A Joint Modeling Approach to Studying English Language Proficiency Development and Time-to-Reclassification

The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in monitoring achievement of English learners. Yet, little is known about academic English language development at the domain-level (listening, speaking, reading, and writing), or how English language development is associated with time-to-reclassification as an English proficient student. Although the substantive findings surrounding English proficiency and reclassification are of great import, the main focus of this dissertation was methodological: the exploration and testing of joint modeling methods for studying both issues. The first joint model studied was a multilevel, multivariate random effects model that estimated the student-specific and school-specific association between different domains of English language proficiency. The second model was a multilevel shared random effects model that estimated English proficiency development and time-to-reclassification simultaneously and treated the student-specific random effects as latent covariates in the time-to-reclassification model. These joint modeling approaches were illustrated using annual English language proficiency test scores and time-to-reclassification data from a large Arizona school district. Results from the multivariate random effects model revealed correlations greater than .5 among the reading, writing and oral English proficiency random intercepts. The analysis of English proficiency development illustrated that some students had attained proficiency in particular domains at different times, and that some students had not attained proficiency in a particular domain even when their total English proficiency score met the state benchmark for proficiency. These more specific domain score analyses highlight important differences in language development that may have implications for instruction and policy. The shared random effects model resulted in predictions of time-to-reclassification that were 97% accurate compared to 80\% accuracy from a conventional discrete-time hazard model. The time-to-reclassification analysis suggested that use of information about English language development is critical for making accurate predictions of the time a student will reclassify in this Arizona school district