Longitudinal data analysis has received widespread interest throughout educational, behavioral, and social science research, with latent growth curve modeling currently being one of the most popular methods of analysis. Despite the popularity of latent growth curve modeling, limited attention has been directed toward understanding the issues of…

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 used the Stanford education data archive (SEDA) data to examine the heterogeneity among urban school districts in the United States. The SEDA 2.1 includes data sets on students' mathematics (Math) and English language arts (ELA) achievement from 2008 to 2014 at the district level. Growth mixture modeling was used to uncover the underlying…

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…

Purpose: Reading fluency establishes the basis for the strong literacy skills needed for academic success. We aim to trace how reading fluency develops from childhood to adulthood and identify factors that influence this development. Method: In this study, 200 families were followed. All participating children (N = 200, 47% female) were ethnic…

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…

This dissertation informed researchers about the performance of different level-specific and target-specific model fit indices in Multilevel Latent Growth Model (MLGM) using unbalanced design and different trajectories. As the use of MLGMs is a relatively new field, this study helped further the field by informing researchers interested in using…

The purpose of this study was to examine the technical features of slopes produced from the curriculum-based measurement in writing (CBM-W) word dictation task. Seventy-nine elementary students in the U.S. Midwest with intensive learning needs responded to weekly word dictation probes across 20 weeks; responses were scored for correct letter…

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…

Growth mixture models (GMMs) are applied to intervention studies with repeated measures to explore heterogeneity in the intervention effect. However, traditional GMMs are known to be difficult to estimate, especially at sample sizes common in single-center interventions. Common strategies to coerce GMMs to converge involve post-hoc adjustments to…

Education experiments frequently assign students to treatment or control conditions within schools. Longitudinal components added in these studies (e.g., students followed over time) allow researchers to assess treatment effects in average rates of change (e.g., linear or quadratic). We provide methods for a priori power analysis in three-level…

States can and should continue to measure student growth in 2021. Growth data will be crucial to understanding how school closures due to COVID-19 have affected student progress and what supports they will need to get back on track. Education leaders will also need growth data to ensure that any recovery efforts are equitable as well as effective…

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,…

Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth…