{'fraction_non_alphanumeric': 0.017335766423357664, 'fraction_numerical': 0.0, 'mean_word_length': 5.452941176470588, '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}}
Estimating and Comparing Growth Using Longitudinal Interim Achievement Data with Seasonal Trends

Abstract Sources of longitudinal achievement data are increasing thanks partially to the expansion of available interim assessments. These tests are often used to monitor the progress of students, classrooms, and schools within and across school years. Yet, few statistical models equipped to approximate the distinctly seasonal patterns in the data exist, nor is there much guidance on how to choose among models. In this study, we present a general statistical model motivated by the seasonal character of interim achievement data and conduct analyses aimed at reducing barriers to the generation of empirical benchmarks for repeated measures achievement data. The model is designed to combine features from traditional polynomial models that estimate year-to-year growth but ignore within-year gains and losses with those from piecewise models, which directly estimate within-year gains/losses but do not include terms for year-to-year growth. Implications for research and policy are discussed.