Addressing Missing Data in Surveys and Implementing Imputation Methods with SPSS
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Keywords:
Missing Data, Imputation, Survey, SPSS, Deterministic, ProbabilisticAbstract
The presence of missing data in surveys or in other types of scientific research poses significant
challenges for academic research, impacting the reliability, validity, and generalizability of study findings.
Missing data can introduce bias into the analysis, leading to erroneous results and conclusions. Furthermore,
missing data can compromise the statistical power of analyses, reducing the precision and accuracy of
estimates. Consequently, researchers may encounter difficulties in drawing robust conclusions or
identifying significant patterns within the data. Missing data can stem from various sources, including
participant non-response, data entry errors, survey design flaws, and respondent unwillingness to disclose
sensitive information. Many researchers struggle with how to handle missing data in their studies. They
often use simple methods like deleting all the cases with missing data, partial deletion, or filling in missing
values with a single number, such as the values of the variables mean, median, or mode. However, these
methods can be misleading because they don't take into account the reasons why data might be missing and
produce estimation errors, so other approaches are implemented to impute the missing values. However,
each approach has its limitations and assumptions, which can influence the validity of results and introduce
additional uncertainty into the analysis. This article analyzes the problem of missing data in social surveys,
the reasons for missing data, the types of missing data, and also suggests several ways, deterministic and
probabilistic, for data imputation.
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