Researchers in career and technical education often ignore more effective ways of reporting and treating missing data and instead implement traditional, but ineffective, missing data methods (Gemici, Rojewski, & Lee, 2012). The recent methodological, and even the non-methodological, literature has increasingly emphasized the importance of reporting the presence of and methods for treating missing data and has encouraged implementing state-of-the-art missing data methods such as multiple imputation and maximum likelihood estimation (see Baraldi & Enders, 2010; Enders, 2006b; Schafer & Graham, 2002; Schlomer, Bauman, & Card, 2010). This article provides a brief overview of maximum likelihood methods for handling missing data, which have several advantages over multiple imputation methods. Additionally, practical strategies for implementing and reporting the treatment of missing data using maximum likelihood methods are discussed. (Contains 3 tables and 2 figures.)