Most quantitative analyses in workforce education are affected by missing data. Traditional approaches to remedy missing data problems often result in reduced statistical power and biased parameter estimates due to systematic differences between missing and observed values. This article examines the treatment of missing data in pertinent quantitative analyses published in recent issues of Career and Technical Education Research. Next, essential missing data patterns and mechanisms are reviewed, and alternative methods of handling missing data are discussed. The article concludes with a comparison of missing data methods using a small sample from the National Longitudinal Survey of Youth 1997 to illustrate the detrimental effects of traditional approaches to handling missing data, and demonstrate the benefits of multiple imputation (MI) as an efficient modern missing data technique.