Some contributions to maximum likelihood factor analysis

By Karl G. Jöreskog. 1967

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Citations in the corpus

On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis (2007, methodology): “In general covariance structure analysis, Equation 2 is used directly for minimization, but specialized computational methods have been developed to minimize Equation 2 in the FA model (e.g., Jöreskog, 1967, 1977).”
On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, background): “Structural equation modeling (SEM), almost from its very beginning, has been divided between covariancebased SEM (CB-SEM; Jöreskog, 1967; Jöreskog, 1969, 1971) and composite-based SEM (Tenenhaus, Esposito Vinzi, Chatelin, & Lauro, 2005; Wold, 1974, 1982).”
BEST PRACTICES IN STRUCTURAL EQUATION MODELING (2007, methodology): “While data-model fit for such models was initially conceived as a formal statistical test of the discrepancy between the true and model-implied covariance matrices (a chi-square test with df = u–t; Joreskog, 1966, 1967 ), such a test now is often viewed as overly strict given its power to detect ev”
Structural Equation Modeling For Travel Behavior Research (2001, methodology): “Modern SEM was originally known as the JKW (Jöreskog-Keesling-Wiley) model.”
(Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling (2003, methodology): “Today, programs like AMOS 5, LISREL 8.5 or EQS 5.7 provide a graphical user-interface allowing the user to configure path diagrams, calculate model fit, and estimate parameters with a few mouse clicks.”
Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis (2010, methodology): “, Jöreskog, 1969; Rubin & Thayer, 1982) so that the rotational indeterminacy inherent in exploratory factor analysis (Jöreskog, 1967) can be eliminated.”
Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size (2012, methodology): “Despite the existing formal mathematical framework and the available articles and textbooks elaborating on the properties of PAF and MLFA [1,5,10,21,28,37,40,46], there is little practical evidence regarding which factor analysis method should be preferred for different types of factor patterns and ”
Bias in Factor Score Regression and a Simple Solution (2011, background): “Jöreskog (1967) reported that 9 out of 11 classical data sets possessed improper solutions, and Anderson and Gerbing (1984) reported that with correct models, their simulation study found that 24.9% of replications had improper solutions.”
Extending psychometric network analysis: Empirical evidence against g in favor of mutualism? (2019, methodology): “In addition, confirmatory techniques were developed (Jöreskog, 1967; Jöreskog & Sörbom, 1989), which provided the opportunity to compare factor models statistically and so also the means to distinguish between competing factor theoretical explanations of the correlation structures.”