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Rating 6.6/10 Sparseness can take a toll on parameter estimation Several simulation studies (Collins et al, 1993; Collins & Tracy, 1997; Collins & Wugalter, 1992) have demonstrated that parameter estimation in LTA remains unbiased even in very sparse data tables However, the standard errors of certain parameters can become unacceptably large This is particularly true for the parameters, as these transition probabilities are conditioned on the latent class membership and latent status membership at Time 1, and thus often based on considerably smaller Ns than the other parameters In addition, identi cation problems become more frequent as sparseness increases This means that researchers who are faced with small samples currently are unlikely to be able to make use of LCA and LTA in their current forms The problems with small samples are due to a lack of information Naturally, small samples contain less information than do large samples. pdf merge software free download windows 7: PDFsam: Split and merge PDF files. Free and open source pdf combine software freeware PDF Split and Merge download | SourceForge.net
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