American Statistical Association New York City Metropolitan Area Chapter Memorial Sloan-Kettering Cancer Center Biostatistics Seminar

Latent variable models have long been utilized by behavioral scientists to summarize constructs that are represented by multiple measured variables or are difficult to measure, such as health practices and psychiatric syndromes. They have been regarded as particularly useful when variables that can be measured are highly imperfect surrogates for the construct of inferential interest, but they are also criticized as being overly abstract, weakly estimable, and sensitive to unverifiable modeling assumptions. First, my talk reviews methods I have developed for assessing modeling assumptions and delineating what are the targets of parameter estimation in the case of maximum likelihood fitting, allowing for a mis-specified model. Then, it describes new work to counterbalance standard latent variable modeling assumptions focused on internal validity of measurement with alternative assumptions say, focused on external or concurrent validation. Small sample performance properties of the methodology are evaluated. The methods will be illustrated using data on post traumatic stress disorder in a population-based sample and adverse health in older adults. It is hoped that the findings will lead to improved usage of latent variable models in scientific investigations.