Categorical growth models describe examinee growth in terms of performance-level category transitions, which implies that some percentage of examinees will be misclassified. This paper introduces a new procedure for estimating the classification accuracy of categorical growth models, based on Rudner's classification accuracy index for item…

Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation, though this may be a dubious assumption. Alternative model specifications have been proposed to reduce…

Growth mixture models (GMMs) are a popular method to uncover heterogeneity in growth trajectories. Harnessing the power of GMMs in applications is difficult given the prevalence of nonconvergence when fitting GMMs to empirical data. GMMs are rooted in the random effect tradition and nonconvergence often leads researchers to modify their intended…

Modeling within-person change over time and between-person differences in change over time is a primary goal in prevention science. When modeling change in an observed score over time with multilevel or structural equation modeling approaches, each observed score counts toward the estimation of model parameters equally. However, observed scores…

We evaluate the feasibility of estimating test-score growth for schools and districts with a gap year in test data. Our research design uses a simulated gap year in testing when a true test gap did not occur, which facilitates comparisons of district- and school-level growth estimates with and without a gap year. We find that growth estimates…

This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…

We evaluate the feasibility of estimating test-score growth with a gap year in testing data, informing the scenario when state testing resumes after the 2020 COVID-19-induced test stoppage. Our research design is to simulate a gap year in testing using pre-COVID-19 data--when a true test gap did not occur--which facilitates comparisons of…

Monitoring the progress of student learning is an important part of teachers' data-based decision making. One such tool that can equip teachers with information about students' learning progress throughout the school year and thus facilitate monitoring and instructional decision making is learning progress assessments. In practical contexts and…

Purpose: The aim of this study was to describe and explain changes in severity of speech sound disorder (SSD) and token-to-token inconsistency in children with high levels of inconsistency. Method: Thirty-nine children (aged 4;6-7;11 [years;months]) with SSDs and high levels of token-to-token inconsistency were assessed every 6 months for 2 years…