Abstract
The random sampling paradigm, typically introduced in basic statistics courses, ensures that a sample of data is, loosely speaking, ‘representative’ of the underlying population. When the population parameters are identified, many common estimation techniques, including least squares, maximum likelihood, and instrumental variables, have desirable statistical properties under random sampling. Unfortunately, while random sampling is convenient, it can be, and often intentionally is, violated when cross-sectional data and panel data are collected. Two important deviations from random sampling are stratified sampling and cluster sampling, or perhaps a combination.
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Wooldridge, J.M. (2018). Stratified and Cluster Sampling. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2639
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2639
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Online ISBN: 978-1-349-95189-5
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