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…

Student growth percentiles (SGPs) are currently used by several states and school districts to provide information about individual students as well as to evaluate teachers, schools, and school districts. For SGPs to be defensible for these purposes, they should be reliable. In this study, we examine the amount of systematic and random error in…

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…

Evidence of student growth is a primary outcome of interest for educational accountability systems. When three or more years of student test data are available, questions around how students grow and what their predicted growth is can be answered. Given that test scores contain measurement error, this error should be considered in growth and…

This study compared the relative contribution of earliest assessment of preschool children's context-specific problem behaviors with subsequent observations of those behaviors for the prediction of later academic and sociobehavioral performance in first grade. Using a nationally representative sample of low-income children from the Head Start…

Growth modeling has been of interest in many assessment programs, including both highstakes and low-states tests. Growth could be modeled using different approaches. This study models growth with an Item Response Theory (IRT) based approach that utilizes item response data. It investigates the impact of complex student clustering structure where…

California's K-12 system relies on the Smarter Balanced Assessment Consortium (SBAC) English and mathematics tests to measure student academic progress and assess school and district performance. This report uses publicly available data to explore trends in student performance during the first three years this test has been in place. Key findings…

Longitudinally modeling the growth of students with significant cognitive disabilities (SWSCDs) on alternate assessments based on alternate achievement standards (AA-AAS) presents many challenges for states. The number of students in Grades 3-8 who remain in a cohort group varies over time, depending on the methods used to construct the…