Item response rates frequently serve as indicators of data quality and potential nonresponse bias. However, key variables from surveys, such as total household income or net worth, are often composite variables constructed from several underlying components. Because such composite variables do not have clearly identifiable response rates, inference on the data quality of these key measures is more difficult. This article proposes three new methods for aggregating data on response rates across questions to create a measure of item response for composite variables. To compare the three methods and illustrate how they can be used (both individually and collectively) to investigate data quality, I analyze item response for net worth in the Survey of Income and Program Participation (SIPP) and the Survey of Consumer Finances (SCF). These new measures provide detailed information about net worth estimates that would be difficult to assess without an item response aggregation method. Overall, these new item response rate methods provide a new way of describing data quality for key measures in surveys and for analyzing changes in data quality over time.