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data preperation and screening, a model is analyzed within a single sampe using a special method that accommodates cases w missing observatinos, computation of composite variables, managage raw data issues, sx^2 = 1,2000, sy^2 = .12, spss, missing data, rescaling a variable changes its mean and variance but not its correlation w other variables, large = when the ratio of the largest to the smallest variance is greater thna 10, form of the input data, matrix summary of the data, create the matrix, estimation method is used that does not assume that the data are normally distributred, non-nomral data are analyzed with a more standar etimation method, but test stats are cacculated that correct for non-normailty, relative variance, are special circumstances present?, form of the input dat, set of estimates can head to worse not better fit, raw data file, lilke those in sem, sem data preperation & screening, variables w extremely low or high variances can be rescaled by multipying their scores by aconstant, if variances beetween variables are very different in magnitude, determine form of data input