SMAG publications



  • Buil-Gil, D., Medina, J.  and Shlomo, N. (2020). Measuring the Dark Figure of Crime in Geographic areas. Small Area Estimation from   the Crime Survey for England and Wales.  To be published in British Journal of Criminology 
  • Buil-Gil, D., Moretti, A., Shlomo, N. and Medina, J. (2020). Assessing the Spatial EBLUP in Small Area Estimation: A Simulation Study and an Application to Confidence in Police Work.   Applied Spatial Analysis and Policy.
  • Cernat, A.and Sakshaug, J. (2020). The Impact of Nurse Continuity on Biosocial Survey Participation. Survey Methods: Insights from the Field (SMIF).
  • Cernat, A.  and Revilla, M. (2020). Moving from Face-to-Face to a Web Panel: Impacts on Measurement Quality. Journal of Survey Statistics and Methodology.
  • Cernat, A.  and Sakshaug, J. (2020). Nurse effects on Measurement Error in Household Biosocial Surveys. BMC Medical Research Methodology, 20(1), 45.
  • Cernat, A.  and Sakshaug, J. (2020). The Impact of Mixed Modes on Multiple Types of Measurement Error. Survey Research Methods, 14(1), 79–91.
  • Goldstein, H. and Shlomo, N. (2020.) A Probabilistic Procedure for Anonymisation and Analysis of Perturbed Datasets.   Journal of Official Statistics,   36(1),   89–115.
  • Moretti A., Shlomo, N and Sakshaug, J. (2020). Multivariate Small Area Estimation of Multidimensional Latent Economic Wellbeing Indicators.   International Statistical Review,   88 (1),   1-28.
  • Saunders, C. and Shlomo, N. (2020). A New Approach to Assess the Normalization of Differential Rates of Protest Participation.     Quality and Quantity.




  • Antoun, C. and Cernat, A. (2019). Factors Affecting Completion Times: A Comparative Analysis of Smartphone and PC Web Surveys: Social Science Computer Review.
  • Bianchi, A., Shlomo, N. Schouten, B., Da Silva, D. and Skinner, C. (2019). Estimation of Response Propensities and   Indicators of Representative Response Using Population-Level Information.  Survey Methodology, 45(2), 217-247.
  • Buil-Gil, D.,  Medina, J. and  Shlomo, N.   (2019). The Geographies of Perceived Neighborhood Disorder. A Small Area Estimation Approach.     Applied Geography,   109.
  • Buil-Gil, D., Moretti, A., Shlomo, N. and Medina, J. (2019). Worry about Crime in Europe. A Model-based Small Area Estimation from the European Social Survey. European Journal of Criminology. 
  • Cernat, A., Sakshaug, J. and Castillo, J. (2019). The Impact of Interviewer Effects on Skin Color Assessment in a Cross-National Context. International Journal of Public Opinion Research.
  • Moretti A., Shlomo, N and Sakshaug, J. (2019). Small Area Estimation of Latent    Economic Wellbeing. Sociological Methods and   Research. Online: 
  • Sakshaug, J. W., Cernat, A. and Raghunathan, T. E. (2019). Do Sequential Mixed-Mode Surveys Decrease Nonresponse Bias, Measurement Error Bias, and Total Bias? An Experimental Study. Journal of Survey Statistics and Methodology, 1–27.
  • Shlomo, N., Krenzke, T. and Li, J. (2019). Confidentiality Protection Approaches for Survey Weighted Frequency Tables. Transactions on Data Privacy, 12(3), 145 – 168.




  • Cernat, A. and Liu, M. (2018). Radio buttons in web surveys: Searching for alternatives. International Journal of Market Research.
  • Drechsler, J. and Shlomo, N. (2018). Preface to the papers on Data Confidentiality and Statistical Disclosure Control. Journal of the Royal Statistical Society, Series A, 181(3),  607-608.
  • Liu, M. and Cernat, A. (2018). Item-by-item Versus Matrix Questions: A Web Survey Experiment. Social Science Computer Review, 36(6), 690–706.
  • Moretti, A., Shlomo, N and Sakshaug, J. (2018). Parametric Bootstrap Mean Squared Error of a Small Area Multivariate EBLUP. Communications in Statistics-Simulation and Computation.
  • Rinott, Y., O’Keefe, C., Shlomo, N., and Skinner, C. (2018). Confidentiality and Differential Privacy in the Dissemination of Frequency Tables. Statistical Sciences, 33( 3), 358-385.
  • Schouten, B., Mushkudiani, N., Shlomo, N., Durrant, G., Lundquist, P. and Wagner, J. (2018). A Bayesian Analysis of Design Parameters in Survey Data Collection.  Journal of Survey Statistics and Methodology, 6(4),  431-464. 
  • Shlomo, N. (2018). Statistical Disclosure Limitation: New Directions and Challenges. Journal of Privacy and Confidentiality,   8(1).




  • Cernat, A. and  Lynn, P. (2017). The Role of E-mail Communications in Determining Response Rates and Mode of Participation in a Mixed-mode Design. Field Methods,
  • De Waal, T., Coutinho, W. and Shlomo, N. (2017). Calibrated Hot Deck Imputation for Numerical Data under Edit Restrictions. Journal of Survey Statistics and Methodology. 5(3), 372–397.
  • Plewis, I. and Shlomo, N. (2017). Using Response Propensities to Improve the Quality of Response in Longitudinal Studies.  Journal of Official Statistics, 33(3),   753–779.
  • Schouten, B. and Shlomo, N. (2017). Selecting  Adaptive Survey Design Strata with Partial R-indicators. International Statistical Review,  85(1), 143-163.



  • Sakshaug, J.W. and Huber, M. (2016) An Evaluation of Panel Nonresponse and Linkage Consent Bias in a Survey of Employees in Germany. Journal of Survey Statistics and Methodology 4, no. 1 pp. 71-93. eScholarID: 272455
  • Conrad, F.G., Couper, M.P. and Sakshaug, J.W. (2016) Classifying Open-Ended Reports: Factors Affecting the Reliability of Occupation Codes. Journal of Official Statistics 32, no. 1 pp. 75-92. eScholarID: 275506
  • Cernat, A., Couper, M., and Ofstedal, M. B. (2016). Estimation of mode effects in the Health and Retirement Study using measurement models. Journal of Survey Statistics and Methodology, 1-24 DOI
  • Liu, M., and Cernat, A. (2016) Multiple-choice versus matrix questions: a web survey experiment. Social Science Computer Review 1–17; article first published online: 28 NOV 2016 | DOI: 10.1177/0894439316674459


  • Shlomo, N. and Goldstein, H. (2015). Editorial: Big Data in Social research, Journal of the Royal Statistical Society, Series A, 178(4), 787-790. 
  • Shlomo, N., Antal, L. and Elliot, M. (2015) Measuring Disclosure Risk and Data Utility for Flexible Table Generators, Journal of Official Statistics, Vol. 31, Issue 2, pp. 305-324  
  • Stivala, A., Koskinen, J., Rolls, D., Wang, P., Robins, R. (2014) Snowball sampling for estimating exponential random graph models for large networks. Social Networks (in press) DOI: 10.1016/j.socnet.2015.11.003 
  • Cernat, A. (2015) Impact of mixed modes on measurement errors and estimates of change in panel data. Survey Research Methods, 9(2), 83-99 DOI: 10.18148.srm.2015.v9i2.5851
  • Cernat, A. (2015). The Impact of Mixing Modes on Reliability in Longitudinal Studies. Sociological Methods & Research 44(3), pp. 427-457 DOI: 10.1177/0049124114553802


  • Pina-Sa╠ünchez, J., Koskinen, J., and Plewis, I. (2014) Measurement Error in Retrospective Work Histories. Survey Research Methods 8(1), 43-55
  • Sakshaug, J.W. and Raghunathan, T.E. (2014) Generating Synthetic Data to Produce Public-Use Microdata for Small Geographic Areas Based on Complex Sample Survey Data with Application to the National Health Interview Survey. Journal of Applied Statistics 41, no. 10 pp. 2103-2122. eScholarID: 271576
  • Sakshaug, J.W. and Raghunathan, T.E. (2014) Generating Synthetic Microdata to Estimate Small Area Statistics in the American Community Survey. Statistics in Transition 15, no. 3 pp. 341-368. eScholarID: 271573
  • Sakshaug, Joseph W, and West, Brady T. (2014) Important considerations when analyzing health survey data collected using a complex sample design.  American journal of public health 104, no. 1 pp. 15-6 eScholarID: 271544/ DOI: 10.2105/AJPH.2013.301515
  • Soler Mares, J. and Shlomo, N. (2014). Data Privacy Using an Evolutionary Algorithm for Invariant Pram Matrices. Computational Statistics and Data Analysis, 79, 1-13. 


  • Hunt, K.J., Shlomo, N. and Addington-Hall, J. (2013) End-of-life care and achieving preferences for place of death in England: Results of a population-based survey using the VOICES-Short Form.  Palliative Medicine, November   DOI: 10.1177/0269216313512012 
  • Pannekoek, J. Shlomo, N. And De Waal, T. (2013) Calibrated Imputation of Numerical Data Under Linear Edit Restrictions.  Annals of Applied Statistics Volume 7, Number 4, pp. 1983-2006
  • Coutinho, W., De Waal, T. and Shlomo, N. (2013) Calibrated Hot-Deck Donor Imputation Subject to Edit Restrictions. Journal of Official Statistics, Vol. 29, No. 2, pp. 299-321
  • Hunt, K.J., Shlomo, N. and Addington- Hall, J. (2013), Recruiting Vulnerable Populations in Survey Research: a Comparative Trial of ‘Opt-in’ Versus ‘Opt-out’ Approaches.  BMC Medical Research Methodology 13:3. DOI: 10.1186/1471-2288-13-3
  • Koskinen, J. H., Robins, G. L., Wang, P., Pattison, P. E. (2013). Bayesian analysis for partially observed network data, missing ties, attributes and actors. Social Networks, vol. 35(4), pp. 514-527
  • Pina-Sanchez, J., Koskinen, J. and  Plewis, I. (2013)  "Implications of Retrospective Measurement Error in Event History Analysis." Metodolgia de Encuestas 15 pp. 5-25  


  • Schouten, B., Bethlehem, J., Beullens, K., Kleven, O., Loosveldt, G., Luiten, A., Rutar, K., Shlomo, N. and Skinner, C.J. (2012), Evaluating, Comparing, Monitoring and Improving Representativeness of Survey Response through R-indicators and Partial R-indicators. International Statistical Review, Vol 80, Issue 3, pp. 382-399.  DOI: 10.1111/j.1751-5823.2012.00189.x
  • Shlomo, N., Skinner, C.J. and Schouten, B. (2012) Estimation of an Indicator of the Representativeness of Survey Response, Journal of Statistical Planning and Inference  142, pp. 201-211
  • Plewis, I., Ketende, S. and Calderwood, L . (2012) "Assessing the accuracy of response propensities in longitudinal studies." Survey Methodology 38, no. 2 pp. 167-171 
  • Mason, A., Richardson, S., Plewis, I. and Best, N. (2012) "Strategy for modelling non-random missing data mechanisms in observational studies using Bayesian methods." Journal of Official Statistics 28, no. 2 pp. 279-302 
  • Sakshaug, J.W., Couper, M.P., Ofstedal, M.B., and Weir, D. (2012) Linking Survey and Administrative Records: Mechanisms of Consent." Sociological Methods and Research 41, no. 4 pp. 535-569. eScholarID: 271580