Bootstrapping within the Multilevel/Hierarchical Linear Modeling Framework: A Primer for Use with SAS and SPLUS
Year: 2004
Type: article
Abstract: Nested data structure obtained from a cluster sampling design often calls for hierarchical linear modeling (HLM) analysis. Such data structure warrants some special considerations when the bootstrap technique is applied. This paper presents some discussions and examples for applying the bootstrap method within the framework of hierarchical linear modeling. A two-level dataset (about 900 students nested under 20 schools) extracted from the High School and Beyond (HSB) was used for illustration. Bootstrap resampling was implemented in both SAS and S-PLUS, and a hierarchical linear model with one Level-1 predictor (student SES), and one Level-2 predictor (type of schools, Catholic or public) was applied. n quantitative research in education and psychology, over-reliance on statistical significance testing has been called into question. Several issues have been raised concerning the use of statistical significance testing in research practice including sample size, the meaningfulness of the traditional null hypothesis, and questions involving the validity of theoretical assumptions underlying parametric statistical inferences (e.g., Carver, 1978; Shaver, 1993; Thompson, 1993). As a result of these and other concerns, researchers are increasingly turning to empirically-grounded resampling procedures in quantitative analyses. Applauded as one of the newest breakthroughs in statistics (Kotz & Johnson, 1992), the bootstrap is
Source: General Linear Model Journal
Authors: J. Kyle Roberts, Xitao Fan
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2155480121