CURSO:ITEM NONRESPONSE AND IMPUTATION TRADUCCION:NO RESPUESTA AL ITEM E IMPUTACION SIGLA:SOL4032 CREDITOS:05 MODULOS:02 CARACTER:OPTATIVO TIPO:CATEDRA CALIFICACION:ESTANDAR DISCIPLINA:SOCIOLOGIA PALABRAS CLAVE:METODOLOGIA DE ENCUESTAS, NO RESPUESTA, IMPUTACION DATOS PERDIDOS I.DESCRIPCIÓN DEL CURSO Missing data are a common problem which can lead to biased results if the missingness is not taken into account at the analysis stage. Imputation is often suggested as a strategy to deal with item nonresponse allowing the analyst to use standard complete data methods after the imputation. However, several misconceptions about the aims and goals (isn't imputation making up data?) of imputation make some users skeptical about the approach. In this course we will illustrate why thinking about the missing data is important and clarify which goals a useful imputation method should try to achieve (and which not). II.OBJETIVOS DE APRENDIZAJE 1.Understand why the default way of dealing with missing data as implemented in most statistical software is often problematic. 2.Realize that it is better not to account for the missingness instead of applying simplistic imputation methods such as mean imputation or last-observation carried forward. 3.Know what is meant by a missing data mechanism and understand the implication of the different mechanisms. 4.Identify the principle ideas and concepts involved in multiple imputation. III.CONTENIDOS 1.Missing data mechanisms. 2.Analysis of missing data. 3.Imputation methods and its applications. 4.Treatment of missing data in survey research. 5.Data processing. IV.METODOLOGIA PARA EL APRENDIZAJE -Flipped-classroom. -Lectures delivered through pre-recorded online video sessions. -Live meetings, via a web platform, with discussions. -Quizzes and Homeworks. -Readings. V.EVALUACION DE APRENDIZAJES -Online quizzes:20% -Homeworks:40% -Class participation in online meetings:10% -Final online Exam:30% VI.BIBLIOGRAFIA Required readings Carpenter, J. and Kenward, M. (2012). Multiple imputation and its application. New York: John Wiley & Sons. Selected chapters. Groves, R.M., Fowler, F.J., Couper, M.P., Lepkowski, J.M., Singer, E., Tourangeau, R. (2004) Survey Methodology, Wiley. Selected chapters. Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data (2nd ed.), New York: John Wiley & Sons. Selected chapters. Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data (2nd ed.), New York: John Wiley & Sons. Selected chapters. Brick, J.M. and Kalton, G. (1996). Handling missing data in survey research. Statistical Methods in Medical Research, 5, 215-238. Selected chapters. Rubin, D.B. (1986). Basic ideas of multiple imputation for nonresponse. Survey Methodology, 12, 37-47. Recommended (optional) readings Rassler, S., Rubin, D.B., Zell, E.R (2007). Incomplete data in epidemology and medical statistics. In: Rao CR, Miller J, Rao DC (eds) Handbook of Statistics, 27, Elsevier, pp 569-601. Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of statistical software, 45(3). PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE INSTITUTO DE SOCIOLOGIA / JUNIO 2018