2016
DOI: 10.1093/pan/mpw020
|View full text |Cite
|
Sign up to set email alerts
|

How Multiple Imputation Makes a Difference

Abstract: Edited by R. Michael AlvarezPolitical scientists increasingly recognize that multiple imputation represents a superior strategy for analyzing missing data to the widely used method of listwise deletion. However, there has been little systematic investigation of how multiple imputation affects existing empirical knowledge in the discipline. This article presents the first large-scale examination of the empirical effects of substituting multiple imputation for listwise deletion in political science. The examinat… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
86
0
1

Year Published

2018
2018
2024
2024

Publication Types

Select...
4
3
2

Relationship

0
9

Authors

Journals

citations
Cited by 174 publications
(87 citation statements)
references
References 48 publications
0
86
0
1
Order By: Relevance
“…Researchers may choose to drop observations with missing values, assign all missing values the same value (based on an assumption, for example, about why a response was not given), impute missing values using variable means, or use other imputation methods. Lall (2016) replicates a large number of empirical political science studies using multiple imputation instead of listwise deletion for missing values and finds that this changes the results for almost half of the studies. Unless there is a clear explanation for missingness that points to an assigned value or method, replication can test the robustness of the original results to alternative missing data techniques.…”
Section: Data Transformationsmentioning
confidence: 85%
“…Researchers may choose to drop observations with missing values, assign all missing values the same value (based on an assumption, for example, about why a response was not given), impute missing values using variable means, or use other imputation methods. Lall (2016) replicates a large number of empirical political science studies using multiple imputation instead of listwise deletion for missing values and finds that this changes the results for almost half of the studies. Unless there is a clear explanation for missingness that points to an assigned value or method, replication can test the robustness of the original results to alternative missing data techniques.…”
Section: Data Transformationsmentioning
confidence: 85%
“…This method although common in ecology, produces downward-biased standard errors since the zeros are treated as knowns rather than probabilistic estimates (Lall , 2016). This means that essentially this does not solve any problem in terms of quality statistical results.…”
Section: Methods Of Handling Missing Datamentioning
confidence: 99%
“…Goodness-of-fit indicators are not equivalent to the probability of a given model being true (Anscombe 1973;King 1986), and the weights constructed this way are not invariant to transformations in the dependent variable. Moreover, our data set has a number of missing observations, so model comparison measures could be misleading (Lall 2016). …”
Section: Extreme Bounds Analysismentioning
confidence: 99%
“…Secondly, in DRF, non-observed cases are not assumed to be missing at random, but rather as values that contain information in themselves. The algorithm assumes that observations are missing for a reason, what is most likely the case with social science data (Lall 2016). This is a more conservative approach than assuming that missing cases fit into an underlying parametric distribution.…”
Section: Random Forestsmentioning
confidence: 99%