Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

An analysis of survey data to determine significant risk factors associated with adolescent marijuana use through utilization of sample weighting methods

Baker, Kelsey (2015) An analysis of survey data to determine significant risk factors associated with adolescent marijuana use through utilization of sample weighting methods. Master's Thesis, University of Pittsburgh. (Unpublished)

Submitted Version

Download (2MB)


This investigation seeks to identify factors associated with adolescent marijuana use in the 30 days prior to survey response collection in the 2012 National Survey on Drug Use and Health (NSDUH). Both inverse probability weighted and unweighted backwards elimination multivariate logistic regression modeling techniques were used to determine these factors. Final models compared the magnitude of the difference between odds ratios, the selection of final variables, the statistical significance of selected variables, and the overall fit of the models to determine whether or not we believed a weighted model was more appropriate for this type of complex sampling survey data.

Our analysis showed that age, tendency towards risky behavior, importance of religious beliefs, academic grades, cigarette use, and alcohol consumption were significant predictors of marijuana use. In addition, the odds of marijuana use in those who smoke cigarettes and consume alcohol are much higher than the odds in those who do not partake in either.

The public health significance of this study is that the results can be used to help public health officials understand the risk factors that affect an adolescent’s decision to use marijuana. This insight would allow them to collaborate with policy makers to more accurately identify at risk teens and allow for avoidance, earlier detection, and treatment strategies.

The assumptions of logistic regression were met, but few model diagnostics were available for the weighted model due to the lack of appropriate statistical diagnostics in the Stata statistical software. However, based on our results, we believe the weighted model, which incorporates the complex sampling methods used in the data collection, is more sufficient for our data. Although the available diagnostics revealed similar results for both models, we saw notable differences in the odds ratios for race and academic grades, which leads us to believe that weights are a necessary component of the model.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Baker, Kelseykkb24@pitt.eduKKB24
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorBuchanich, Jeanine Mjeanine@pitt.eduJEANINE
Committee MemberYouk, Adayouk@pitt.eduYOUK
Committee MemberBertolet, Marniebertoletm@edc.pitt.eduMHB12
Date: 28 January 2015
Date Type: Publication
Defense Date: 14 November 2014
Approval Date: 28 January 2015
Submission Date: 23 November 2014
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 115
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: National Survey Data, Sampling Weights, Logistic Regression, Marijuana, Adolescent Drug Use, Methodological Comparisons, Alcohol, Cigarette Smoking
Date Deposited: 28 Jan 2015 15:14
Last Modified: 01 Jan 2017 06:15


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item