Project Activities
The researchers planned to conduct secondary analyses of data obtained through two large longitudinal studies previously conducted by the principal investigator (PI), Project 2—Oracy/Literacy Development of Spanish-Speaking Children and Optimizing Educational Outcomes for English Language Learners. Analyses will explore the factors affecting the identification and classification of reading and language disabilities among ELL students in kindergarten through grade 2.
Structured Abstract
Setting
The current study will combine data from two previous projects conducted by the PI. Taken together, data from these projects were collected from 40 elementary schools across 12 districts in Texas and California.
Sample
The extant datasets include data from 3,000 ELL students in kindergarten through grade 2 who received instruction through either Structured English Immersion or other bilingual programs (i.e., those that use Spanish in instruction).
Intervention
Due to the nature of this project, there is no intervention.
Research design and methods
This study involves secondary analyses of six time points of longitudinal data obtained from two previous projects. Across both projects, students were assessed at the beginning and end of each academic year in both English and Spanish on a battery of reading and language measures designed to be comparable in the two languages. All measures were collected in each grade and at each time point, with a few exceptions (e.g., in kindergarten, measures assessed pre-literacy skills, as opposed to literacy skills; intellectual functioning was only measured in the fall of each year). Secondary analysis of these data will be used to compare and contrast different disability classification and identification methods (IQ-achievement discrepancy, low achievement, and pattern of growth) to determine their validity for ELL students with reading disabilities, language disabilities, and co-morbid reading and language disabilities.
Control condition
Due to the nature of the research design, there is no control condition.
Key measures
Measures included standardized and researcher-developed assessments and narrative language measures designed to assess oral language proficiency, reading proficiency, and literacy-related skills in each language. Oral proficiency was measured using the English and Spanish language versions of the Woodcock Language Proficiency Battery-Revised (WLPB-R), including five subtests—Memory for Sentences, Picture Vocabulary, Oral Vocabulary, Listening Comprehension, and Verbal Analogies—and an overall composite Oral Language Proficiency score. Mercer Mayer's wordless picture book frog stories were used to elicit narrative language samples in English and Spanish to derive mean length of utterance in words, number of different words, number of total words, words per minute, total utterances, the subordination index (measure of syntactic complexity), and a measure of narrative structure. For English and Spanish, reading proficiency was measured using three subtests from the WLPB-R—Letter-Word Identification, Word Attack, and Passage Comprehension. In addition, timed decoding (fluency) was measured using the Test of Word Reading Efficiency (TOWRE) in English. A parallel measure to the TOWRE, previously developed by the PI, was used to measure timed decoding (fluency) skills in Spanish. Literacy-related skills included alphabet and letter sound knowledge, phonological awareness, rapid automatized naming, and verbal short-term memory. To measure phonological awareness in English, the Comprehensive Test of Phonological Processes (CTOPP) was used. All other literacy-related skills were measured by assessments developed by the PI. The Test of Phonological Processes in Spanish (TOPPS) was used to measure rapid automatized naming and verbal short-term memory in English and Spanish. The TOPPS was also used to measure phonological awareness in Spanish. A measure developed by the PI was used to measure alphabet and letter-sound knowledge in English and Spanish. Lastly, the Raven's Progressive Color Matrices, a nonverbal assessment of logical reasoning, was used to measure intellectual functioning.
Data analytic strategy
The researchers will use a variety of data analysis strategies, including regression and generalized growth mixture models, for the identification and classification of disabilities (based on the IQ-achievement discrepancy, low achievement, and growth pattern methods). They will also use several different strategies for the validation of those classifications, including profile analysis, discriminant analysis, cross-sectional contingency tables for examining concordance and co-morbidity, contingency tables with a temporal dimension for assessing classification stability, and individual growth mode.
People and institutions involved
IES program contact(s)
Products and publications
ERIC Citations: Find available ERIC citations here.
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