This chapter provides further data considerations in developing and testing multilevel models. In many ways, the quality of the data that researchers bring to bear on a particular research problem is the key determinant of the credibility of their results. The quality of data will also inform the extent to which the results will contribute to knowledge building. The first part of the chapter discusses challenges associated with missing data. It introduces how to incorporate multiple imputed data sets into a master data set that can be analyzed using SPSS Mixed to provide a pooled set of estimates. Next, the chapter addresses sample weights, a limitation in SPSS Mixed, offering different weighting schemes that can help guide the use of sample weights at different levels in the analysis using software that permits the incorporation of weights at multiple hierarchical levels.