2006
DOI: 10.1186/1471-2288-6-57
|View full text |Cite
|
Sign up to set email alerts
|

Dealing with missing data in a multi-question depression scale: a comparison of imputation methods

Abstract: Background: Missing data present a challenge to many research projects. The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce. The objective of this study was to compare six different imputation techniques for dealing with missing data in the Zung Self-reported Depression scale (SDS).

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

3
360
0
16

Year Published

2009
2009
2021
2021

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 520 publications
(395 citation statements)
references
References 14 publications
3
360
0
16
Order By: Relevance
“…The implementation qualities that were most frequently reported were those concerning resources (including time constraints, financial constraints, and technical difficulties) and providers’ attitudes towards measures. These findings could explain why missing responses were reported in some of the studies included in this review (Arends et al ., 2014; Chesworth et al ., 2015; Dubbert, Cooper, Kirchner, Meydrech, & Bilbrew, 2002; Thyrian et al ., 2010) and health care research (Shrive et al ., 2006). Providers may not return audio‐recordings (Weissman, Rounsaville, & Chevron, 1982) or checklists, if they feel uncomfortable with audio‐recording or if they are overwhelmed with paperwork.…”
Section: Discussionmentioning
confidence: 99%
“…The implementation qualities that were most frequently reported were those concerning resources (including time constraints, financial constraints, and technical difficulties) and providers’ attitudes towards measures. These findings could explain why missing responses were reported in some of the studies included in this review (Arends et al ., 2014; Chesworth et al ., 2015; Dubbert, Cooper, Kirchner, Meydrech, & Bilbrew, 2002; Thyrian et al ., 2010) and health care research (Shrive et al ., 2006). Providers may not return audio‐recordings (Weissman, Rounsaville, & Chevron, 1982) or checklists, if they feel uncomfortable with audio‐recording or if they are overwhelmed with paperwork.…”
Section: Discussionmentioning
confidence: 99%
“…The problem of missing data was handled by comparing the results of analyses using multiple imputation, and lowest, highest, and mean value carried forward methods. 25 This did not affect the outcome of the analyses.…”
Section: Discussionmentioning
confidence: 99%
“…In order to maintain the quality of the data, ten participants who failed to fill in more than 60% of the total number of items in the JW-DEQ, the IES-R, or the Short-form MPQ were excluded from the subsequent analyses. Little's missing completely at random test (MCAR) test was not significant (p = 0.695), thus the mean for an item across the entire sample (question mean) was applied in such cases (Little, 1988;Shrive et al, 2003). Therefore, the data of 238 (51%) women were included in the Structural regression analysis (primiparas n = 138, multiparas n = 100).…”
Section: Characteristics Of Participantsmentioning
confidence: 99%
“…Fourth, after examining Little's MCAR test (Little, 1988) a single imputation method instead of a multiple one was employed in the present analysis. Although the authors considered that the single imputation method was an optimal solution, it is often claimed that with this method the distribution of values moves to the centre due to substituted values, which may lead to bias (Shrive, 2003).…”
Section: Limitations Of the Studymentioning
confidence: 99%