Abstract
Numerous methodologies exist to analyze longitudinal fluency data, including individual growth curve analysis in both observed and latent frameworks, cross-lagged regression to assess interrelations between variables, and multilevel frameworks that consider time as nested within individual. This chapter discusses latent change score (LCS) analysis of oral reading fluency (ORF) and reading comprehension data from a longitudinal sample of 16,000 students from first to fourth grade and compares to linear and nonlinear growth curve analysis. Results highlight the benefits of LCS models compared to traditional linear and nonlinear latent growth models, as well as implications for modeling change with correlated skills.
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Petscher, Y., Koon, S., Herrera, S. (2016). Using Latent Change Score Analysis to Model Co-Development in Fluency Skills. In: Cummings, K., Petscher, Y. (eds) The Fluency Construct. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2803-3_12
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