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.  In this paper, I propose three new methods for aggregating data on response rates across questions to create a measure of item response for composite variables.  To show how these methods can be used to investigate data quality, I analyze item response for net worth in the Survey of Income and Program Participation and the Survey of Consumer Finances.  All three of the new measures show that item response rates went up in the SIPP after the redesign in the 2014 Panel, but are still lower than item response rates in the SCF.  This comparison between surveys would be difficult without a method for aggregating item response rates.  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.