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29... : G Azar, B. (2002). Finding a solution for missing data. Monitor on Psychology, 33 (7), 70. Bhaskaran, K.; & Smeeth, L. (2014). What is the difference between missing completely at random and missing at random? International Journal of Epidemiology, 43 (4), 1336-1339. Bodner, T. E. (2006). Missing data: Prevalence and reporting practices. Psychological Reports, 99, 675 680. Cao, W.; Tsiatis, A. A.; & Davidian, M. (2009). Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data. Biometrika, 96, 723-734. Dillman, D. A.; Eltinge, J. L.; Groves, R. M.; & Little, R. J. (2002). Survey nonresponse in design, data collection, and analysis. Survey nonresponse, 3-26. Enders, C. K. (2003). Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data. Psychological methods, 8 (3), 322. Fowler Jr, F. J. (2013). Survey research methods. Sage publications. Graham, J. W.; & Coffman, D. L. (2012). Structural equation modeling with missing data. Handbook of structural equation modeling, 277-295. Groves, R. M.; & Couper, M. P. (2012). Nonresponse in household interview surveys. John Wiley & Sons. Groves, R. M.; Fowler Jr, F. J.; Couper, M. P.; Lepkowski, J. M.; Singer, E.; & Tourangeau, R. (2011). Survey methodology (Vol. 561). John Wiley & Sons. Hox, J.; De Leeuw, E. D.; Couper, M. P.; Groves, R. M.; De Heer, W.; Kuusela, V.; & Belak, E. (2002). The influence of interviewers attitude and behaviour on household survey nonresponse: An international comparison. In: R. M. Groves, D. A. Dillman, J. L. Eltinge, & R.J.A. Little (Eds). Survey nonresponse. New York: Wiley, pp. 103-120 Knol, M. J.; Janssen, K. J.; Donders, A. R. T.; Egberts, A. C.; Heerdink, E. R.; Grobbee, D. E.;... & Geerlings, M. I. (2010). Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example. Journal of Clinical Epidemiology, 63 (7), 728-736.

6: $6 $;/6 789 5%4) 30 Kromrey, J. D.; & Hines, C. V. (1994). Nonrandomly missing data in multiple regression: An empirical comparison of common missing-data treatments. Educational & Psychological Measurement, 54 (3), 573-593. Little, R. J.; & Rubin, D. B. (2014). Statistical analysis with missing data. John Wiley & Sons. Mcdonald, R. A.; Thurston, P. W.; & Nelson, M. R. (2000). A Monte Carlo study of missing item methods. Organizational Research Methods, 3 (1), 71-92. Peugh, J. L.; & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74 (4), 525-556. Raghunathan, T. E. (2004). What do we do with missing data? Some options for analysis of incomplete data. Annu. Rev. Public Health, 25, 99-117. Roth, P. L. (1994). Missing data: A conceptual review for applied psychologists. Personnel Psychology, 47 (3), 537-560. Seaman, S.; Galati, J.; Jackson, D.; & Carlin, J. (2013). What Is Meant by" Missing at Random"? Statistical Science, 257-268. Tang, G.; Little, R. J.; & Raghunathan, T. E. (2003). Analysis of multivariate missing data with nonignorable nonresponse. Biometrika, 747-764. Tourangeau, R.; Rips, L. J.; & Rasinski, K. (2000). The psychology of survey response. Cambridge University Press. Van der Heijden, G. J.; Donders, A. R. T.; Stijnen, T.; & Moons, K. G. (2006). Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. Journal of Clinical Epidemiology, 59 (10), 1102-1109.