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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Anna Suleymanova
Comparative Analysis of Approaches to Multiple Imputation Results Aggregation
Sociology
(Bachelor’s programme)
10
2016
In current research the comparative analysis of approaches to multiple imputation aggregation methods depending on research situation was performed. Through the agency of statistical experiment the effectiveness of approaches to multiple imputation aggregation was evaluated in relation to situations described by different shares of missing data, different scales of variables and three widespread analytical instruments, such as descriptive statistics, correlations and multiple linear regression. Based on these evaluations following recommendations were composed:

1. For nominal variables description the Rubin’s rule is preferred, for ordinal and interval variables description the choice of approach depends on the share of missing data.

2. For Kramer’s V and Spearman correlation coefficient the choice of approach also depends on the share of missing data, for Pearson’s correlation coefficient the averaging of imputed variables is preferred.

3. For multiple linear regression Rubin’s rule is preferred in any depending on research situation.

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