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IES Grant

Title: Identification of Reading and Language Disabilities in Spanish-Speaking English Learners
Center: NCSER Year: 2016
Principal Investigator: Francis, David Awardee: University of Houston
Program: Special Education Policy, Finance, and Systems      [Program Details]
Award Period: 2 years (8/1/2016-7/30/2018) Award Amount: $699,743
Goal: Exploration Award Number: R324A160258

Purpose: The purpose of this study is to explore factors related to the identification and classification of reading and language disabilities among Spanish-speaking English language learners (ELLs) in an effort to provide school-based professionals with clearer criteria for identifying learning disabilities in these students. ELL students are the fastest growing subgroup of students in U.S. public schools and are disproportionately at risk for poor academic outcomes. Given the risk for poor outcomes, the identification of disabilities among this group of students is challenging. The goal of this study is to compare and contrast different disability identification and classification methods (i.e., IQ-achievement discrepancy, low achievement, and growth patterns) and examine student and contextual factors related to the consistency and inconsistency in identifications within and across classifications over time. The results of this study are expected to provide an empirical basis for a theoretically grounded framework for the identification and classification of learning disabilities in students whose first language is not English.

Project Activities: The researchers will conduct secondary analyses of data obtained through two large longitudinal studies previously conducted by the Principal Investigator, 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.

Products: The products of this project will include preliminary evidence of the validity of different methods to identify and classify learning disabilities among ELL students, as well as peer-reviewed publications and presentations.

Structured Abstract

Setting: The current study will combine data from two previous projects conducted by the Principal Investigator. 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: 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 (i.e., 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 (i.e., 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 Principal Investigator, 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 Principal Investigator. 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 Principal Investigator 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 models.