The ability of different imputation methods for missing values in mental measurement questionnaires

Abstract

Background: Incomplete data are of particular important influence in mental measurement questionnaires. Most experts, however, mostly focus on clinical trials and cohort studies and generally pay less attention to this deficiency. We aim is to compare the accuracy of four common methods for handling items missing from different psychology questionnaires according to the items non-response rates. Method: All data were drawn from the previous studies including the self-acceptance scale (SAQ), the activities of daily living scale (ADL) and self-esteem scale (RSES). SAQ and ADL dataset, simulation group, were used to compare and assess the ability of four imputation methods which are direct deletion, mode imputation, Hot-deck (HD) imputation and multiple imputation (MI) by absolute deviation, the root mean square error and average relative error in missing proportions of 5, 10, 15 and 20%. RSES dataset, validation group, was used to test the application of imputation methods. All analyses were finished by SAS 9.4. Results: The biases obtained by MI are the smallest under various missing proportions. HD imputation approach performed the lowest absolute deviation of standard deviation values. But they got the similar results and the performances of them are obviously better than direct deletion and mode imputation. In a real world situation, the respondents' average score in complete data set was 28.22 ± 4.63, which are not much different from imputed datasets. The direction of the influence of the five factors on self-esteem was consistent, although there were some differences in the size and range of OR values in logistic regression model. Conclusion: MI shows the best performance while it demands slightly more data analytic capacity and skills of programming. And HD could be considered to impute missing values in psychological investigation when MI cannot be performed due to limited circumstances.

Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example

“…However, it ignored the relationship between the value of missing and other variables, which made its imputation performance was not ideal in most missing scenarios compared with other methods. This finding was consistent with the conclusion of previous research [ 26 ]. However, this study also found that under MAR (the ratio of missing proportion 1:2) mechanism, when the missing proportion was greater than about 25%, the Kappa of Mode was much higher than that of other methods.…”

“…Mode is one of the simplest methods to impute missing value, which is to impute missing value with the mode of not missing value of each variable [ 26 ]. It is generally used for non-numerical variables.…”

The Development of an Experience Measure for an Online Digital Mental Health Community

“…Digital service users reading content in online communities are more likely to be 'lurkers', individuals who will read community content but not actively participate 74 . Therefore, digital environments might be more prone to missing or misleading data around instrument completions and administration, different approaches to data imputation and how missing data is treated can have consequences in psychometric testing and measure performance 75 . Researchers in digital contexts should be aware of these issues, report missing data, and think in advance what psychometric properties the measure should be tested upon.…”

An Economic Evaluation Supported by Qualitative Data About the Patient Concerns Inventory (PCI) versus Standard Treatment Pathway in the Management of Patients with Head and Neck Cancer

“…When the results were similar, only patients who completed the baseline, intermediate follow-up and final follow-up were included in the sensitivity analysis. This was done to avoid bias by imputation or other methods of computing missing variables [ 30 ].…”