Best Practices in Using Large, Complex Samples: The Importance of Using Appropriate Weights and Design Effect Compensation
DOI
https://doi.org/10.7275/2kyg-m659
Abstract
Large surveys often use probability sampling in order to obtain representative samples, and these data sets are valuable tools for researchers in all areas of science. Yet many researchers are not formally prepared to appropriately utilize these resources. Indeed, users of one popular dataset were generally found not to have modeled the analyses to take account of the complex sample (Johnson & Elliott, 1998) even when publishing in highly-regarded journals. It is well known that failure to appropriately model the complex sample can substantially bias the results of the analysis. Examples presented in this paper highlight the risk of error of inference and mis-estimation of parameters from failure to analyze these data sets appropriately. Accessed 5,573 times on https://pareonline.net from September 19, 2011 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Osborne, Jason
(2019)
"Best Practices in Using Large, Complex Samples: The Importance of Using Appropriate Weights and Design Effect Compensation,"
Practical Assessment, Research, and Evaluation: Vol. 16, Article 12.
DOI: https://doi.org/10.7275/2kyg-m659
Available at:
https://scholarworks.umass.edu/pare/vol16/iss1/12