%0 Journal Article
%T Missing Data Techniques and the Statistical Conclusion Validity of Survey
%A Justin McLawhorn
%A Timothy J. Grigsby
%J Journal of Drug Issues
%@ 1945-1369
%D 2019
%R 10.1177/0022042618795878
%X The goal of the present review was to examine whether or not the use of modern missing data techniques impacts the statistical conclusion validity of research on alcohol and drug use outcomes in survey-based research studies. We identified 28 papers and received complete case data from the authors of 12 studies. Seven studies (25%) reported the missing data pattern (missing not at random [MNAR], missing at random [MAR], missing completely at random [MCAR]), 15 studies (53.6%) indicated the amount of missing observations in the data set, and a significant proportion of studies (n = 13, 46.4%) did not report any of the conditions or assumptions under which the missing data analysis was performed or implemented. Six of the 12 (50%) studies analyzed reported a different number of statistically significant associations between the complete case and full sample analyses. Efforts should be made to make missing data analysis more accessible, easy to implement and report to improve transparency and reproducibility of findings
%K missing data
%K imputation
%K maximum likelihood
%K validity
%K alcohol
%K drug
%K reproducibility
%K survey
%U https://journals.sagepub.com/doi/full/10.1177/0022042618795878