Comparing Single-Level and Multi-Level Analyses with Complex Sampling Weights.
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| Title: | Comparing Single-Level and Multi-Level Analyses with Complex Sampling Weights. |
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| Authors: | Shen, Ting1 (AUTHOR) tingshen@mst.edu |
| Source: | Journal of Research on Educational Effectiveness. Apr-Jun2026, Vol. 19 Issue 2, p516-540. 25p. |
| Subject Terms: | *Education statistics, Multilevel models, Statistical weighting, Statistical models, Simulation methods & models |
| Company/Entity: | Trends in International Mathematics & Science Study |
| Abstract: | Large-scale assessment (LSA) data are increasingly utilized to inform education policy and practice worldwide. Because LSA adopts complex sampling designs with unequal probabilities of selection, the use of sampling weights is essential. However, research evidence is scarce, and uncertainty remains regarding how sampling weights should be used, and which approaches are preferrable. This study draws on data from the Early Childhood Longitudinal Study – Kindergarten, the Program for International Student Assessment, and the Trends in International Mathematics and Science Study to compare seven different approaches involving weighting, scaling, and modeling in the context of LSA data. Simulation results indicate that multi-level models without sampling weights have the smallest root mean square error (RMSE) whereas single-level models with overall sampling weights have the largest RMSE. Empirical findings suggest that, overall, weighting, scaling, and modeling choice do not substantially affect statistical significance. Both simulation and empirical analyses reveal that three approaches—multi-level models with school sampling weights, and multi-level models with two-level sampling weights with either size scaling or effective scaling—perform similarly. Discussion and practical recommendations are provided. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Research on Educational Effectiveness is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Abstract: | Large-scale assessment (LSA) data are increasingly utilized to inform education policy and practice worldwide. Because LSA adopts complex sampling designs with unequal probabilities of selection, the use of sampling weights is essential. However, research evidence is scarce, and uncertainty remains regarding how sampling weights should be used, and which approaches are preferrable. This study draws on data from the Early Childhood Longitudinal Study – Kindergarten, the Program for International Student Assessment, and the Trends in International Mathematics and Science Study to compare seven different approaches involving weighting, scaling, and modeling in the context of LSA data. Simulation results indicate that multi-level models without sampling weights have the smallest root mean square error (RMSE) whereas single-level models with overall sampling weights have the largest RMSE. Empirical findings suggest that, overall, weighting, scaling, and modeling choice do not substantially affect statistical significance. Both simulation and empirical analyses reveal that three approaches—multi-level models with school sampling weights, and multi-level models with two-level sampling weights with either size scaling or effective scaling—perform similarly. Discussion and practical recommendations are provided. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19345747 |
| DOI: | 10.1080/19345747.2025.2504439 |
