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Laboratory Tests in Addition to the Alvarado Score in the Management of Acute Appendicitis in School-Age Children

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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Disclosure Risk from Factor Scores

. (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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An Integrated Database to Measure Living Standards

Survey Non-response.” International Statistical Review 78(1): 40–64. Doi: . Attanasio, O., E. Hurst, and L. Pistaferri. 2015. “The Evolution of Income, Consumption, and Leisure Inequality in the US, 1980–2010.” In Improving the Measurement of Consumer Expenditures , edited by C.D. Carroll, T.F. Crossley, and J. Sabelhaus, 100–140. University of Chicago Press. Augurzy, B. and C.M. Schmidt. 2001. The Propensity Score: A Means to an End , IZA Discussion Paper No. 271. Available at: https

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A Contamination Model for Selective Editing

., 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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EVALD – a Pioneer Application for Automated Essay Scoring in Czech

. European Languages Resources Association (ELRA). Burstein, Jill, Karen Kukich, Susanne Wolff, Chi Lu, and Martin Chodorow. Computer analysis of essays. 1998. Castro-Castro, Daniel, Rocío Lannes-Losada, Montse Maritxalar, Ianire Niebla, Celia Pérez-Marqués, Nancy C. Álamo-Suárez, and Aurora Pons-Porrata. A Multilingual Application for Automated Essay Scoring. In Advances in Artificial Intelligence – IBERAMIA 2008 , pages 243–251, Berlin, Heidelberg, 2008. Springer Berlin Heidelberg. Foltz, Peter W., Darrell Laham, and Thomas K. Landauer. The Intelligent

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Parallel Phrase Scoring for Extra-large Corpora

., 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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SELEKT – A Generic Tool for Selective Editing

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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Selective Editing: A Quest for Efficiency and Data Quality

). 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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Does the Length of Fielding Period Matter? Examining Response Scores of Early Versus Late Responders


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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Generalized Method of Moments Estimators for Multiple Treatment Effects Using Observational Data from Complex Surveys

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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