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Analysis with missing data in drug prevention research.
J. W. Graham
, S. M. Hofer
, A. M. Piccinin
Biobehavioral Health
Research output
:
Contribution to journal
›
Review article
›
peer-review
105
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Scopus citations
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Keyphrases
Prevention Research
100%
Missing Data
100%
Missing Data Analysis
100%
Drug Prevention
100%
Expectation-maximization Algorithm
66%
Missing Data Problem
66%
Data Analysis Methods
33%
Attrition
33%
Parameter Estimation
33%
Covariance Matrix
33%
General Linear Model
33%
Multilevel Structural Equation Modeling
33%
Categorical Data
33%
Maximum Likelihood Method
33%
Biased Results
33%
Analysis Solution
33%
Multiple Imputation
33%
Algorithm Parameters
33%
Measurement Procedure
33%
Thorn
33%
Missingness
33%
Systematic Application
33%
Modeling Procedure
33%
Mathematics
Missing Data Analysis
100%
Expectation-Maximization Algorithm
66%
Covariance Matrix
33%
General Linear Model
33%
Parameter Estimate
33%
Categorical Data
33%
Maximum Likelihood
33%
Multiple Imputation
33%
Missingness
33%
Analysis Model
33%
Structural Equation Modeling
33%
Psychology
Expectation-Maximization Algorithm
100%
Linear Model
50%
Biochemistry, Genetics and Molecular Biology
Expectation-Maximization Algorithm
100%
Structural Equation Modeling
50%