I am interested in historical references such as the examples below.
This question is with regard to inference from all surveys, but especially for establishment surveys (businesses, farms, and organizations, as noted by the International Conference[s] on Establishment Surveys, ICES).
Ken Brewer (formerly of the Australian National University, and elsewhere) has not only contributed greatly to the development of inference from surveys and the foundation of statistical inference, he also has been a recorder of this history. We often learn a great deal from history, and the cycles perhaps contained within it. Often one might see a claim that an approach is new, when it may be simply a rediscovery of something old. Synthesizing knowledge and devising the best/optimum approach for a given situation takes both innovation, and a basic knowledge of all that has been learned.
A prime example is Ken Brewer's Waksberg article:
Brewer, K.R.W. (2014), “Three controversies in the history of survey sampling,” Survey Methodology,
(December 2013/January 2014), Vol 39, No 2, pp. 249-262. Statistics Canada, Catalogue No. 12-001-X.
http://www.statcan.gc.ca/pub/12-001-x/2013002/article/11883-eng.htm
Ken believed in using probability sampling and models together, but he explains the different approaches.
And there is this amusing account:
Brewer, K. (2005). Anomalies, probing, insights: Ken Foreman’s role in the sampling inference controversy of the late 20th century. Aust. & New Zealand J. Statist., 47(4), 385-399.
There is also the following:
Hanif, M. (2011),
https://www.researchgate.net/publication/287454770_Ken_brewer%27s_contributions_to_survey_statistics_and_the_foundations_of_statistical_inference
Brewer, K.R.W. (1994). Survey sampling inference: some past perspectives and present prospects.
Pak. J. Statist., 10A, 213-233.
Brewer, K.R.W. (1999). Design-based or prediction-based inference stratified random vs stratified balanced sampling.
Int. Statist. Rev., 67(1), 35-47.
On page 2, "History and Ubiquity," in https://www.researchgate.net/publication/261474011_The_Classical_Ratio_Estimator_Model-Based
there is a very short account which I included, as recommended by Ken Brewer. Note that the model example that Ken gave in his Waksberg Award paper was for the case of the classical ratio estimator.
There is also this:
Brewer, K. and Gregoire, T.G. (2009). Introduction to survey sampling. Sample surveys: design, methods, and applications, handbook of statistics, 29A, eds. Pfeffermann, D. and Rao, C.R., Elsevier, Amsterdam, 9-37.
But I especially like the humorous aspect of Ken Brewer's account of the trials and tribulations of Ken Foreman, referenced above.
Other characters who may loom large would include William Cochran, Morris Hansen, Leslie Kish, Carl-Erik Sarndal, Fritz Schueren, and many others. A good current example would be Sharon Lohr.
Do you have other references to the history of survey inference?
Article Ken brewer's contributions to survey statistics and the foun...
Article The Classical Ratio Estimator (Model-Based)
Alternatively, perhaps you would prefer only to comment on the 2013 Waksberg Award paper:
Brewer, K.R.W. (2014), “Three controversies in the history of survey sampling,” Survey Methodology,
(December 2013/January 2014), Vol 39, No 2, pp. 249-262. Statistics Canada, Catalogue No. 12-001-X.
http://www.statcan.gc.ca/pub/12-001-x/2013002/article/11883-eng.htm
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PS - Please note that there is generally a huge decrease in sample size needs when good auxiliary/regressor data are available.
I am not interested of biseness application of scientific survey
Thank you for your thoughtful question, I am attaching a few articles.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2831349/
http://blogs.worldbank.org/impactevaluations/review-imbens-and-rubin-causal-inference-book
https://en.wikipedia.org/wiki/Rubin_causal_model
http://www.nycasa.org/NYCASA-InferentialStatistics-Touro-KHZou.pdf
Dear James
Thank you for this important point highlight, the history of all branches of science must be well considered, because we can learn great lessons from the former experiments and ideas, and may be getting new discovery by rejoining and combining different ideas including old ones, besides we must learn from the mistakes of the previous attempts to develop successful approaches .
The links may be useful for survey collection:
http://homepages.wmich.edu/~lewisj/332/PDF/History.pdf
http://www.census.gov/history/www/innovations/data_collection/developing_sampling_techniques.html
Good Luck
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