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Though the likelihood is a useful tool for obtaining estimates of regression parameters, it is not readily available in the fit of hierarchical binary data models. The correlated observations negate the opportunity to have a joint likelihood when fitting hierarchical logistic regression models. Through conditional likelihood, inferences for the regression and covariance parameters as well as the intraclass correlation coefficients are usually obtained. In those cases, I have resorted to use of Laplace approximation and large sample theory approach for point and interval estimates such as Wald-type confidence intervals and profile likelihood confidence intervals. These methods rely on distributional assumptions and large sample theory. However, when dealing with small hierarchical datasets they often result in severe bias or non-convergence. I present a generalized quasi-likelihood approach and a generalized method of moments approach; both do not rely on any distributional assumptions but only moments of response. As an alternative to the typical large sample theory approach, I present bootstrapping hierarchical logistic regression models which provides more accurate interval estimates for small binary hierarchical data. These models substitute computations as an alternative to the traditional Wald-type and profile likelihood confidence intervals. I use a latent variable approach with a new split bootstrap method for estimating intraclass correlation coefficients when analyzing binary data obtained from a three-level hierarchical structure. It is especially useful with small sample size and easily expanded to multilevel. Comparisons are made to existing approaches through both theoretical justification and simulation studies. Further, I demonstrate my findings through an analysis of three numerical examples, one based on cancer in remission data, one related to the China’s antibiotic abuse study, and a third related to teacher effectiveness in schools from a state of southwest US.
ContributorsWang, Bei (Author) / Wilson, Jeffrey R (Thesis advisor) / Kamarianakis, Ioannis (Committee member) / Reiser, Mark R. (Committee member) / St Louis, Robert (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Created2017
Description
Correlation is common in many types of data, including those collected through longitudinal studies or in a hierarchical structure. In the case of clustering, or repeated measurements, there is inherent correlation between observations within the same group, or between observations obtained on the same subject. Longitudinal studies also introduce association between the covariates and the outcomes across time. When multiple outcomes are of interest, association may exist between the various models. These correlations can lead to issues in model fitting and inference if not properly accounted for. This dissertation presents three papers discussing appropriate methods to properly consider different types of association. The first paper introduces an ANOVA based measure of intraclass correlation for three level hierarchical data with binary outcomes, and corresponding properties. This measure is useful for evaluating when the correlation due to clustering warrants a more complex model. This measure is used to investigate AIDS knowledge in a clustered study conducted in Bangladesh. The second paper develops the Partitioned generalized method of moments (Partitioned GMM) model for longitudinal studies. This model utilizes valid moment conditions to separately estimate the varying effects of each time-dependent covariate on the outcome over time using multiple coefficients. The model is fit to data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to investigate risk factors of childhood obesity. In the third paper, the Partitioned GMM model is extended to jointly estimate regression models for multiple outcomes of interest. Thus, this approach takes into account both the correlation between the multivariate outcomes, as well as the correlation due to time-dependency in longitudinal studies. The model utilizes an expanded weight matrix and objective function composed of valid moment conditions to simultaneously estimate optimal regression coefficients. This approach is applied to Add Health data to simultaneously study drivers of outcomes including smoking, social alcohol usage, and obesity in children.
ContributorsIrimata, Kyle (Author) / Wilson, Jeffrey R (Thesis advisor) / Broatch, Jennifer (Committee member) / Kamarianakis, Ioannis (Committee member) / Kao, Ming-Hung (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Generalized Linear Models (GLMs) are widely used for modeling responses with non-normal error distributions. When the values of the covariates in such models are controllable, finding an optimal (or at least efficient) design could greatly facilitate the work of collecting and analyzing data. In fact, many theoretical results are obtained on a case-by-case basis, while in other situations, researchers also rely heavily on computational tools for design selection.
Three topics are investigated in this dissertation with each one focusing on one type of GLMs. Topic I considers GLMs with factorial effects and one continuous covariate. Factors can have interactions among each other and there is no restriction on the possible values of the continuous covariate. The locally D-optimal design structures for such models are identified and results for obtaining smaller optimal designs using orthogonal arrays (OAs) are presented. Topic II considers GLMs with multiple covariates under the assumptions that all but one covariate are bounded within specified intervals and interaction effects among those bounded covariates may also exist. An explicit formula for D-optimal designs is derived and OA-based smaller D-optimal designs for models with one or two two-factor interactions are also constructed. Topic III considers multiple-covariate logistic models. All covariates are nonnegative and there is no interaction among them. Two types of D-optimal design structures are identified and their global D-optimality is proved using the celebrated equivalence theorem.
Three topics are investigated in this dissertation with each one focusing on one type of GLMs. Topic I considers GLMs with factorial effects and one continuous covariate. Factors can have interactions among each other and there is no restriction on the possible values of the continuous covariate. The locally D-optimal design structures for such models are identified and results for obtaining smaller optimal designs using orthogonal arrays (OAs) are presented. Topic II considers GLMs with multiple covariates under the assumptions that all but one covariate are bounded within specified intervals and interaction effects among those bounded covariates may also exist. An explicit formula for D-optimal designs is derived and OA-based smaller D-optimal designs for models with one or two two-factor interactions are also constructed. Topic III considers multiple-covariate logistic models. All covariates are nonnegative and there is no interaction among them. Two types of D-optimal design structures are identified and their global D-optimality is proved using the celebrated equivalence theorem.
ContributorsWang, Zhongsheng (Author) / Stufken, John (Thesis advisor) / Kamarianakis, Ioannis (Committee member) / Kao, Ming-Hung (Committee member) / Reiser, Mark R. (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Created2018
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
In the presence of correlation, generalized linear models cannot be employed to obtain regression parameter estimates. To appropriately address the extravariation due to correlation, methods to estimate and model the additional variation are investigated. A general form of the mean-variance relationship is proposed which incorporates the canonical parameter. The two variance parameters are estimated using generalized method of moments, negating the need for a distributional assumption. The mean-variance relation estimates are applied to clustered data and implemented in an adjusted generalized quasi-likelihood approach through an adjustment to the covariance matrix. In the presence of significant correlation in hierarchical structured data, the adjusted generalized quasi-likelihood model shows improved performance for random effect estimates. In addition, submodels to address deviation in skewness and kurtosis are provided to jointly model the mean, variance, skewness, and kurtosis. The additional models identify covariates influencing the third and fourth moments. A cutoff to trim the data is provided which improves parameter estimation and model fit. For each topic, findings are demonstrated through comprehensive simulation studies and numerical examples. Examples evaluated include data on children’s morbidity in the Philippines, adolescent health from the National Longitudinal Study of Adolescent to Adult Health, as well as proteomic assays for breast cancer screening.
ContributorsIrimata, Katherine E (Author) / Wilson, Jeffrey R (Thesis advisor) / Kamarianakis, Ioannis (Committee member) / Kao, Ming-Hung (Committee member) / Reiser, Mark R. (Committee member) / Stufken, John (Committee member) / Arizona State University (Publisher)
Created2018
Description
This thesis will examine the recruitment process of educated millennials coming from four-year institutions to their first job out of college. When referring to millennials throughout my research, I am specifically focusing on current college graduates in order to better relate to my own experiences as a soon-to-be-graduate seeking a job. I will examine the various recruiting techniques, i.e. channels to connect with graduates, and the hiring and interview process as a whole. This thesis will also discuss the challenges and differences of recruiting millennials versus other generations. It will also discuss the latest trends in college and early talent recruiting. In order to do this, I conducted a number of in-depth interviews with recruiters and hiring managers from various companies that recruit heavily from Arizona State University (ASU), in order to determine what these companies have done to be successful among young college graduates. I aimed to identify the specific techniques that these companies use to connect with recent college graduates, what skills these firms are looking for, and what the hiring process looks like for new millennial employees. I also conducted an extensive online literature search about recruiting educated millennials in the workforce, and I used that information as a basis to form my interview questions. The interviews were meant to confirm or deny that research, but the interviewees also revealed many new trends and insights. I hope that this information will be beneficial not only to college seniors seeking first-time employment, but also to other companies who feel that they are struggling to capture young talent.
ContributorsCapra, Alexandria Luccia (Author) / Kalika, Dale (Thesis director) / Eaton, Kathryn (Committee member) / W. P. Carey School of Business (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
ASU's international student population has been growing exponentially in the last few years. Specifically, the fastest growing group has been international students from China. However, many of these students are arriving with inaccurate expectations of life at an American university. Furthermore, prospective students in China that have a desire to attend school in the U.S. are struggling to find a university that is affordable and respected. There is a huge opportunity for ASU to reach this market of students and increase their enrollment of international Chinese students. Our project aimed to create advertisements of ASU that target international Chinese students and their parents. The purpose of our project is to provide inspiration that ASU can utilize to create a professional marketing campaign to target this population of potential students.
ContributorsKagiyama, Kristen (Co-author) / Le, Alethea (Co-author) / Chien, Hsui Fen (Thesis director) / Chau, Angie (Committee member) / W. P. Carey School of Business (Contributor) / Department of Marketing (Contributor) / Department of Supply Chain Management (Contributor) / School of International Letters and Cultures (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Fringe: Abstract Fringe is a feature length screenplay and a work of original science fiction. The story takes place in the future, on a planet far from Earth but it is told from the human perspective and is meant to draw into question many issues present in society today: prejudice, hatred, multiculturalism, war, and social division. The screenplay seeks to pose an allegorical relationship between the humanity living on the planet, and the enemies they face, and the present day conflict between America and the Middle East or ISIS. The story follows Miles as he is forced to ally with his sworn enemy, the Lue, and learn to fight together to save his world from destruction. Miles begins the film bitter, resentful, and filled with prejudice towards his foes, much like a majority of Americans today. Instead of focussing on that conflict though, my story unites these two bitter enemies and asks them to put aside their violent and hateful pasts to fight a new, more powerful foe together. As the events unfold my characters learn that their enemies can be just like them and that they have something valuable to offer their world. My screenplay is about finding commonality with the enemy, on both sides of a conflict. By the end of my tale, Miles learns that there is good to be found in the world, even in his sworn enemies, if he looks close enough. It may seem like an archetypal plot on the surface but I worked hard to create a world that has not been seen in film before, an original science fiction universe that can bring these issues into the light and entertain an audience while doing so. I feel that my screenplay does just that, offering entertainment with and edge of social commentary, and stays true to the science fiction form.
ContributorsTrcic, Colton Walker (Author) / Maday, Gregory (Thesis director) / Bernstein, Gregory (Committee member) / WPC Graduate Programs (Contributor) / W. P. Carey School of Business (Contributor) / School of Film, Dance and Theatre (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The January 12, 2010 Haiti earthquake, which hit Port-au-Prince in the late afternoon, was the cause of over 220,000 deaths and $8 billion in damages \u2014 roughly 120% of national GDP at the time. A Mw 7.5 earthquake struck rural Guatemala in the early morning in 1976 and caused 23,000-25,000 deaths, three times as many injuries, and roughly $1.1 billion in damages, which accounted for approximately 30% of Guatemala's GDP. The earthquake which hit just outside of Christchurch, New Zealand early in the morning on September 4, 2010 had a magnitude of 7.1 and caused just two injuries, no deaths, and roughly 7.2 billion USD in damages (5% of GDP). These three earthquakes, all with magnitudes over 7 on the Richter scale, caused extremely varied amounts of economic damage for these three countries. This thesis aims to identify a possible explanation as to why this was the case and suggest ways in which to improve disaster risk management going forward.
ContributorsHeuermann, Jamie Lynne (Author) / Schoellman, Todd (Thesis director) / Mendez, Jose (Committee member) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Traditional educational infrastructures and their corresponding architectures have degenerated to work in opposition to today's scholastic objectives. In consideration of the necessity of formal education and academic success in modern society, a re-imagination of the ideal educational model and its architectural equivalent is long overdue. Fortunately, the constituents of a successful instructional method exist just outside our windows. This thesis, completed in conjunction with the ADE422 architectural studio, seeks to identify the qualities of a new educational paradigm and its architectural manifestation through an exploration of nature and biophilic design. Architectural Studio IV was challenged to develop a new academic model and corresponding architectural integration for the Herberger Young Scholars Academy, an educational institution for exceptionally gifted junior high and high school students, located on the West Campus of Arizona State University. A commencing investigation of pre-established educational methods and practices evaluated compulsory academic values, concepts, theories, and principles. External examination of scientific studies and literature regarding the functions of nature within a scholastic setting assisted in the process of developing a novel educational paradigm. A study of game play and its relation to the learning process also proved integral to the development of a new archetype. A hypothesis was developed, asserting that a nature-centric educational model was ideal. Architectural case studies were assessed to determine applicable qualities for a new nature-architecture integration. An architectural manifestation was tested within the program of the Herberger Young Scholars Academy and through the ideal functions of nature within an academic context.
ContributorsTate, Caroline Elizabeth (Author) / Underwood, Max (Thesis director) / Hejduk, Renata (Committee member) / De Jarnett, Mitchell (Committee member) / The Design School (Contributor) / W. P. Carey School of Business (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05