Design Effects of Multilevel Estimates From National Probability Samples

This research empirically evaluates data sets from the National Center for Education Statistics (NCES) for design effects of ignoring the sampling design in weighted two-level analyses. Currently, researchers may ignore the sampling design beyond the levels that they model which might result in incorrect inferences regarding hypotheses due to biased standard error estimates; the degree of bias depends on the informativeness of any ignored stratification and clustering in the sampling design. Some multilevel software packages accommodate first-stage sampling design information for two-level models but not all. For five example public release data sets from the NCES, design effects of ignoring the sampling design in unconditional and conditional two-level models are presented for 15 dependent variables selected based on a review of published research using these five data sets. Empirical findings suggest that there are minor effects of ignoring the additional sampling design and no differences in inference would be made had the first-stage sampling design been ignored. Strategically, researchers without access to multilevel software that can accommodate the sampling might consider including stratification variables as independent variables at level 2 of their model. 1 Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA 2 American Institutes for Research, Washington, DC, USA Corresponding Author: Laura M. Stapleton, Department of Human Development and Quantitative Methodology, University of Maryland, 3304 Benjamin Building, College Park, MD 20742, USA. Email: [email protected] Sociological Methods & Research 1-28 a The Author(s) 2016 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0049124116630563 smr.sagepub.com at UNIV OF MICHIGAN on February 15, 2016 smr.sagepub.com Downloaded from