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Journal articles on the topic 'Sample surveys'

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Published: 4 June 2021
Last updated: 18 June 2025

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1

Barnett, Vic. "Outliers in sample surveys." Journal of Applied Statistics 21, no. 5 (1994): 373–81. http://dx.doi.org/10.1080/757584014.

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2

Madrigal, J. L. "Theory of Sample Surveys." Technometrics 40, no. 4 (1998): 356–57. http://dx.doi.org/10.1080/00401706.1998.10485574.

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3

Smith, T. M. F. "Biometrika Centenary: Sample surveys." Biometrika 88, no. 1 (2001): 167–243. http://dx.doi.org/10.1093/biomet/88.1.167.

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4

Todd, Michael M., and Leon F. Burmeister. "Principles of Successful Sample Surveys." Anesthesiology 99, no. 6 (2003): 1251–52. http://dx.doi.org/10.1097/00000542-200312000-00003.

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5

McCarty, Christopher. "Determining Sample Size for Surveys." CAM 6, no. 3 (1994): 5. http://dx.doi.org/10.1177/1525822x9400600302.

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6

Titterington, D. M., William G. Madow, Harold Nisselson, Ingram Olkin, and Donald Rubin. "Incomplete Data in Sample Surveys." Journal of the American Statistical Association 80, no. 391 (1985): 768. http://dx.doi.org/10.2307/2288499.

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7

Smith, T. M. F. "Taguchi methods and sample surveys." Total Quality Management 5, no. 5 (1994): 243–54. http://dx.doi.org/10.1080/09544129400000045.

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8

Kinney, Satkartar K., and Darryl A. Cooney. "Nonresponse Bias in Sample Surveys." New Directions for Institutional Research 2019, no. 181 (2019): 35–46. http://dx.doi.org/10.1002/ir.20296.

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9

Rao, T. J. "Mahalanobis’ contributions to sample surveys." Resonance 4, no. 6 (1999): 27–33. http://dx.doi.org/10.1007/bf02834633.

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10

., Durr-E.-Nayab, and G. M. Arif. "Pakistan Panel Household Survey: Sample Size and Attrition." Pakistan Development Review 53, no. 2 (2014): 223–37. http://dx.doi.org/10.30541/v53i2pp.223-237.

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The socio-economic databases in Pakistan, as in most countries, can be classified into three broad categories, namely registration-based statistics, data produced by different population censuses and household survey-based data. The registration system of births and deaths in Pakistan has historically been inadequate [Afzal and Ahmed (1974)] and the population censuses have not been carried out regularly. The household surveys such as Pakistan Demographic Survey (PDS), Labour Force Survey (LFS) and Household Income Expenditure Survey (HIES) have been periodically conducted since the 1960s. These surveys have filled the data gaps created by the weak registration system and the irregularity in conducting censuses. The data generated by the household surveys have also enabled social scientists to examine a wide range of issues, including natural increase in population, education, employment, poverty, health, nutrition, and housing. All these surveys are, however, cross-sectional in nature so it is not possible to gauge the dynamics of these social and economic processes, for example the transition from school to labour market, movement into or out of poverty, movement of labour from one state of employment to another. A proper understanding of such dynamics requires longitudinal or panel datasets where the same households are visited over time. Since panel surveys are complex and expensive to carry out, they are not as commonly conducted as the cross-sectional surveys anywhere in the world and in Pakistan they are even rarer
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11

Elkasabi, Mahmoud, Z. Tuba Suzer-Gurtekin, and Yuxuan Chen. "A framework for sample size calculations in longitudinal surveys to measure net and gross changes." PLOS ONE 18, no. 9 (2023): e0291449. http://dx.doi.org/10.1371/journal.pone.0291449.

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In longitudinal surveys, repeated measurements are collected from the same sample units over time to measure gross change (i.e., change at the level of individual sample members). Longitudinal samples are sometimes supplemented by fresh sample to measure net change (i.e., change at the aggregate level). That is, in each measurement wave, while one part of the sample is newly recruited (fresh), another part overlaps with previously interviewed sample (repeated interviews). Many aspects of survey design of longitudinal surveys have been studied extensively, such as definition of target population, sample design, survey weighting, intervals between interviews, nonresponse, and panel attrition. Although the impact of the overlap between samples on the statistical power has been studied, sample size determination lacks a formulation that takes account of these factors in longitudinal surveys that aim to measure net and gross changes simultaneously. In this study, we propose a framework for sample size calculation to measure net and gross changes in estimated means or proportions concurrently in longitudinal surveys. We present a framework to compute panel and fresh sample sizes for varying levels of net and gross change. Finally, we illustrate the framework using nchange, an R package we developed to execute the algorithm of the proposed framework. The framework and the R package will support researchers to determine sample sizes targeting specific power of analysis with respect to measuring net and gross changes in rotating- or split-panel surveys.
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12

Bokun, Natalia. "Sample surveys of households in Belarus: state and perspectives." Statistics in Transition new series 14, no. 1 (2013): 89–106. http://dx.doi.org/10.59170/stattrans-2013-006.

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The main principles, characteristics and problems of three sample surveys of households (HH), conducted by the State Statistics of Belarus are considered: 1) The Household Sample Surveys (on expenses and incomes), 2) Private Subsidiary Plots in rural areas (PSP) and 3) Labour Force Survey (LFS). For each of them the purpose, sampling plan, sample design, data collection mode, the methods of estimation and possible ways to improve the surveys are discussed.
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13

BAZIGOS, G., and S. KAVADAS. "Optimal sampling designs for large-scale fishery sample surveys in Greece." Mediterranean Marine Science 8, no. 2 (2007): 65. http://dx.doi.org/10.12681/mms.154.

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The paper presents the quality problem of fishery statistics produced by the conducted land-based and sea-going, large scale sample surveys of the survey programme of the Institute of Marine Biological Resources of the Hellenic Centre for Marine Research (IMBR/HCMR) in Greece, and the optimality strategies developed in their sampling designs for the maximization of precision of the calculated sample estimates for a given cost of sampling.The optimality problems of the sampling designs of the individual large scale sample surveys are described, and the optimality solutions developed under the sampling variance structure are explained.The paper deals with the optimization of the following three large scale sample surveys: biological sample survey of commercial landings (BSCL), experimental fishing sample survey (EFSS), and commercial landings and effort sample survey (CLES).
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14

Cornesse, Carina, Annelies G. Blom, David Dutwin, et al. "A Review of Conceptual Approaches and Empirical Evidence on Probability and Nonprobability Sample Survey Research." Journal of Survey Statistics and Methodology 8, no. 1 (2020): 4–36. http://dx.doi.org/10.1093/jssam/smz041.

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Abstract There is an ongoing debate in the survey research literature about whether and when probability and nonprobability sample surveys produce accurate estimates of a larger population. Statistical theory provides a justification for confidence in probability sampling as a function of the survey design, whereas inferences based on nonprobability sampling are entirely dependent on models for validity. This article reviews the current debate about probability and nonprobability sample surveys. We describe the conditions under which nonprobability sample surveys may provide accurate results in theory and discuss empirical evidence on which types of samples produce the highest accuracy in practice. From these theoretical and empirical considerations, we derive best-practice recommendations and outline paths for future research.
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15

Dr. Vyas Dubey, Dr. Minal Uprety, and Ujjwal Dubey. "Estimating Population Mean in Sample Surveys." International Journal of Engineering and Management Research 10, no. 6 (2020): 106–17. http://dx.doi.org/10.31033/ijemr.10.6.15.

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The paper deals with a generalized estimator of population mean which includes several estimators as its particular cases. Under certain conditions, the proposed estimator is more efficient than existing estimators. Results are supported by numerical illustration.
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16

Little, Roderick J. "Weighting and Prediction in Sample Surveys." Calcutta Statistical Association Bulletin 60, no. 3-4 (2008): 147–67. http://dx.doi.org/10.1177/0008068320080301.

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17

McNamara, James F. "Sample Sizes for School Preference Surveys." International Journal of Educational Reform 1, no. 1 (1992): 83–90. http://dx.doi.org/10.1177/105678799200100113.

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18

Wilcox, James B., Danny N. Bellenger, and Edward E. Rigdon. "Assessing Sample Representativeness in Industrial Surveys." Journal of Business & Industrial Marketing 9, no. 2 (1994): 51–61. http://dx.doi.org/10.1108/08858629410059834.

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19

Hu, Wayne, and Andrey V. Kravtsov. "Sample Variance Considerations for Cluster Surveys." Astrophysical Journal 584, no. 2 (2003): 702–15. http://dx.doi.org/10.1086/345846.

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20

Kozak, Marcin. "On Sample Allocation in Multivariate Surveys." Communications in Statistics - Simulation and Computation 35, no. 4 (2006): 901–10. http://dx.doi.org/10.1080/03610910600880286.

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21

Cohen, Michael P. "Sample Size Considerations for Multilevel Surveys." International Statistical Review 73, no. 3 (2006): 279–87. http://dx.doi.org/10.1111/j.1751-5823.2005.tb00149.x.

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22

Keeble, Claire. "Maximum-likelihood estimation for sample surveys." Journal of Applied Statistics 40, no. 12 (2013): 2777. http://dx.doi.org/10.1080/02664763.2013.820437.

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23

Craig, Simon, and Diana Egerton-Warburton. "Surveys: Sample sizes and response rates." Emergency Medicine Australasia 25, no. 4 (2013): 376–77. http://dx.doi.org/10.1111/1742-6723.12090.

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24

Marchant, B. P., and R. M. Lark. "Optimized Sample Schemes for Geostatistical Surveys." Mathematical Geology 39, no. 1 (2007): 113–34. http://dx.doi.org/10.1007/s11004-006-9069-1.

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25

Watson, Verity, Terry Porteous, Tim Bolt, and Mandy Ryan. "Mode and Frame Matter: Assessing the Impact of Survey Mode and Sample Frame in Choice Experiments." Medical Decision Making 39, no. 7 (2019): 827–41. http://dx.doi.org/10.1177/0272989x19871035.

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Background. Choice experiments (CE) are applied in health economics to elicit public preferences and willingness to pay (WTP). CEs are frequently administered as Internet-based surveys. Internet surveys have recognized advantages, but concerns exist about the representativeness of Internet samples, data quality, and the impact on elicited values. Aim. We conducted the first study in health comparing an Internet-based CE survey with the more traditional general population mail survey. We also compared the Internet-based and mail CE surveys with computer-assisted personal interviews (CAPIs), which are commonly used to elicit health state valuations. Methods. Two separate samples were drawn from 2 United Kingdom (UK) volunteer Internet panels (IPs), CAPIs were undertaken with respondents sampled from UK Census Output Areas, and mail surveys were sent to UK households drawn from the postcode address file (PAF). Each mode received more than 1000 respondents. We compared modes and frames using objective measures (response rate, sample representativeness of the UK population, elicited values, theoretical validity, and cost per response) and subjective/self-reported measures (time taken to complete the study, perceived study consequentiality, and stated attribute nonattendance). This study intentionally confounded the survey modes and sample frame by choosing sample frames that are typically used by researchers for each mode. Results. Estimated WTP differs across mode-frame pairs. On most measures, CAPIs dominated. They are more expensive, however. On all measures, except response rates, Internet surveys dominated the mail survey. They were also cheaper. Conclusion. Researchers using IPs should pay attention to response rates and be aware that the quality of IPs differs. Given the importance of perceived consequentiality and attribute attendance in CEs, future research should address their impact across modes and frames.
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26

Kordos, Jan. "Application of rotation methods in sample surveys in Poland." Statistics in Transition new series 13, no. 2 (2013): 243–60. http://dx.doi.org/10.59170/stattrans-2012-020.

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The author reviews theory and application of rotation methods in sample surveys in Poland. He begins with reviewing designs of the surveys across time, depending on different objectives, focusing on partial rotation of sub-samples, and considers estimation problems and data quality issues generally. Next, he refers to some articles and books about surveys published over time, starting with Wilks (1940), Patterson (1950), Eckler (1955), Woodruff (1963), Rao and Graham (1964), Bailar (1975), Duncan and Kalton (1987) and Kalton and Citro (1993). He mentions also early Polish papers on rotation methods (Kordos (1966, 1967, 1971, 1982); Lednicki, 1982; Szarkowski and Witkowski, 1994), and concentrates on Polish household surveys, mainly Household Budget Survey (HBS), Labour Force Survey (LFS) and EU Statistics on Living Conditions and Income (EU-SILC). Special attention is devoted to last research on rotation sampling done by Polish sampling statisticians: Ciepiela et al. (2012), Kordos (2002), Kowalczyk (2002, 2003, 2004), Kowalski (2006, 2009), Kowalski and Wesołowski (2012) and Wesołowski (2010). Concluding remarks are given at the end.
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27

Lin, Y. X., D. Steel, and R. L. Chambers. "Restricted quasi-score estimating functions for sample survey data." Journal of Applied Probability 41, A (2004): 119–30. http://dx.doi.org/10.1239/jap/1082552194.

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This paper applies the theory of the quasi-likelihood method to model-based inference for sample surveys. Currently, much of the theory related to sample surveys is based on the theory of maximum likelihood. The maximum likelihood approach is available only when the full probability structure of the survey data is known. However, this knowledge is rarely available in practice. Based on central limit theory, statisticians are often willing to accept the assumption that data have, say, a normal probability structure. However, such an assumption may not be reasonable in many situations in which sample surveys are used. We establish a framework for sample surveys which is less dependent on the exact underlying probability structure using the quasi-likelihood method.
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28

Lin, Y. X., D. Steel, and R. L. Chambers. "Restricted quasi-score estimating functions for sample survey data." Journal of Applied Probability 41, A (2004): 119–30. http://dx.doi.org/10.1017/s0021900200112240.

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This paper applies the theory of the quasi-likelihood method to model-based inference for sample surveys. Currently, much of the theory related to sample surveys is based on the theory of maximum likelihood. The maximum likelihood approach is available only when the full probability structure of the survey data is known. However, this knowledge is rarely available in practice. Based on central limit theory, statisticians are often willing to accept the assumption that data have, say, a normal probability structure. However, such an assumption may not be reasonable in many situations in which sample surveys are used. We establish a framework for sample surveys which is less dependent on the exact underlying probability structure using the quasi-likelihood method.
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29

Calahan, Charles A., and Walter R. Schumm. "An Exploratory Analysis of Family Social Science Mail Survey Response Rates." Psychological Reports 76, no. 3_suppl (1995): 1379–88. http://dx.doi.org/10.2466/pr0.1995.76.3c.1379.

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Dillman (1978) has aggressively promoted his Total Design Method as a proven technique for generating higher response rates to social science surveys. Many university faculty teach his technique on the assumption that its use will generate high response rates for the individual student who uses it faithfully in thesis or dissertation research. In an exploratory assessment of six of Dillman's mail survey steps—quality of covering letter, use of follow-ups, importance of the study, survey's appearance or readability, survey's length, and type of sample (general public or specialized population)—with 15 family social science surveys, we found that, despite the small sample size, type of sample, and follow-up predicted mail survey response rates, as did a summary measure of the use of all six steps. The results suggest that the Total Design Method's mail survey steps are useful even for low-cost research by graduate students.
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30

Chiu, Stephen Wing-kai, and Niantao Jiang. "The future of telephone surveys in Hong Kong." Social Transformations in Chinese Societies 13, no. 1 (2017): 2–19. http://dx.doi.org/10.1108/stics-09-2016-0016.

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Purpose This paper aims to compare residential fixed-line telephone surveys with cell phone surveys for assessing the extent of the potential undercoverage issue evaluating the necessity and feasibility of conducting cell phone surveys or dual-frame telephone surveys in Hong Kong. Design/methodology/approach The research team simultaneously carried out a conventional fixed-line telephone survey and a cell phone survey in 2015 with similar features on survey design, sampling and data collection procedures. Two samples with sample size of 801 and 1,203 were achieved separately. Data collected were analysed to see to what extent survey findings will be biased if the sampling frame of telephone surveys is solely based on residential fixed-line numbers in Hong Kong, and if such a bias does exist, whether a survey conducted through cell phones or by adding a cell phone-only (CO) group would be an ideal solution for it. Findings The findings show that the cooperation rates for the cell phone survey were much lower than those of the fixed-line telephone survey. The respondents from two surveys were fairly different. However, estimates of most commonly used socio-demographic characteristics from the latter group had the least bias compared with population statistics. Supplanting the traditional fixed-line survey with a cell phone survey or supplementing it with a CO group will not make the resulting sample a better representative of the population but it will amplify the sample bias on the major social socio-demographic characteristics. Originality/value This paper empirically compares the two types of telephone surveys in a well-designed scientific study.
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31

Espenshade, Peter, and Jaiyul Yoo. "Sample Variance in Cosmological Observations with a Narrow Field of View." Astrophysical Journal 953, no. 2 (2023): 163. http://dx.doi.org/10.3847/1538-4357/ace71b.

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Abstract Surveys with a narrow field of view can play an important role in probing cosmology, but inferences from these surveys suffer from large sample variance, arising from random fluctuations around the cosmic mean. The standard method for computing the sample variance is based on two key approximations: treating perturbations linearly and the survey geometry as a box. We demonstrate that it can lead to a significant underestimate of the sample variance in narrow surveys. We present a new method for accurately computing the sample variance and apply our method to the recent observations of the warm–hot intergalactic medium (WHIM) based on spectroscopic measurements of blazars. We find that the sample variances in these surveys are significantly larger than the quoted measurement errors; for example, the cosmic mean baryon density contained in the WHIM could be lower by 54% at 1σ fluctuation than estimated in one observation. Accurately quantifying the sample variance is essential in deriving correct interpretations of the measurements in surveys with a small field of view.
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32

Pelletier, David L., and Louis A. H. Msukwa. "The use of national sample surveys for nutritional surveillance: Lessons from Malawi's national sample survey of agriculture." Social Science & Medicine 32, no. 8 (1991): 887–98. http://dx.doi.org/10.1016/0277-9536(91)90244-7.

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33

Frey, James H., and John Goyder. "The Silent Minority: Nonrespondents on Sample Surveys." Contemporary Sociology 18, no. 1 (1989): 155. http://dx.doi.org/10.2307/2072023.

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34

Andersen, Ronald, and John Goyder. "The Silent Minority: Nonrespondents on Sample Surveys." Social Forces 68, no. 3 (1990): 1009. http://dx.doi.org/10.2307/2579420.

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35

Nandram, B., and J. Sedransk. "Bayesian Predictive Inference for Longitudinal Sample Surveys." Biometrics 49, no. 4 (1993): 1045. http://dx.doi.org/10.2307/2532246.

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36

Berger, Yves G., Mohammed E. H. Tirari, and Yves Tille. "Towards optimal regression estimation in sample surveys." Australian New Zealand Journal of Statistics 45, no. 3 (2003): 319–29. http://dx.doi.org/10.1111/1467-842x.00286.

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37

Hansen, Morris H., and William N. Hurwitz. "The Problem of Nonresponse in Sample Surveys." American Statistician 58, no. 4 (2004): 292–94. http://dx.doi.org/10.1198/000313004x6328.

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38

Blalock, R. Reed, and Thomas H. Dial. "Assessing Sample Representativeness in Surveys of Physicians." Evaluation & the Health Professions 13, no. 3 (1990): 364–66. http://dx.doi.org/10.1177/016327879001300306.

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39

Lopez-Lee, David. "From surveys to policies: Who to sample." Computers, Environment and Urban Systems 13, no. 2 (1989): 67–74. http://dx.doi.org/10.1016/0198-9715(89)90035-5.

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40

Nygård, Fredrik, and Arne Sandström. "Income inequality measures based on sample surveys." Journal of Econometrics 42, no. 1 (1989): 81–95. http://dx.doi.org/10.1016/0304-4076(89)90077-8.

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41

Graubardand, Barry I., and Edward L. Korn. "Inference for Superpopulation Parameters Using Sample Surveys." Statistical Science 17, no. 1 (2002): 73–96. http://dx.doi.org/10.1214/ss/1023798999.

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42

Shao, Jun. "Impact of the Bootstrap on Sample Surveys." Statistical Science 18, no. 2 (2003): 191–98. http://dx.doi.org/10.1214/ss/1063994974.

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43

Post, R. J., and A. L. Millest. "Sample size in parasitological and vector surveys." Parasitology Today 7, no. 6 (1991): 141. http://dx.doi.org/10.1016/0169-4758(91)90279-w.

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44

Barrett, Boyd N., Brett van Poorten, Andrew B. Cooper, and Wolfgang Haider. "Concurrently Assessing Survey Mode and Sample Size in Off-Site Angler Surveys." North American Journal of Fisheries Management 37, no. 4 (2017): 756–67. http://dx.doi.org/10.1080/02755947.2017.1324543.

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45

Khan, M. G. M. "Statistics for Real-Life Sample Surveys: Non-Simple-Random Samples and Weighted Data." American Statistician 62, no. 4 (2008): 361. http://dx.doi.org/10.1198/tas.2008.s271.

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46

Gligorova, Lidija, and Marijana Kožul. "A new design and a weighting strategy of a survey on tourist activity of the Croatian population." Croatian Review of Economic, Business and Social Statistics 2, no. 2 (2016): 41–54. http://dx.doi.org/10.1515/crebss-2016-0012.

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Abstract Until 2014, the sample for the statistical survey Tourist Activity of the Population of the Republic of Croatia, shortly named as TADS, was a quota sample. The sample selection and the data collection for the survey in the Computer Assisted Telephone Interviewing (CATI) mode were conducted by a hired marketing research agency. In the summer of 2014, the Croatian Bureau of Statistics (CBS) set up its own call centre for CATI surveys. At the beginning of 2015, the CBS started developing a new probability sample design and a weighting strategy of TADS. The surveys of all the four quarters of 2015, fully conducted by the CBS, were treated as test surveys. The purpose of this paper is to describe and evaluate the finally accepted overlapping dual-frame survey design and a weighting procedure based on the sample data collected in all the quarters of 2015. The paper shows how the new design is incorporated in the existing circumstances in which surveys are conducted. The overlapping dual-frame survey design of TADS produces survey estimates related to the whole survey population based on two independent samples from two overlapping sampling frames (the first sampling frame consists of landline phone numbers from the phone book and the second one consists of mobile phone numbers from the phone book). In spite of a large undercoverage of the survey population in the phone book, using both phone types in TADS improves the total coverage of the survey population and relieves a problem of coverage and response bias. The applied weighting procedure is described systematically. The probability sampling allows calculating quality indicators such as standard errors of survey estimates and helps optimizing the survey process.
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47

Lacasa, Fabien, Marcos Lima, and Michel Aguena. "Super-sample covariance approximations and partial sky coverage." Astronomy & Astrophysics 611 (March 2018): A83. http://dx.doi.org/10.1051/0004-6361/201630281.

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Super-sample covariance (SSC) is the dominant source of statistical error on large scale structure (LSS) observables for both current and future galaxy surveys. In this work, we concentrate on the SSC of cluster counts, also known as sample variance, which is particularly useful for the self-calibration of the cluster observable-mass relation; our approach can similarly be applied to other observables, such as galaxy clustering and lensing shear. We first examined the accuracy of two analytical approximations proposed in the literature for the flat sky limit, finding that they are accurate at the 15% and 30–35% level, respectively, for covariances of counts in the same redshift bin. We then developed a harmonic expansion formalism that allows for the prediction of SSC in an arbitrary survey mask geometry, such as large sky areas of current and future surveys. We show analytically and numerically that this formalism recovers the full sky and flat sky limits present in the literature. We then present an efficient numerical implementation of the formalism, which allows fast and easy runs of covariance predictions when the survey mask is modified. We applied our method to a mask that is broadly similar to the Dark Energy Survey footprint, finding a non-negligible negative cross-z covariance, i.e. redshift bins are anti-correlated. We also examined the case of data removal from holes due to, for example bright stars, quality cuts, or systematic removals, and find that this does not have noticeable effects on the structure of the SSC matrix, only rescaling its amplitude by the effective survey area. These advances enable analytical covariances of LSS observables to be computed for current and future galaxy surveys, which cover large areas of the sky where the flat sky approximation fails.
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48

Yang, Jinyi, Xiaohui Fan, Ansh Gupta, et al. "DESI z ≳ 5 Quasar Survey. I. A First Sample of 400 New Quasars at z ∼ 4.7–6.6." Astrophysical Journal Supplement Series 269, no. 1 (2023): 27. http://dx.doi.org/10.3847/1538-4365/acf99b.

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Abstract We report the first results of a high-redshift (z ≳ 5) quasar survey using the Dark Energy Spectroscopic Instrument (DESI). As a DESI secondary target program, this survey is designed to carry out a systematic search and investigation of quasars at 4.8 < z < 6.8. The target selection is based on the DESI Legacy Imaging Surveys (the Legacy Surveys) DR9 photometry, combined with the Pan-STARRS1 data and J-band photometry from public surveys. A first quasar sample has been constructed from the DESI Survey Validation 3 (SV3) and first-year observations until 2022 May. This sample includes more than 400 new quasars at redshift 4.7 ≤ z < 6.6, down to 21.5 magnitude (AB) in the z band, discovered from 35% of the entire target sample. Remarkably, there are 220 new quasars identified at z ≥ 5, more than one-third of existing quasars previously published at this redshift. The observations so far result in an average success rate of 23% at z > 4.7. The current spectral data set has already allowed analysis of interesting individual objects (e.g., quasars with damped Lyα absorbers and broad absorption line features), and statistical analysis will follow the survey’s completion. A set of science projects will be carried out leveraging this program, including quasar luminosity function, quasar clustering, intergalactic medium, quasar spectral properties, intervening absorbers, and properties of early supermassive black holes. Additionally, a sample of 38 new quasars at z ∼ 3.8–5.7 discovered from a pilot survey in the DESI SV1 is also published in this paper.
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49

Sarioglo, V. "25 Years of Experience in Household Sample Surveys in the Official Statistics of Ukraine: Main Assets, Problems, Prospects." Statistics of Ukraine 100, no. 1 (2023): 27–39. http://dx.doi.org/10.31767/su.1(100)2023.01.03.

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Since 1998, the state statistics of Ukraine has accumulated a huge experience in organizing and conducting household sample surveys in accordance with international recommendations and standards. Nowadays, the harmonized system of surveys includes the Household Living Conditions Survey, the Labor Force Survey and the Survey of Household Agricultural Activities in rural areas. About 100,000 households are surveyed annually, which represent all private households in various samples of these surveys. A significant number of specialists with the necessary knowledge and skills have been trained in the State Statistics Service of Ukraine (hereinafter - SSSU), territorial bodies of the SSSU.
 The article considers the main characteristics of the SSSU experience in the field of methodology and practice of organizing and conducting the household sample surveys, collecting and processing surveys’ data, statistical indicators estimation. The strengths and weaknesses of the acquired experience, potential directions for improvement of surveys, risks that may affect the quality of their results in the future, plans for the development of the system of sample surveys and the use of new methods of data collecting and processing are discussed.
 It is noted that the acquired experience made it possible to approach in 2021 to the development of the basis for introduction of such actual survey in Ukraine as the EU statistics on income and living conditions (EU-SILC), to foresee the use of new data collection technologies CAPI, CATI and CAWI without any particular problems.
 At the same time, it is shown that some important methodologies regarding the estimation of indicators based on survey results have not been implemented. It is noted that an urgent problem for the SSSU is the use of "big data" in household surveys, which provides the opportunity of significantly reducing the burden on households during the survey, improving the completeness and timeliness of information about them, reducing costs for survey providing, etc. Such data could provide also estimation of certain important statistical indicators during the war. The risks of an accelerated transition for conducting the state household sample surveys mainly using CATI and CAWI technologies are discussed, related to the possibility of a significant deterioration in the quality of the survey results due to the lack of relevant skills of the SSSU specialists, insufficient level of Internet use by the population, lack of population and household registers with quality data in Ukraine, etc.
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50

Dodeen, Hamzeh Mohd. "The Prevalence of Missing Data in Survey Research." International Journal for Innovation Education and Research 6, no. 3 (2018): 83–90. http://dx.doi.org/10.31686/ijier.vol6.iss3.978.

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The credibility of surveys relies significantly on the completeness of the data collected from representative samples. Missing data is a serious problem in survey research. The existence of variables with missing information negatively affects the research results and findings. This study examines the prevalence of missing data in surveys, and additionally compares its incidence between genders. A total of 119 relevant surveys from different countries represented the sample of this study. Results indicated that, on average, 38% of data was lost in the surveys analyzed. Males and females were very similar with respect to the extent of missing data, with an average of 37% and 38% respectively. Overall, results show that only 62% of the initial sample size was available at the end of the data collection stage.
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