{'fraction_non_alphanumeric': 0.047283702213279676, 'fraction_numerical': 0.023138832997987926, 'mean_word_length': 5.1419753086419755, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Comparison of Methods for Estimating and Testing Latent Variable Interactions

Structural equation modeling methods for estimating and testing hypotheses about an interaction between continuous variables were investigated. The methods were (a) Bollen's (1996) 2-stage least squares (TSLS) method, Ping's (1996) 2-step maximum likelihood (ML) method, and Jaccard and Wan's (1995) ML method for the Kenny-Judd model (Kenny & Judd, 1984); (b) a 2-step ML procedure and ML estimation of the Jöreskog-Yang model (Jöreskog & Yang 1996); and (c) ML estimation of a revised Jöreskog-Yang model. The TSLS procedure exhibited more bias and lower power than the other methods. Under ML estimation of the Jöreskog-Yang model, Type I error rates were not well controlled when robust standard errors were used. Among the remaining procedures, the Jaccard-Wan procedure and ML estimation of the revised Jöreskog-Yang procedure were most effective, with the latter having some small advantages over the former.