NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 3 results Save | Export
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Liu, Haiyan; Zhang, Zhiyong; Grimm, Kevin J. – Grantee Submission, 2016
Growth curve modeling provides a general framework for analyzing longitudinal data from social, behavioral, and educational sciences. Bayesian methods have been used to estimate growth curve models, in which priors need to be specified for unknown parameters. For the covariance parameter matrix, the inverse Wishart prior is most commonly used due…
Descriptors: Bayesian Statistics, Computation, Statistical Analysis, Growth Models
Peer reviewed Peer reviewed
Direct linkDirect link
Nelson, Peter M.; Van Norman, Ethan R.; Klingbeil, Dave A.; Parker, David C. – Psychology in the Schools, 2017
Although extensive research exists on the use of curriculum-based measures for progress monitoring, little is known about using computer adaptive tests (CATs) for progress-monitoring purposes. The purpose of this study was to evaluate the impact of the frequency of data collection on individual and group growth estimates using a CAT. Data were…
Descriptors: Progress Monitoring, Computer Assisted Testing, Data Collection, Scheduling
Peer reviewed Peer reviewed
Direct linkDirect link
Allen, Jeff – Applied Measurement in Education, 2017
Using a sample of schools testing annually in grades 9-11 with a vertically linked series of assessments, a latent growth curve model is used to model test scores with student intercepts and slopes nested within school. Missed assessments can occur because of student mobility, student dropout, absenteeism, and other reasons. Missing data…
Descriptors: Achievement Gains, Academic Achievement, Growth Models, Scores