Traditional surveys are excellent instruments for establishing the correlational relationship between two constructs. However, they are unable to identify reasons why such correlations exist. Computer- Adaptive Surveys (CAS) are multi-dimensional instruments where questions asked of respondents depend on the previous questions asked. Their principal advantage is they allow the survey developer to input a large number of potential causes. Respondents then roll down through the causes to identify the one or few significant causes impacting a correlation. This study compared a café satisfaction CAS to a traditional survey of the same item bank to test whether CAS performs its intended task better than a traditional survey. Our study demonstrates that when one is trying to find root cause, CAS achieves a higher response rate, requires fewer items for respondents to answer, has better item discrimination, and has a higher agreement among respondents for each item.