Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.

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A treadmill with vibration isolation and stabilization designed for the International Space Station (ISS) was evaluated during Shuttle mission STS-81. Three crew members ran and walked on the device, which floats freely in zero gravity. For the majority of the more than 2 hours of locomotion studied, the treadmill showed peak to peak linear and angular displacements of less than 2.5 cm and 2.5 degrees, respectively. Vibration transmitted to the vehicle was within the microgravity allocation limits that are defined for the ISS. Refinements to the treadmill and harness system are discussed. This approach to treadmill design offers the possibility of generating 1G-like loads on the lower extremities while preserving the microgravity environment of the ISS for structural safety and vibration free experimental conditions.

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This book is 323 pages long, consisting of 8 chapters, with a bibliography and index. The first chapters introduce FORTRAN 95 and provide some discussion of scientific software development. Chapters 5 through 8 primarily focus on either developing a user interface, or connecting to other software that provides a user interface. Thus, the primary focus is in making routines available to others, not on writing them. Journal of Statistical Software