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References Cohen, J. (1988) Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum. Cohen, J. (1992) A power primer. Psychological Bulletin, Vol 112(1), 155-159. Fisz M. (1967) Rachunek Prawdopodobieństwa i Statystyka Matematyczna. PWN, Warsaw. Hamedani, G. G.; Tata, M. N. (1975) On the determination of the bivariate normal distribution from distributions of linear combinations of the variables. The American Mathematical Monthly, 82 (9): 913-915. Noether G. E. (1987) Sample Size Determination for Some Common Nonparametric Tests

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., Saloña, M. I. (1999) A biometrical study of Berniniella serratirostris (Acari: Oribatei) and some related species. In: Bruin, J., van der Geest, L. P., Sabelis, M. W. (eds.). Ecology and Evolution of the Acari. Kluwer Academic Publishers, Dordrecht, pp. 569-579. Cao, Y., Hawkins, C. P., Vinson, M. R. (2003). Measuring and controlling data quality in biological assemblage surveys with special reference to stream benthic macroinvertebrates. Freshwater Biol., 48, 1898-1911. Cardini, A., Elton, S. (2007). Sample size and sampling error in geometric morphometric studies of

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REFERENCES Abd Elgawad, M. A., Barakat, H. M. ,Yan, T. (2019a). Bivariate limit theorems for record values based on random sample sizes. Sankhya A: Indian J. Statist. Available from: (accessed 25.10.2019). Abd Elgawad, M. A., Barakat, H. M., Xiong, S. (2019b). Limit distributions of random record model in a stationary Gaussian sequence. Comm. Statist. - Theory Meth. (accessed 25.10.2019). Barakat, H. M. (2007). Limit theory of generalized order statistics. J. Statist

.C.F., Dodou, D. & Wieringa P.A. (2009). Exploratory factor analysis with small sample sizes. Multivariate Behavioral Research, 44, 147−181. DOI: 10.1080/00273170902794206. Dengler, J., Lobel, S. & Dolnik C. (2009). Species constancy depends on plot size a problem for vegetation classification and how it can be solved. J. Veg. Sci ., 20, 754−766. DOI: 10.1111/j.1654-1103.2009.01073.x. Diaconis, P. & Efron B. (1983). Computer-intensive methods in statistics. Sci. Am. , 248, 116−130. doi:10.1038/scientificamerican0583-116 Dochtermann, N.A. & Jenkins S.H. (2011


Determining sample sizes in multistage samples requires variance components for each stage of selection. The relative sizes of the variance components in a cluster sample are dramatically affected by how much the clusters vary in size, by the type of sample design, and by the form of estimator used. Measures of the homogeneity of survey variables within clusters are related to the variance components and affect the numbers of sample units that should be selected at each stage to achieve the desired precision levels. Measures of homogeneity can be estimated using standard software for random-effects models but the model-based intracluster correlations may need to be transformed to be appropriate for use with the sample design. We illustrate these points and implications for sample size calculation for two-stage sample designs using a realistic population derived from household surveys and the decennial census in the U.S.

Reprod Sci 2010, 118, 176-181. 11. García-Herreros M., Aparicio I.M., Barón F.J., García- Marín L.J., Gil M.C.: Standardization of sample preparation, staining and sampling methods for automated sperm head morphometry analysis of boar spermatozoa. Int J Andro 2006, 29, 553-563. 12. Garrett C., Liu D.Y., Baker H.W.: Selectivity of the human sperm-zona pellucida binding process to sperm head morphometry. Fertil Steril 1997, 67, 362-371. 13. Hidalgo M., Rodríguez I., Dorado Sanz J., Soler C.: Effect of sample size and staining methods on stallion sperm morphometry by the


Adaptive survey designs can be used to allocate sample elements to alternative data collection protocols in order to achieve a desired balance between some quality measure and survey costs. We compare four alternative methods for allocating sample elements to one of two data collection protocols. The methods differ in terms of the quality measure that they aim to optimize: response rate, R-indicator, coefficient of variation of the participation propensities, or effective sample size. Costs are also compared for a range of sample sizes. The data collection protocols considered are CAPI single-mode and web-CAPI sequential mixed-mode. We use data from a large experiment with random allocation to one of these two protocols. For each allocation method we predict outcomes in terms of several quality measures and costs. Although allocating the whole sample to single-mode CAPI produces a higher response rate than allocating the whole sample to the mixed-mode protocol, we find that two of the targeted allocations achieve a better response rate than single-mode CAPI at a lower cost. We also find that all four of the targeted designs out-perform both single-protocol designs in terms of representativity and effective sample size. For all but the smallest sample sizes, the adaptive designs bring cost savings relative to CAPI-only, though these are fairly modest in magnitude.