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Yu, Chong Ho; Douglas, Samantha; Lee, Anna; An, Min – Practical Assessment, Research & Evaluation, 2016
This paper aims to illustrate how data visualization could be utilized to identify errors prior to modeling, using an example with multi-dimensional item response theory (MIRT). MIRT combines item response theory and factor analysis to identify a psychometric model that investigates two or more latent traits. While it may seem convenient to…
Descriptors: Visualization, Item Response Theory, Sample Size, Correlation
Beavers, Amy S.; Lounsbury, John W.; Richards, Jennifer K.; Huck, Schuyler W.; Skolits, Gary J.; Esquivel, Shelley L. – Practical Assessment, Research & Evaluation, 2013
The uses and methodology of factor analysis are widely debated and discussed, especially the issues of rotational use, methods of confirmatory factor analysis, and adequate sample size. The variety of perspectives and often conflicting opinions can lead to confusion among researchers about best practices for using factor analysis. The focus of the…
Descriptors: Factor Analysis, Educational Research, Best Practices, Sample Size
Peer reviewedLathrop, Richard G.; Williams, Janice E. – Educational and Psychological Measurement, 1989
A Monte Carlo study determined the Inverse Scree Test's shape with various numbers of true groups and under different conditions of distribution shape and sample size. Six simulated distributions of 3,000 subjects each and 1 with 1,500 were created. Findings suggest relative distribution independence, number independence, and modest…
Descriptors: Cluster Analysis, Computer Simulation, Factor Analysis, Graphs


