{'fraction_non_alphanumeric': 0.01540436456996149, 'fraction_numerical': 0.0012836970474967907, 'mean_word_length': 5.290322580645161, '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}}
A Simulation of Factor Analytic Reliability Varying Sample Size and Number of Variables

An examination of four factor analytic models employing random sampling experiments is undertaken using a methodology and hypothetical population factor structure first employed by Browne (2). The factor models are each explored under four separate conditions, varying sample size and number of variables. Under these limited conditions, it is argued that there are no practical differences among the factor models considered with respect to sampling error in the absence of a Heywood variable. However, with respect to the ability of each model to capture, early and at convergence, the number of factors in the population, the alpha factor model is shown to have the greatest reliability.