Matching Items (32)
Description
Many longitudinal studies, especially in clinical trials, suffer from missing data issues. Most estimation procedures assume that the missing values are ignorable or missing at random (MAR). However, this assumption leads to unrealistic simplification and is implausible for many cases. For example, an investigator is examining the effect of treatment on depression. Subjects are scheduled with doctors on a regular basis and asked questions about recent emotional situations. Patients who are experiencing severe depression are more likely to miss an appointment and leave the data missing for that particular visit. Data that are not missing at random may produce bias in results if the missing mechanism is not taken into account. In other words, the missing mechanism is related to the unobserved responses. Data are said to be non-ignorable missing if the probabilities of missingness depend on quantities that might not be included in the model. Classical pattern-mixture models for non-ignorable missing values are widely used for longitudinal data analysis because they do not require explicit specification of the missing mechanism, with the data stratified according to a variety of missing patterns and a model specified for each stratum. However, this usually results in under-identifiability, because of the need to estimate many stratum-specific parameters even though the eventual interest is usually on the marginal parameters. Pattern mixture models have the drawback that a large sample is usually required. In this thesis, two studies are presented. The first study is motivated by an open problem from pattern mixture models. Simulation studies from this part show that information in the missing data indicators can be well summarized by a simple continuous latent structure, indicating that a large number of missing data patterns may be accounted by a simple latent factor. Simulation findings that are obtained in the first study lead to a novel model, a continuous latent factor model (CLFM). The second study develops CLFM which is utilized for modeling the joint distribution of missing values and longitudinal outcomes. The proposed CLFM model is feasible even for small sample size applications. The detailed estimation theory, including estimating techniques from both frequentist and Bayesian perspectives is presented. Model performance and evaluation are studied through designed simulations and three applications. Simulation and application settings change from correctly-specified missing data mechanism to mis-specified mechanism and include different sample sizes from longitudinal studies. Among three applications, an AIDS study includes non-ignorable missing values; the Peabody Picture Vocabulary Test data have no indication on missing data mechanism and it will be applied to a sensitivity analysis; the Growth of Language and Early Literacy Skills in Preschoolers with Developmental Speech and Language Impairment study, however, has full complete data and will be used to conduct a robust analysis. The CLFM model is shown to provide more precise estimators, specifically on intercept and slope related parameters, compared with Roy's latent class model and the classic linear mixed model. This advantage will be more obvious when a small sample size is the case, where Roy's model experiences challenges on estimation convergence. The proposed CLFM model is also robust when missing data are ignorable as demonstrated through a study on Growth of Language and Early Literacy Skills in Preschoolers.
ContributorsZhang, Jun (Author) / Reiser, Mark R. (Thesis advisor) / Barber, Jarrett (Thesis advisor) / Kao, Ming-Hung (Committee member) / Wilson, Jeffrey (Committee member) / St Louis, Robert D. (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is common in the analysis of data to provide a goodness-of-fit test to assess the performance of a model. In the analysis of contingency tables, goodness-of-fit statistics are frequently employed when modeling social science, educational or psychological data where the interest is often directed at investigating the association among multi-categorical variables. Pearson's chi-squared statistic is well-known in goodness-of-fit testing, but it is sometimes considered to produce an omnibus test as it gives little guidance to the source of poor fit once the null hypothesis is rejected. However, its components can provide powerful directional tests. In this dissertation, orthogonal components are used to develop goodness-of-fit tests for models fit to the counts obtained from the cross-classification of multi-category dependent variables. Ordinal categories are assumed. Orthogonal components defined on marginals are obtained when analyzing multi-dimensional contingency tables through the use of the QR decomposition. A subset of these orthogonal components can be used to construct limited-information tests that allow one to identify the source of lack-of-fit and provide an increase in power compared to Pearson's test. These tests can address the adverse effects presented when data are sparse. The tests rely on the set of first- and second-order marginals jointly, the set of second-order marginals only, and the random forest method, a popular algorithm for modeling large complex data sets. The performance of these tests is compared to the likelihood ratio test as well as to tests based on orthogonal polynomial components. The derived goodness-of-fit tests are evaluated with studies for detecting two- and three-way associations that are not accounted for by a categorical variable factor model with a single latent variable. In addition the tests are used to investigate the case when the model misspecification involves parameter constraints for large and sparse contingency tables. The methodology proposed here is applied to data from the 38th round of the State Survey conducted by the Institute for Public Policy and Michigan State University Social Research (2005) . The results illustrate the use of the proposed techniques in the context of a sparse data set.
ContributorsMilovanovic, Jelena (Author) / Young, Dennis (Thesis advisor) / Reiser, Mark R. (Thesis advisor) / Wilson, Jeffrey (Committee member) / Eubank, Randall (Committee member) / Yang, Yan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Designing a hazard intelligence platform enables public agencies to organize diversity and manage complexity in collaborative partnerships. To maintain the integrity of the platform while preserving the prosocial ethos, understanding the dynamics of “non-regulatory supplements” to central governance is crucial. In conceptualization, social responsiveness is shaped by communicative actions, in which coordination is attained through negotiated agreements by way of the evaluation of validity claims. The dynamic processes involve information processing and knowledge sharing. The access and the use of collaborative intelligence can be examined by notions of traceability and intelligence cohort. Empirical evidence indicates that social traceability is statistical significant and positively associated with the improvement of collaborative performance. Moreover, social traceability positively contributes to the efficacy of technical traceability, but not vice versa. Furthermore, technical traceability significantly contributes to both moderate and high performance improvement; while social traceability is only significant for moderate performance improvement. Therefore, the social effect is limited and contingent. The results further suggest strategic considerations. Social significance: social traceability is the fundamental consideration to high cohort performance. Cocktail therapy: high cohort performance involves an integrative strategy with high social traceability and high technical traceability. Servant leadership: public agencies should exercise limited authority and perform a supporting role in the provision of appropriate technical traceability, while actively promoting social traceability in the system.
ContributorsWang, Chao-shih (Author) / Van Fleet, David (Thesis advisor) / Grebitus, Carola (Committee member) / Wilson, Jeffrey (Committee member) / Shultz, Clifford (Committee member) / Arizona State University (Publisher)
Created2015
Description
The Pearson and likelihood ratio statistics are well-known in goodness-of-fit testing and are commonly used for models applied to multinomial count data. When data are from a table formed by the cross-classification of a large number of variables, these goodness-of-fit statistics may have lower power and inaccurate Type I error rate due to sparseness. Pearson's statistic can be decomposed into orthogonal components associated with the marginal distributions of observed variables, and an omnibus fit statistic can be obtained as a sum of these components. When the statistic is a sum of components for lower-order marginals, it has good performance for Type I error rate and statistical power even when applied to a sparse table. In this dissertation, goodness-of-fit statistics using orthogonal components based on second- third- and fourth-order marginals were examined. If lack-of-fit is present in higher-order marginals, then a test that incorporates the higher-order marginals may have a higher power than a test that incorporates only first- and/or second-order marginals. To this end, two new statistics based on the orthogonal components of Pearson's chi-square that incorporate third- and fourth-order marginals were developed, and the Type I error, empirical power, and asymptotic power under different sparseness conditions were investigated. Individual orthogonal components as test statistics to identify lack-of-fit were also studied. The performance of individual orthogonal components to other popular lack-of-fit statistics were also compared. When the number of manifest variables becomes larger than 20, most of the statistics based on marginal distributions have limitations in terms of computer resources and CPU time. Under this problem, when the number manifest variables is larger than or equal to 20, the performance of a bootstrap based method to obtain p-values for Pearson-Fisher statistic, fit to confirmatory dichotomous variable factor analysis model, and the performance of Tollenaar and Mooijaart (2003) statistic were investigated.
ContributorsDassanayake, Mudiyanselage Maduranga Kasun (Author) / Reiser, Mark R. (Thesis advisor) / Kao, Ming-Hung (Committee member) / Wilson, Jeffrey (Committee member) / St. Louis, Robert (Committee member) / Kamarianakis, Ioannis (Committee member) / Arizona State University (Publisher)
Created2018
Description
Research has found there is a lack of women present in the IS industry. In order to combat this problem, this research examines why women are not choosing IS majors at the university level. At Arizona State University, the Computer Information Systems undergraduate degree program is only 23 percent female. Many different factors can influence the decision to choose a major, so survey methodology was used to ascertain what factors were the most important to different demographic groups when making this decision. The study found no significant gender difference when making this decision, but rather a difference between specific majors. Genuine interest, interesting work and high career earnings were identified as the most influential reasons for choosing a college major. The results were used to create recommendations for the IS Department at ASU to implement in the next year and encourage more female participation in the CIS undergraduate degree program.
ContributorsJorgenson, Erica Marie (Author) / Santanam, Raghu (Thesis director) / Moser, Kathleen (Committee member) / Department of Information Systems (Contributor) / W. P. Carey School of Business (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Service providers in the hotel industry are interested in identifying the factors that contribute to consumers' choice of hotel booking method. In an effort to determine these factors we used the predictive analytic tool of logistic regression. In particular, we concentrated on the choice of booking directly on a hotel website as compared to a third-party website. We found that consumers with children were 2.94 times more likely to use a hotel's website. We found that consumers who place a high importance on cost were 1.42 times more likely to use a third-party website for booking a hotel. These results could be useful for hotel marketing and sales representatives to better understand the preferences of their customers and improve the hotel reservation services provided. Predicting consumer needs and choices have the potential to optimize sales and increase profits.
ContributorsMolinaro, Erin Rose (Author) / Wilson, Jeffrey (Thesis director) / Dawson, Gregory (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / W. P. Carey School of Business (Contributor)
Created2015-05
DescriptionThe Impact of Lifestyle on Running Success
ContributorsRibbe, Kara Pauline (Author) / Dawson, Gregory (Thesis director) / Olsen, Timothy (Committee member) / Moser, Kathleen (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / W. P. Carey School of Business (Contributor) / School of Accountancy (Contributor)
Created2013-05
Description
Although the sport and exercise of running has a great amount of benefits to anyone's health, there is a chance of injury that can occur. There are many variables that can contribute to running injury. However, because of the vast amount of footsteps a frequent runner takes during their average run, foot strike pattern is a significant factor to be investigated in running injury research. This study hypothesized that due to biomechanical factors, runners that exhibited a rear foot striking pattern would display a greater incidence of chronic lower extremity injury in comparison to forefoot striking counterparts. This hypothesis would support previous studies conducted on the topic. Student-athletes in the Arizona State University- Men's and Women's Track & Field program, specifically those who compete in distance events, were given self reporting surveys to provide injury history and had their foot strike patterns analyzed through video recordings. The survey and analysis of foot strike patterns resulted in data that mostly followed the hypothesized pattern of mid-foot and forefoot striking runners displaying a lower average frequency of injury in comparison to rear foot strikers. The differences in these averages across all injury categories was found to be statistically significant. One category that displayed the most supportive results was in the average frequency of mild injury. This lead to the proposed idea that while foot strike patterns may not be the best predictor of moderate and severe injuries, they may play a greater role in the origin of mild injury. Such injuries can be the gateway to more serious injury (moderate and severe) that are more likely to have their cause in other sources such as genetics or body composition for example. This study did support the idea that foot strike pattern can be the main predictor in incidence of running injuries, but also displayed that it is one of many major factors that contribute to injuries in runners.
ContributorsBaker-Slama, Garrett Richard (Author) / Harper, Erin (Thesis director) / Cataldo, Donna (Committee member) / Wilson, Jeffrey (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2014-05
Description
This thesis provides a detailed analysis and risk assessment of the various stakeholders impacted by the US Airways-American Airlines merger. The stakeholders include employees, shareholders, new American passengers, the cities of Phoenix and Dallas, and other airlines. In order to understand how these stakeholders are impacted, we did thorough analysis of past major airline mergers and referenced cases from those mergers. Because the history of the airline industry is filled with hundreds of mergers and acquisitions, we only reference the America West-US Airways, Delta-Northwest, United-Continental, and Southwest-AirTran mergers in our thesis.
ContributorsFoletta, Hannah (Co-author) / Duckworth, Jessica (Co-author) / Dawson, Gregory (Thesis director) / Moser, Kathleen (Committee member) / Barrett, The Honors College (Contributor) / Department of Marketing (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / W. P. Carey School of Business (Contributor) / WPC Graduate Programs (Contributor)
Created2014-05
DescriptionDuring the Third Wave of Democratization, the United States has influenced many different cultures through politics and social interests. The way in which this has occurred is through their marketing and advertising. Many companies are the reason that the United States is a super power today.
ContributorsNebeker, Garrett Albert (Author) / Wilson, Jeffrey (Thesis director) / Reiser, Mark (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / W. P. Carey School of Business (Contributor)
Created2015-05