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maximum likelihood estimation procedure, x^2 and its degrees of freedom and significance level, allows the program to estimate just the variance of teh disturbance, exogenous variables that are dichotomous (e.g,. gender), adjusted goodness of fit index (agfi), number of degrees of freedom = positive, interpreted as a pearson chi-square statistic (x^2), 'too many ' correlation residuals .10, 1= perfect fit, sample size, same as multiple regression, maximum likelihood (ml), no degrees of freedom, bentler comparative fit index (cfi), related to disturbances, ml allows model-implied correlations betweeen endogenous variables andthe distrubances of subsequent variables the endogenous variables are specified to affect, depending on model, 0= poor fit, 0, what is a good fit?, no hard guidelines... the more...the worse fit of specific portions of the model, bentler-bonett non-normed fit index (nnfi), model = 0, types of path models, overall proportion of explained variance, generally similar to multiple regression assumptions, disturbances are teypiocally represenedt in teh syntax of model-fitting programs as latent variables that have a singel indicator, teh observe endogenous variable associated with it., interpretation, ????, lower bound is zero, but no theoretical upper bound, srmr, ml estimaets of the variances and covaraniances of the observed exogenous variables are simply the observed values, researchers typically report multiple fit indexes, small sample size, model= may be positive, e.g., gfi. nfi, cfi, x^2, very sensitive to sample size, agfi, nnfi, options, squared multipel correlation in that it includes a built-in adjustment for model complexity, .10, a test of significance of the difference in fit between that model and a just identified version of it, popular, nonsignificant, tend to fit the same data better than do simipler ones, e.g., nnfi, differences between observed and model- implied correlations, most model fitting programs require the specification that the residual path coefficient of distrubances is set to 1.0, some parts of the model can poorly fit the data, exogenous varialbes are independent of disturbances, joreskog-sorbom goodness of fit index (gfi), appropriate for nonrecursive models, limitations of all fit indexes, correlation residuals, overidentified models with almost perfect fit, x^2 df ratio 3, just identified, values of fit indexes indicate only the overall or average fit of a model, of multiple indexes, bentler-bonett normed fit index (nfi), value of the ml fitting function, gfi, nfi, cfi, a shrinkage -corrected squared multiple correlation in that in includes a built-in adjustment for model complexity, for model-fitting programs, .90, 1, good values of fit index do not indicate the predictive power of the model is also high, .90 , values are not interpretable in a standardized way, sample size, x^2 may be significant even though differences between observed and model-implied covarinaces are slight, poor model fit, standardized root mean squared residual (srmr), generalized likelihood ratio (g^2), fit indexes do not indicate if results are theoretically meaningful, ml assumes multivariate normality of endogenous variables and exogenous variabls that are continous