that often occurs in testing situations. In this dissertation, a multilevel statistical model for detecting DIF in dichotomously scored items that is equivalent to the traditional Mantel-Haenszel method for detecting DIF in dichotomously scored items will be presented. This model is called the Multilevel Mantel-Haenszel model. The reformulated Multilevel Mantel-Haenszel method is a special case of an item response theory model (IRT) embedded in a logistic regression model with discrete ability levels. Results for the Multilevel Mantel-Haenszel model were analyzed using the hierarchical generalized linear framework (HGLM) of the HLM multilevel software program. Parameter recovery of the Mantel-Haenszel log odds-ratio by the Multilevel Mantel-Haenszel model is first demonstrated by Illustrative examples. A simulation study provides further support that (1) the Multilevel Mantel-Haenszel can fully recover the log odds-ratio of the traditional Mantel-Haenszel , (2) the Multilevel Mantel-Haenszel is a method capable of properly detecting the presence of DIF in dichotomously scored items, and, (3) the Multilevel Mantel-Haenszel performance compares favorably to the performance of the traditional Mantel-Haenszel. [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/en-US/products/dissertations/individuals.shtml.]