This paper examines the need, feasibility, and validity of combining two biometric (urine and saliva) and three self-report (recency, peak quantity, and frequency) measures of substance use for marijuana, cocaine, opioids, and other substances (including alcohol and other drugs). Using data from 337 adults with substance dependence, we used structural equation modeling to demonstrate that these multiple measures are driven by the same underlying factor (substance use) and that no single measure is without error. We then compared the individual measures and several possible combinations of them (including one based on the latent factors and another based on the Global Appraisal of Individual Needs (GAIN) Substance Frequency Scale) to examine how well each predicted a wide range of substance-related problems. The measure with the highest construct validity in these analyses varied by drug and problem. Despite their advantages for detection, biometric measures were frequently less sensitive to the severity of other problems. Composite measures based on the substance-specific latent factors performed better than simple combinations of the biometric and psychometric measures. The Substance Frequency Scale from the GAIN performed as well as or better than all measures across problem areas, including the latent factor for any use. While the research was limited in some ways, it has important implications for the ongoing debate about the proper way to combine biometric and psychometric data.