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Astra Zviedre, Arnis Eņģelis, Pēteris Tretjakovs, Irisa Zīle and Aigars Pētersons

REFERENCES Bachur, R. G., Hennelly, K., Callahan, M. J., Chen, C., Monuteaux, M. C. (2012). Diagnostic imaging and negative appendectomy rates in children: Effects of age and gender. Pediatrics , 129 (5), 877–884. Cobben, L. P., Otterloo, A. M., Puylaert, J. B. (2000). Spontaneously resolving appendicitis: Frequency and natural history in 60 patients. Radiology, 215 (2), 349–352. Dingemann, J., Ure, B. (2012). Imaging and the use of scores for the diagnosis of appendicitis in children. Eur. J. Pediatr. Surg., 22 (3), 195

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Jörg Drechsler, Gerd Ronning and Philipp Bleninger

. (1967). A Comparison of Four Methods for Constructing Factor Scores. Psychometrika, 32, 381-401. DOI: BF02289653 O’Keefe, C., Sparks, R., McAullay, D., and Loong, B. (2012). Confidentialising Survival Analysis Output in a Remote Data Access System. Journal of Privacy and Confidentiality 4. Available at: (accessed January 17, 2014). O’Keefe, C.M. and Good, N.M. (2008). A Remote Analysis Server - What Does Regression Output Look Like? In Privacy in Statistical

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Marco Di Zio and Ugo Guarnera

., and Scholtus, S. (2011). Handbook of Statistical Data Editing and Imputation. New York: John Wiley and Sons. Ghosh-Dastidar, B. and Schafer, J.L. (2006). Outlier Detection and Editing Procedures for Continuous Multivariate Data. Journal of Official Statistics, 22, 487-506. Granquist, L. (1997). The New View on Editing. International Statistical Review, 65, 381-387. Hedlin, D. (2003). Score Functions to Reduce Business Survey Editing at the U.K. Office for National Statistics. Journal of Official Statistics, 19, 177

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Mohammed Mediani, Jan Niehues and Alex Waibel

., Roland Kuhn, and Howard Johnson. Phrasetable smoothing for statistical machine translation. In EMNLP , pages 53-61, 2006. Gao, Qin and Stephan Vogel. Training phrase-based machine translation models on the cloudopen source machine translation toolkit chaski. Prague Bull. Math. Linguistics , 93: 37-46, 2010. Hardmeier, Christian. Fast and extensible phrase scoring for statistical machine translation. Prague Bull. Math. Linguistics , 93:87-96, 2010. Koehn, Philipp, Hieu Hoang, Alexandra

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Anders Norberg

Editing.” Statistical Review 2: 105-118. Granquist, L. 1997. “The New View on Editing.” International Statistical Review 3: 381-387. Doi: Granquist, L. and J. Kovar. 1997. “Editing of Survey Data: How Much Is Enough?” Survey Measurement and Process Quality, 415-435. Doi: Hedlin, D. 2003. “Score Functions to Reduce Business Survey Editing at the UK Office for National Statistics.” Journal of Official Statistics 19: 177

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Ton de Waal

). Three Eras of Survey Research. Public Opinion Quarterly, 75, 861-871. DOI: Hedlin, D. (2003). Score Functions to Reduce Business Survey Editing at the U.K. Office for National Statistics. Journal of Official Statistics, 19, 177-199. Hedlin, D. (2008). Local and Global Score Functions in Selective Editing. UN/ECE Work Session on Statistical Data Editing, 21-23 April, Vienna. Hidiroglou, M.A. and Berthelot, J.-M. (1986). Statistical Editing and Imputation for Periodic Business

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Richard Sigman, Taylor Lewis, Naomi Dyer Yount and Kimya Lee


This article discusses the potential effects of a shortened fielding period on an employee survey’s item and index scores and respondent demographics. Using data from the U.S. Office of Personnel Management’s 2011 Federal Employee Viewpoint Survey, we investigate whether early responding employees differ from later responding employees. Specifically, we examine differences in item and index scores related to employee engagement and global satisfaction. Our findings show that early responders tend to be less positive, even after adjusting their weights for nonresponse. Agencies vary in their prevalence of late responders, and score differences become magnified as this proportion increases. We also examine the extent to which early versus late responders differ on demographic characteristics such as grade level, supervisory status, gender, tenure with agency, and intention to leave, noting that nonminorities and females are the two demographic characteristics most associated with responding early.

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Bin Liu, Cindy Long Yu, Michael Joseph Price and Yan Jiang

7. References Ashmead, R. 2014. “Propensity Score Methods for Estimating Causal Effects from Complex Survey Data.” Ph.D. Dissertation, Ohio State University. Retrieved from . Berg, E., J.K. Kim, and C. Sinner. 2016. “Imputation under Informative Sampling.” Journal of Survey Statistics and Methodology 4: 436–462. Doi: 10.1093/jssam/smw032. Breidt, F.J., G. Claeskens, and J.D. Opsomer. 2005. “Model-Assisted Estimation for Complex Surveys Using Penalised Splines.” Biometrika 92(4): 831

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David Haziza and Éric Lesage

Protection for Unit Nonresponse With a Nonlinear Calibration-Weighting Routine.” Survey Research Methods 6: 105-111. Lee, S. 2006. “Propensity Score Adjustments as a Weighting Scheme for Volunteer Panel Web Surveys.” Journal of Official Statistics 22: 329-349. Little, R.J.A. 1986. “Survey Nonresponse Adjustments for Estimates of Means.” International Statistical Review 54: 139-157. Little, R.J.A. and S. Vartivarian. 2005. “Does Weighting for Nonresponse Increase the Variance of Survey Means?” Survey Methodology 31: 161

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Anita Dobek, Krzysztof Moliński and Ewa Skotarczak

References Chandra T.K., Joshi S.N. (1983): Comparison of likelihood ratio, Rao’s and Wald’s tests and a conjecture of C.R. Rao. Sankhya A, 45: 226-246. Fox J. (1997): Applied regression analysis, linear models, and related methods. Thousand Oaks, CA, US: Sage Publications, Inc. Li B. (2001): Sensitivity of Rao’s score test, the Wald test and the likelihood ratio test to nuisance parameters. J. Statistical Planning and Inference 97: 57-66. Madansky A. (1989): A comparison of the Likelihood Ratio