Matching Items (4)
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- All Subjects: drinking
- All Subjects: Quantitative Psychology and Psychometrics
- Creators: Barrera, Manuel
- Member of: ASU Electronic Theses and Dissertations
Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
Description
Although research has documented robust prospective relationships between externalizing symptomatology and subsequent binge drinking among adolescents, the extent to which internalizing symptoms increase risk for drinking remains controversial. In particular, the role of anxiety as a predictor of binge drinking remains unclear. Recent evidence suggests that one possible reason for these mixed findings is that separate dimensions of anxiety may differentially confer risk for alcohol use. The present study tested two dimensions of anxiety - worry and physiological anxiety -- as predictors of binge drinking in a longitudinal study of juvenile delinquents. Overall, results indicate that worry and physiological anxiety showed differential relations with drinking behavior. In general, worry was protective against alcohol use, whereas physiological anxiety conferred risk for binge drinking, but both effects were conditional on levels of offending. Implications for future research examining the role of anxiety in predicting drinking behavior among youth are discussed.
ContributorsNichter, Brandon (Author) / Chassin, Laurie (Thesis advisor) / Barrera, Manuel (Committee member) / Presson, Clark (Committee member) / Arizona State University (Publisher)
Created2014
Description
Positive alcohol outcome expectancies (AOEs) are consistent longitudinal predictors of later alcohol use; however, exclusion of solitary drinking contexts in the measurement of AOEs may have resulted in an underestimation of the importance of low arousal positive (LAP) effects. The current study aimed to clarify the literature on the association between AOEs and drinking outcomes by examining the role of drinking context in AOE measurement. Further, exclusion of contextual influences has also limited understanding of the unique effects of AOEs relative to subjective responses (SR) to alcohol. The present study addressed this important question by exploring relations between AOEs and SR when drinking context was held constant across parallel measures of these constructs. Understanding which of these factors drives relations between alcohol effects and drinking behavior has important implications for intervention. After conducting confirmatory factor analysis (CFA) and tests of measurement invariance for the AOE and SR measures, 4 aims collectively examined the role of context in reporting of AOEs (Aims 1 and 2), the extent to which context specific AOEs uniquely relate to drinking outcomes (Aim 3), and the importance of context effects on correspondence between AOEs and SR (Aim 4). Results of Aims 1 and 2 demonstrated that participants are imagining contexts when reporting on measures of AOEs that do not specify the context, and found significant mean differences in high and low arousal positive AOEs across contexts. Contrary to the hypotheses of Aim 3, context-specific AOEs were not significantly associated with drinking behavior. Results of Aim 4 indicated that while LAP AOEs for both unspecified and solitary contexts were associated with LAP SR in a solitary setting, unspecified context AOEs had a stronger relation than the solitary context AOEs. No significant relations between high arousal positive (HAP) AOEs and HAP SR emerged. The findings suggest that further investigation of the relation between context-specific AOEs and drinking outcomes/SR is warranted. Future studies of these hypotheses in samples with a wider range of drinking behavior, or at different stages of alcohol involvement, will elucidate whether mean level differences in context specific AOEs are important in understanding alcohol related outcomes.
ContributorsScott, Caitlin (Author) / Corbin, William (Thesis advisor) / MacKinnon, David (Committee member) / Barrera, Manuel (Committee member) / Chassin, Laurie (Committee member) / Arizona State University (Publisher)
Created2016
Description
Item response theory (IRT) and related latent variable models represent modern psychometric theory, the successor to classical test theory in psychological assessment. While IRT has become prevalent in the assessment of ability and achievement, it has not been widely embraced by clinical psychologists. This appears due, in part, to psychometrists' use of unidimensional models despite evidence that psychiatric disorders are inherently multidimensional. The construct validity of unidimensional and multidimensional latent variable models was compared to evaluate the utility of modern psychometric theory in clinical assessment. Archival data consisting of 688 outpatients' presenting concerns, psychiatric diagnoses, and item level responses to the Brief Symptom Inventory (BSI) were extracted from files at a university mental health clinic. Confirmatory factor analyses revealed that models with oblique factors and/or item cross-loadings better represented the internal structure of the BSI in comparison to a strictly unidimensional model. The models were generally equivalent in their ability to account for variance in criterion-related validity variables; however, bifactor models demonstrated superior validity in differentiating between mood and anxiety disorder diagnoses. Multidimensional IRT analyses showed that the orthogonal bifactor model partitioned distinct, clinically relevant sources of item variance. Similar results were also achieved through multivariate prediction with an oblique simple structure model. Receiver operating characteristic curves confirmed improved sensitivity and specificity through multidimensional models of psychopathology. Clinical researchers are encouraged to consider these and other comprehensive models of psychological distress.
ContributorsThomas, Michael Lee (Author) / Lanyon, Richard (Thesis advisor) / Barrera, Manuel (Committee member) / Levy, Roy (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2011