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Date of Award
Education Evaluation and Research
Shlomo S. Swilowsky
Commercial statistical packages such as SPSS which include multiple imputation(MI) options make replacement of missing values easier. Use of such data handling utilities risks biased data resulting from artificially wide or narrow confidence intervals (CIs) however. Convenience samples of sizes 20, 40, 50, 100, 200, and 500 were drawn from the 2015 Consumer Expenditure Survey (United States Department of Labor, 2016) and evaluated using linear regression. Random deletion at levels of 10%, 20%, 30%, and 40% was performed on 6 of 8 selected variables. MI of 20, 40, 60, and 100 imputations were performed. Comparison of confidence interval widths between intact and imputed data showed an overall pattern of widened CIs in imputed data sets as compared to intact data. Larger sample sizes were significantly associated with narrower CIs. A suggestive association between CIs and number of imputations was found. Thirteen of 24 original data sets returned patterns of significant association between variables. Eight of 24 data sets showed migration either above or below nominal alpha thresholds post imputation, suggesting MI results should not immediately be taken at face value.
Kavanagh, Maurice, "The Effect Of Number Of Imputations On Parameter Estimates In Multiple Imputation With Small Sample Sizes" (2018). Wayne State University Dissertations. 2035.