Journal articles: 'Sample surveys'

1

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

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2

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

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

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Todd, Michael M., and Leon F. Burmeister. "Principles of Successful Sample Surveys." Anesthesiology 99, no. 6 (December 1, 2003): 1251–52. http://dx.doi.org/10.1097/00000542-200312000-00003.

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

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

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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 (September 1985): 768. http://dx.doi.org/10.2307/2288499.

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Kinney, Satkartar K., and Darryl A. Cooney. "Nonresponse Bias in Sample Surveys." New Directions for Institutional Research 2019, no. 181 (March 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 (June 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 (June 1, 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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Hu, Wayne, and Andrey V. Kravtsov. "Sample Variance Considerations for Cluster Surveys." Astrophysical Journal 584, no. 2 (February 20, 2003): 702–15. http://dx.doi.org/10.1086/345846.

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12

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

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13

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

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14

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

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15

Cohen, Michael P. "Sample Size Considerations for Multilevel Surveys." International Statistical Review 73, no. 3 (December 14, 2006): 279–87. http://dx.doi.org/10.1111/j.1751-5823.2005.tb00149.x.

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16

Kozak, Marcin. "On Sample Allocation in Multivariate Surveys." Communications in Statistics - Simulation and Computation 35, no. 4 (November 23, 2006): 901–10. http://dx.doi.org/10.1080/03610910600880286.

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17

Keeble, Claire. "Maximum-likelihood estimation for sample surveys." Journal of Applied Statistics 40, no. 12 (July 16, 2013): 2777. http://dx.doi.org/10.1080/02664763.2013.820437.

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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 (June 1994): 51–61. http://dx.doi.org/10.1108/08858629410059834.

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19

Marchant, B. P., and R. M. Lark. "Optimized Sample Schemes for Geostatistical Surveys." Mathematical Geology 39, no. 1 (February 28, 2007): 113–34. http://dx.doi.org/10.1007/s11004-006-9069-1.

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20

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 (December 31, 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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21

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 (September 15, 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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22

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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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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24

BAZIGOS, G., and S. KAVADAS. "Optimal sampling designs for large-scale fishery sample surveys in Greece." Mediterranean Marine Science 8, no. 2 (December 3, 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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25

Cornesse, Carina, Annelies G. Blom, David Dutwin, Jon A. Krosnick, Edith D. De Leeuw, Stéphane Legleye, Josh Pasek, 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 (January 9, 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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26

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

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27

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

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28

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

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29

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

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30

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

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31

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

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32

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

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33

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 (September 2003): 319–29. http://dx.doi.org/10.1111/1467-842x.00286.

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34

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

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35

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

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36

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

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37

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 (January 1991): 887–98. http://dx.doi.org/10.1016/0277-9536(91)90244-7.

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38

Chiu, Stephen Wing-kai, and Niantao Jiang. "The future of telephone surveys in Hong Kong." Social Transformations in Chinese Societies 13, no. 1 (May 2, 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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39

Calahan, Charles A., and Walter R. Schumm. "An Exploratory Analysis of Family Social Science Mail Survey Response Rates." Psychological Reports 76, no. 3_suppl (June 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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40

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 (June 19, 2017): 756–67. http://dx.doi.org/10.1080/02755947.2017.1324543.

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41

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

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42

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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43

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 (December 1, 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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44

Smith, T. M. F. "Sample Surveys 1975-1990; An Age of Reconciliation?" International Statistical Review / Revue Internationale de Statistique 62, no. 1 (April 1994): 5. http://dx.doi.org/10.2307/1403539.

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45

Prasad, Shakti. "PRODUCT EXPONENTIAL METHOD OF IMPUTATION IN SAMPLE SURVEYS." Statistics in Transition New Series 19, no. 1 (2018): 159–66. http://dx.doi.org/10.21307/stattrans-2018-010.

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46

Simsek, Zeki. "Sample surveys via electronic mail: a comprehensive perspective." Revista de Administração de Empresas 39, no. 1 (March 1999): 77–83. http://dx.doi.org/10.1590/s0034-75901999000100010.

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Although postal questionnaires, personal interviewing, and telephone interviewing are the main methods of survey-based research, there is an increasing use of e-mail as a data collection medium. However, little, if any, published Western research in general and that of Turkish in particular have investigated e-mail survey technique from pure survey research perspective. Attempting to develop a framework to assess e-mail as a data collection mean, the purpose of this study is to explore e-mail-based questionnaire technique from complementary angles. To this goal, sample representativeness, data quality, response rates, and advantages and disadvantages of e-mail surveying are discussed.

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47

Stafford, Joshua D., Kenneth J. Reinecke, and Richard M. Kaminski. "SAS PROCEDURES FOR DESIGNING AND ANALYZING SAMPLE SURVEYS." Bulletin of the Ecological Society of America 84, no. 3 (July 2003): 111–14. http://dx.doi.org/10.1890/0012-9623(2003)84[111:spfdaa]2.0.co;2.

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48

Rao, C. R. "Combinatorics: Applications to Experimental Designs and Sample Surveys." Electronic Notes in Discrete Mathematics 15 (May 2003): 151–53. http://dx.doi.org/10.1016/s1571-0653(04)00564-5.

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49

Welsh, A. H., and Elvezio Ronchetti. "Bias-calibrated estimation from sample surveys containing outliers." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60, no. 2 (1998): 413–28. http://dx.doi.org/10.1111/1467-9868.00133.

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50

Szreder, Mirosław, and Jerzy P. Gwizdała. "Possible Future Developments of Sample Surveys in Finance." Argumenta Oeconomica Cracoviensia, no. 18 (2018): 69–82. http://dx.doi.org/10.15678/aoc.2018.1804.

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Journal articles on the topic 'Sample surveys'

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