Abstract
Many large scale surveys have designs that are complex, incorporating stratification and perhaps more than one stage of selection. Data from these surveys are used for a considerable amount of analysis, involving the computation of statistics ranging from simple totals and means, to those required for the comparison of domains, linear and logistic regression analysis and contingency table analysis. These analyses are usually done using computer software which does not take the design into account. This paper focuses on the development and use of computer programs which take the design into account for such analyses.
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References
Bean, J. A. (1975), “Distributions and properties of variance estimators for complex multistage probability samples. An empirical distribution”. Vital and Health Statistics, Series 2, No. 65, U.S. Department of Health, Education, and Welfare. Washington: U.S. Government Printing Office.
Beaton, A. E., D. B. Rubin, and J. L. Barone (1976), “The acceptability of regression solutions: another look at computational accuracy”. Journal of the American Statistical Association 71, 315–321.
Bellhouse, D. R. (1980), “Computation of variance-covariance estimates for general multi-stage designs”. In COMSTAT: Processing in Computation Statistics, ed. M. M. Barritt and D. Wishart, pp. 57 – 63. Vienna: Physica-Verlag.
Binder, B. A., M. Gratton, M. A. Hidiroglou, and J. N. K. Rao (1983), “Analysis of categorical data from surveys with complex designs”. Paper presented at the Seminar on Recent Developments in the Analysis of Large Scale Data Sets, sponsored by the Statistical Offices of European Communities, Luxemburg.
Binder, B. A. (1983), “On the variances of asymptotically normal estimation from complex surveys”. International Statistical Review 51, 279–292.
Clogg, C. C. (1982), “Some models for the analysis of association in multiway cross-classifications having ordered categories”. Journal of the American Statistical Association 77, 803–815.
Fay, R. E. (1982), “Contingency tables for complex designs, CPLX”. Proceedings of the American Statistical Association, Section on Survey Research Methods, pp. 44 – 53.
Fay, R. E. (1983), “CPLX—Contingency tables analysis for complex sample designs, program documentation”. Unpublished report. U.S. Bureau of the Census; Washington.
Fay, R. E. (1985), “A jackknifed chi-square test for complex samples”. Journal of the American Statistical Association 90, 148–157.
Fellegi, I. P. (1980), “Approximate tests of independence and goodness of fit based on stratified multistage samples”. Journal of the American Statistical Association 71, 665–670.
Frankel, M. R. (1971). Inference from Survey Samples. Ann Harbor: Institute for Social Research, University of Michigan.
Fuller, W. A. (1975), “Regression analysis for sample surveys”. Sankhyā 37, 117–132.
Hidiroglou, M. A. (1974), “Estimation of regression parameters for finite populations”. Ph.D. Thesis, Iowa State University.
Hidiroglou, M. A., W. A. Fuller, and R. D. Hickman (1980), “SUPER CARP”, Survey Section, Iowa State University, Ames, Iowa.
Hidiroglou, M. A., and J. N. K. Rao (1985), “On chi-squared tests with categorical data from the Canada health survey”. Statistics Canada report.
Jones, H. L. (1974), “Jackknife estimation of functions of stratum means”. Biometrika 62, 343–348.
Kish, L., and M. R. Frankel (1974), “Inference from complex samples”. Journal of the Royal Statistical Society, Series B 86, 1–37.
Kopitze, R., T. J. Boardman, and F. A. Graybill (1975), “Least square programs. A look at the square root procedure”. The American Statistician 29, 64–66.
Krewski, D., and J. N. K. Rao (1981), “Inference from stratified samples: properties of the linearization, jackknife and balanced repeated replication methods”. Annals of Statistics 9, 1010–1019.
Ling, R. G. (1974), “Comparison of several algorithms for computing sample means and variances”. Journal of the American Statistical Association 69, 859–866.
Longley, J. (1967), “An appraisal of least squares programs for the electronic computer from the point of view of the use”. Journal of the American Statistical Association 62, 819–841.
Maurer, K., G. Jones, and E. Bryant (1978), “Repeated computational efficiency of the linearized and balanced repeated replication procedures for computing sampling variances”. Proceedings of the American Statistical Association, Section on Survey Research Methods, 388–391.
McCarthy, P. J. (1966), “Replication: An approach to the analysis of data from complex surveys”. NCHS report, Series 2, No. 14, U.S. Dept. of Health, Education and Welfare.
Mellor, R. W. (1973), “Subsample replication variance estimators”. Ph.D. Thesis, Harvard University.
Nelder, J. A., and R. W. M. Wedderbum (1972), “Generalized linear models”. Journal of the Royal Statistical Society, Series A 135, 370–384.
Quenouille, M. H. (1956), “Notes on bias estimation”. Biometrika 43, 353–360.
Raj, D. (1966), “Some remarks on a simple procedure of sampling without replacement”. Journal of the American Statistical Association 61, 393–397.
Raj, D. (1968), SamplingTheory. New York: McGraw-Hill.
Rao, J. N. K. (1975), “Unbiased variance estimation for multi-stage design”. Sankhya, Series C 37, 133–139.
Rao, J. N. K., and A. J. Scott (1984), “On chi-square tests for multiway contingency tables with cell proportions estimated from survey data”. Annals of Statistics 12, 46–60.
Shah, B. V. (1978), “SUDAAN: Survey data analysis software”. Proceedings of the American Statistical Association, Statistical Computation Section, pp. 146–151.
The Health of Canadians (1981). Report of the Canada Health Survey, Health and Welfare and Statistics Canada Publication, Catalogue 82–538E.
Tukey, J. W. (1958), “Bias and confidence in not-quite large samples”. Annals of Mathematical Statistics 29, 614.
Wilkinson, J. H. (1975), The Algebraic Eigenvalues Problem. Oxford: Clarendon Press, 229 – 233.
Woodruff, R. W. (1971), “A simple method for approximating the variance of a complicated estimate”. Journal of the American Statistical Association 6, 411–414.
Woodruff, R. S., and B. D. Causey (1976), “Computerized method for approximating the variance of a complicated estimate”. Journal of the American Statistical Association 71, 315–321.
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© 1987 D. Reidel Publishing Company, Dordrecht, Holland
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Hidiroglou, M.A., Paton, D.G. (1987). Some Experiences in Computing Estimates and Their Variances Using Data from Complex Survey Designs. In: MacNeill, I.B., Umphrey, G.J., Bellhouse, D.R., Kulperger, R.J. (eds) Advances in the Statistical Sciences: Applied Probability, Stochastic Processes, and Sampling Theory. The University of Western Ontario Series in Philosophy of Science, vol 34. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-4786-3_20
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DOI: https://doi.org/10.1007/978-94-009-4786-3_20
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