Latent variable models assess the common variance across multiple indicators of a specific construct and are often used when measurement error may bias parameter estimates. However, care must be taken when interpreting the meaning of the latent construct when using item indicators that come from different measurement domains (e.g., self-report and biochemical indicators of smoking). Utilizing simulated data, we demonstrate that even though a model may be considered to have a "good fit" based on conventional criteria, data interpretation may be misleading or erroneous if precautions are not taken when specifying residual covariances. These findings have important implications for health-related research. Whenever different kinds of data are used to define latent variables in a health domain, exactly what items are used, and what biases may be present can affect, sometimes dramatically, (a) the definition of the latent variables and (b) the effects of the latent variables on other variables of interest.