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The Concept Map you are trying to access has information related to:
factor analysis, maximum likelihood, full component model, random error (unrelaiblity), indicators specified to measure a common underlying factor all have relatively high loadings on that factor, testing apriori models, extraction, by the factors, each indicator is speciifed tomeasure (load on) a single latent variable, factor analysis, uncorrelated (orthogonal), cfa, effects due to measurement method, good loadings, need to evaluate and add to the map?, centroid, correlated (oblique), correlations between factors are not excessively high (e.g., .85, 1) factors range from one up to the number of observd variables, types, typically pearson correlations between the observed variables and the factors, 2) observed variables are allowed to correlate w every factor, multiple factors, spss command menu, principal axis, systematic variance (nonrandom), extraction methods, efa, 3) factor solutions require rotation, the common factor model, principal componets, represents unique variance, determing of factors