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ERIC Number: EJ1045885
Record Type: Journal
Publication Date: 2014-Nov
Pages: 9
Abstractor: As Provided
Reference Count: 34
ISBN: N/A
ISSN: EISSN-1531-7714
Analyzing Group Level Effects with Clustered Data Using Taylor Series Linearization
Huang, Francis L.
Practical Assessment, Research & Evaluation, v19 n13 Nov 2014
Clustered data (e.g., students within schools) are often analyzed in educational research where data are naturally nested. As a consequence, multilevel modeling (MLM) has commonly been used to study the contextual or group-level (e.g., school) effects on individual outcomes. The current study investigates the use of an alternative procedure to MLM: regression using Taylor series linearization (TSL) variance estimation. Despite the name, regressions using TSL are straightforward to conduct, can yield consistent and unbiased estimates and standard errors (given the appropriate conditions), and can be performed using a variety of commercially-and freely-available statistical software. I analyze a subsample of the High School and Beyond (HSB) dataset using MLM, regression using TSL, and ordinary least squares regression and compare results. In addition, 12,000 random samples are drawn from the HSB dataset of varying level-one and level-two sample sizes in order to compute biases in standard errors based on the different conditions. Sample R and SAS syntax showing how to run regressions using TSL are provided.
Dr. Lawrence M. Rudner. e-mail: editor@pareonline.net; Web site: http://pareonline.net
Publication Type: Journal Articles; Reports - Research
Education Level: High Schools; Secondary Education; Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A