Abstract/Details

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation


2011 2011

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Abstract (summary)

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis.

Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.

Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results.

Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.

Indexing (details)


Subject
Social research
Classification
0344: Social research
Identifier / keyword
Social sciences; Motivational interviewing; Multiple imputation; Secondary data analysis
Title
Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation
Author
Wei, Ming-Yi
Number of pages
127
Publication year
2011
Degree date
2011
School code
0219
Source
MAI 50/03M, Masters Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781267015884
Advisor
Perez, Adriana
Committee member
Harris, Theodore R.; Walters, Scott T.
University/institution
The University of Texas School of Public Health
Department
Biostatistics
University location
United States -- Texas
Degree
M.P.H.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
1502173
ProQuest document ID
908349788
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/908349788
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