{'fraction_non_alphanumeric': 0.023293172690763052, 'fraction_numerical': 0.00642570281124498, 'mean_word_length': 5.455958549222798, '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}}
Unobserved Potential: Latent Variables and Music Education Research


 This article describes several latent variable approaches that can support rigorous quantitative inquiry in music education. I provide a definition of latent variables, list several advantages associated with their use for the measurement of constructs, and review three types of latent variables featuring utility for music education scholars: factor models, item response models, and latent class (mixture) models. Each type of model is illustrated with exemplar studies from music education literature. In addition, I report the results of a systematic analysis of latent variables’ use in published music education research. Over the past decade, latent variables were used in only 14.29% of the quantitative research reports in two prominent journals: the Bulletin of the Council for Research in Music Education and the Journal of Research in Music Education. The majority of these latent variable applications (80.56%) were factor models; however, there are myriad additional possibilities. While not appropriate for every study, latent variable models have as-yet unrealized potential to improve the richness and rigor in nearly all areas of music education research.