Full Report - Center for Collaborative Education
Full Report - Center for Collaborative Education Full Report - Center for Collaborative Education
APPENDIX 4: Additional HLM Results
A discussion of our HLM analyses of MCAS scores and student–level characteristics and school environmental factors is presented in ‘In Depth: Using Hierarchical Linear Modeling (HLM) To Determine the Relative Importance of Individual and School Level Factors in LEP Students’ ELA and Math MCAS Outcomes’ (see Chapter VIII). This appendix supplements that discussion by providing additional information from existing literature and by presenting the results of the HLM analyses in more detail. Existing Literature Using HLM to analyze educational outcomes for ELL students is a common approach in existing research. The rationale for using HLM to study outcomes for ELLs is the range in approaches to ELL and LEP programs from school to school and district to district. Even within the HLM research on LEP students, there are several different approaches. The most common approach is evaluating student outcomes in the context of student-level and school-level variables, including ELL/LEP placement as a student-level covariate (e.g. Callahan, Wilkinson, & Muller, 2010; Brown et al., 2010; Wang, Niemi, & Wang, 2007). While the HLM research on ELL students is far from exhaustive, there are several factors that have emerged as significant when analyzing educational outcomes for these students. The literature using a two-level linear model including student and school level factors highlights the following significant student level variables which were also found to be significant in our study: gender (Brown, Nguyen, and Stephenson, 2010; Rumberger and Thomas, 2000; Callahan, Wilkinson, & Muller, 2010; Wang et al., 2007); language proficiency (Dawson & Williams, 2008; Wang et al., 2007, Hao & Bonstead- Bruns, 1998); and being designated as a student with disabilities (Wang et al., 2007). Attendance, a behavioral variable, was also been found to be significant (Rumberger, 1995; Rumberger & Palardy, 2005; Rumberger & Thomson, 2000). All of these factors were considered in developing the HLM models for this analysis. The literature typically treats program participation as an individual level variable and most frequently compares between two different types of ELL programs (SEI, TBE, 2-way) or two different intensities of treatment (ESL and ELL program). In this study we compared the educational attainment of LEP students in ELL programs with that of LEP students in general education. The literature also identifies several school level variables that are consistently statistically significant in two-level linear models. In particular, existing literature highlights the following significant school-level variables that were also found to be significant in our study: school size (Werblow & Duesbery, 2009; Wang et al. 2007; Rumberger & Palardy, 2005; Lee & Smith, 1999; Lee & Bryk, 1989), school poverty level (Werblow & Duesbery, 2009; Braun et al, 2006; Lee & Smith, 1999, Hao & Bonstead-Bruns, 1998), LEP density (Werblow & Duesbery, 2009), and proportion of mobile students (Rumberger & Palardy, 2005; Rumberger & Thomas, 2000). School quality variables are also mentioned in the literature and found significant in our study, such as the percentage of teachers that are highly qualified/percentage of teachers that are licensed in their subject (Munoz & Chang, 2008; Braun et al. 2006, Rumberger & Palardy, 2005; Rumberger & Thomas, 2000). In addition, we have included a school’s AYP status in Math or ELA. Results The results of the HLM analyses support the findings of the descriptive analysis presented in this report. The key findings of the HLM analyses are presented in the in-depth section; the following tables present the detailed results of the HLM analysis in each subject area (for more information on variables and model development, please see Appendix 1: Methods). In the following tables, the plus and minus signs represents positive (+) and negative (-) relationships between the variables and the student’s MCAS score. In other words, when the relationship between the independent variable and MCAS scores is positive, students’ MCAS scores tend to increase as the variable increases; when the relationship is negative, students’ MCAS scores tend to decrease as the variable decreases. For the two-category variables gender, SPED, program enrollment, and AYP, a plus sign (+) indicates that the state of the category indicated in the independent variable list (e.g. ‘Female’) is associated with higher MCAS scores, while a minus sign (-) indicates that the other variable category (e.g. ‘Male’) is associated with higher scores. Finally, the p-value indicates whether or not the direction of the relationship is Improving Educational Outcomes of English Language Learners in Schools and Programs in Boston Public Schools 137
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APPENDIX 4: Additional<br />
HLM Results
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