The advantages of applying confirmatory factor analysis (CFA) to multitrait-multimethod (MTMM) data are widely recognized. However, because CFA as traditionally applied to MTMM data incorporates single indicators of each scale (i.e., each trait/method combination), important weaknesses are the failure to: (1) correct appropriately for measurement error in scale scores; (2) separate error due to low internal consistency from uniqueness due to weak trait and/or method effects; (3) test whether items or subscales accurately reflect the intended factor structure; and (4) test for correlated uniquenesses. However, when the analysis begins with multiple indicators of each scale (i.e., items or subscales), second-order factor analysis can be used to address each of these problems. In this approach first-order factors defined by multiple items or subscales are posited for each scale, and the method and trait factors are posited as second-order factors. This paper illustrates models that incorporate multiple indicators of each scale. The advantages of their application are discussed. A three-page list of references and the description of the models, mentioned in the text, supplement the paper. (Author/JAZ)