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• W1570261832 abstract "Missing data are a pervasive problem in many data sets and seem especially widespread in social and economic studies, such as customer satisfaction surveys. Imputation is an intuitive and flexible way to handle the incomplete data sets that result. This chapter starts with a basic discussion of missing-data patterns, which describe which values are observed in the data matrix and which are missing, and missing-data mechanisms, which concern the relationship between missingness and the values of variables in the data matrix. Then, it reviews the four classes of approaches to handling missing data, with a focus on multiple imputation (MI), which is the most generally useful approach for survey data, including customer satisfaction data. Further, a simple MI analysis is conducted for the ABC annual customer satisfaction survey (ACSS) data, and results are compared to those from alternative missing-data methods. Controlled Vocabulary Terms Missing data; statistical data; surveys" @default.
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• W1570261832 date "2011-11-09" @default.
• W1570261832 modified "2023-09-26" @default.
• W1570261832 title "Missing Data and Imputation Methods" @default.
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• W1570261832 doi "https://doi.org/10.1002/9781119961154.ch8" @default.
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