Author's Keywords: Education, HLM, multi-level modeling, tracking, race, gender, socioeconomic status, mathematics achievement	Convention All Academic Convention makes running your annual conference simple and cost effective. It is your online solution for abstract management, peer review, and scheduling for your annual meeting or convention. Submission - Custom fields, multiple submission types, tracks, audio visual, multiple upload formats, automatic conversion to pdf. Review - Peer Review, Bulk reviewer assignment, bulk emails, ranking, z-score statistics, and multiple worksheets! Reports - Many standard and custom reports generated while you wait. Print programs with participant indexes, event grids, and more! Scheduling - Flexible and convenient grid scheduling within rooms and buildings. Conflict checking and advanced filtering. Communication - Bulk email tools to help your administrators send reminders and responses. Use form letters, a message center, and much more! Management - Search tools, duplicate people management, editing tools, submission transfers, many tools to manage a variety of conference management headaches! Click here for more information.
Association: Name: American Sociological Association URL: http://www.asanet.org
Citation: URL: http://www.allacademic.com/meta/p22176_index.html Direct Link: HTML Code:

Parks, Kathrin. "A Formula for Low Achievement: Using Multi-level Models To Understand Variance in Mathematics Achievement" Paper presented at the annual meeting of the American Sociological Association, Marriott Hotel, Loews Philadelphia Hotel, Philadelphia, PA, Aug 12, 2005 <Not Available>. 2008-10-23 <http://www.allacademic.com/meta/p22176_index.html>

Parks, K. A. , 2005-08-12 "A Formula for Low Achievement: Using Multi-level Models To Understand Variance in Mathematics Achievement" Paper presented at the annual meeting of the American Sociological Association, Marriott Hotel, Loews Philadelphia Hotel, Philadelphia, PA Online <PDF>. 2008-10-23 from http://www.allacademic.com/meta/p22176_index.html

Publication Type: Conference Paper/Unpublished Manuscript Abstract: The following study utilizes data from the High School and Beyond Study in order to predict mathematics achievement using both student characteristics and school level characteristics. Utilizing Hierarchical Linear Modeling, this study extends the body of literature by exploring how race, socio-economic status, and gender, as well as the percentage of minority students in a school, whether or not the school is Catholic, the proportion of students in the academic track, and the mean socioeconomic status of the school all affect mathematics achievement. Through this methodology, it was possible to see the direct effects of both student level and school level variables on achievement, as well as the cross-level interaction of all of these variables. Findings suggest that there are discrepancies in how different types of students achieve, as well as how those students achieve in varying contexts. Many of the variables were statistically significant in their effect on mathematics achievement. Implications for this research are discussed and considerations for future research are presented.

Hierarchical Linear Modeling (HLM) Analysis of Student Growth in Secondary Education Mathematics: A School-Based Application of Growth Modeling

Effects of Social Capital on Minority and Immigrant Students' School Achievement

The Effect of High-Stakes Testing and the Over-Representation of African American Students in Louisiana Public Schools