Matching Items (459)
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
The purpose of this paper is to understand how companies are finding high potential employees and if they are leaving top talent behind in their approach. Eugene Burke stated in 2014 that 55% of employees that are labeled as a High Potential Employee will turn over and move companies. Burke (2014) also states that the average high potential employee tenure is five years. The Corporate Leadership Council says that on average, 27% of a company's development budget is spent on its high potential program (CEB 2017). For a midsize company, the high potential development budget is almost a million dollars for only a handful of employees, only to see half of the investment walking out the door to another company . Furthermore, the Corporate Leadership Council said that a study done in 2005 revealed that 50% of high potential employees had significant problems within their job (Kotlyar and Karkowsky 2014). Are time and resources are being given to the wrong employees and the right employees are being overlooked? This paper exams how companies traditionally select high potential employees and where companies are potentially omitting employees who would be better suited for the program. This paper proposes that how a company discovers their top talent will correlate to the number of turnovers or struggles that a high potential employee has on their job. Future research direction and practical considerations are also presented in this paper.
ContributorsHarrison, Carrie (Author) / Mizzi, Philip (Thesis director) / Ruediger, Stefan (Committee member) / Department of Management and Entrepreneurship (Contributor) / School of Sustainability (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In which industry that has ever been profit generating, does a firm profit from their failure? The United States has a mass incarceration problem. With 25% of the world prison population residing in the US, spending on detention costs the US government $80 billion annually. Over 50% of the individuals incarcerated in America are of black or Latino descent. This massive growth in the incarcerated population of America began in the 1970s and with the passive support of American citizens has created an industry whose players profit from the detention of people. Currently, the privately run detention facilities in the United States hold 7% of state prisoners, 18% of federal prisoners, and nearly 75% of ICE's undocumented detainee population. The detention of people for profit is an idea rooted in the same profit motive that allowed the institution of slavery to flourish. However even after the 13th Amendment abolished slavery in the U.S., the oppressive forces behind slave-era economics have been perpetuated through legislation and policies that continued the stratification of society and reinforcement of the social order. With the help of corporate lobbyists, political action committees, and organizations such as the American Legislative Exchange Council, the corporate shareholders of private prisons, such as CoreCivic and The GEO Group, are able to directly align their profit-driven interests with those of federal and state legislators. By the incorporation of legislation and policy into state and federal law, the shareholders of private prisons are able to directly affect legislation as well as their own potential for profit. The justification for the usage of private prisons is thought to be seen in the price savings and flexibility that it provides for federal and state governments. However, due to the law enforcement contractor's exemption from public record laws, there is no clear evidence of where the cost savings occur, or even if there are cost savings at all. Is it ethical for a for-profit-prison corporation to be responsible for the care, security, and rehabilitation of an individual, when if they fail to rehabilitate the individual, it will add to the number of inmates under their control? The measure of a prison's failure to rehabilitate an inmate is considered the recidivism rate, and is affected when an inmate leaves a detention facility, commits another crime, then is arrested. This profit motive is causing our society to incarcerate increasing numbers of people in private prisons. For-profit prisons financially benefit from long-term incarceration and recidivism. The passive investments from public and private employees and institutions through investment corporations are the legs that allow the private prison industry to stand. Twenty-nine investment firms, such as The Vanguard Group and Fidelity Investments, own nearly two-thirds of the two largest players in the private prison industry. This includes the passive investments by public institutions such as the Arizona State University Foundation's $600 million endowment fund as well as the $500 million directly invested into CoreCivic and GEO Group from the University of Texas/ Texas A&M Investment Management Company. The goal of abolishing private prisons will require years of litigation against the giants of the industry as well as the governmental entities supporting them. However, we can start today by demanding divestiture by our school and similar institutions as well continuing to share the knowledge of the oppressive forces associated with the detention of individuals for profit.
ContributorsBayham, Michael (Author) / Gomez, Alan (Thesis director) / Dacey, John (Committee member) / W.P. Carey School of Business (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The purpose of this thesis is to explore how Blockchain technology can help solve problems large corporations commonly face. For example, it is a common problem for large businesses and organizations to manage sales contracts with thousands of items on them. Likewise, it can be difficult to accurately monitor complex payment histories with thousands of items on them. Another issue is the difficulty that is introduced when making periodic reconciliations based on separate recording systems. At a broader level, some organizations may hesitate to do business with new strange companies or oversea companies for the first time because they do not trust that the other organization can deliver what they promise. Such problems cost organizations a lot of money, effort, and time to solve. However, Blockchain technology, first developed in 2009, could revolutionize how the business community deals with these common problems. The shared and immutable ledger on Blockchain can help organizations to keep track on transactions, manage the contracts in a smarter way, ensure correct purchase history records, eliminate the periodically reconciliation processes, and provide visibility for real-time transactions.
ContributorsHuynh, Phu Thanh (Author) / Popova, Laura (Thesis director) / Pankaj, Sneha (Committee member) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
As climate change and air pollution continue to plague the world today, committed citizens are doing their part to minimize their environmental impact. However, financial limitations have hindered a majority of individuals from adopting clean, renewable energy such as rooftop photovoltaic solar systems. England Sustainability Consulting plans to reverse this limitation and increase affordability for residents across Northern California to install solar panel systems for their energy needs. The purpose of this proposal is to showcase a new approach to procuring solar panel system components while offering the same products needed by each customer. We will examine market data to further prove the feasibility of this business approach while remaining profitable and spread our company's vision across all of Northern California.
ContributorsEngland, Kaysey (Author) / Dooley, Kevin (Thesis director) / Keahey, Jennifer (Committee member) / Department of Supply Chain Management (Contributor) / School of Social and Behavioral Sciences (Contributor) / W.P. Carey School of Business (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The National Basketball Association (NBA) is one of the Big Four Sporting Leagues of US Professional Sports. In recent years, the NBA has enjoyed milestone seasons in both attendance and television ratings, resulting in steady increases to both, over the previous decade. (Morgan, 2017) This surge can be attributed in part to the integration of "cultural recognition" initiatives and the overall message of inclusivity on the part of NBA franchises, with their respective promotions and advertisements such as television, social media, radio, etc. Heritage Nights, such as "Noche Latina," among other variants in the NBA, typically feature culturally influenced changes to team logos, giveaways, and other consumer offerings. In markets where Hispanics make up a significant percentage of the fan-base, such as Phoenix, NBA franchises such as the Phoenix Suns must ascertain the financial or perceptual impacts, associated with risks of stereotyping, offending or otherwise unintentionally alienating different categories of fans. To this end, data was collected from the local NBA franchises' fanbase, specifically Phoenix Suns season-ticket holders, and was statistically checked for significant relationships between both categories of fans and several different variables. This analysis found that only $192K in revenue is being missed through the investment of Heritage Nights, and that fan perceptions of stereotypical or offensive giveaways and practices have no significant effect on game or event attendance, despite the stereotypes toward giveaways and practices still being present. Implications of this study provide possible next steps for the Suns and continue to widen the scope of demographical sports marketing both in professional basketball and beyond.
ContributorsGibbens, Patrick Alexander (Author) / Eaton, John (Thesis director) / McIntosh, Daniel (Committee member) / Department of Supply Chain Management (Contributor) / School of Music (Contributor) / Department of Marketing (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Sagebrush Coffee is a small business in Chandler, Arizona that purchases green beans, roasts them in small batches for quality, and ships fresh, gourmet roasted coffee beans across the nation. Deciding which coffee beans to buy and roast is one of the most crucial business decisions Sagebrush and other gourmet coffee roasters face. Further complicating this decision is the fact that coffee is a crop, and like all crops, has a specific growing season and the exact same product cannot usually be ordered from year to year, even if it proves to be successful. The goal of this research is to use data analytics and visualization to help Sagebrush make better purchasing decisions by identifying consumer purchasing trends and providing a recommendation for their portfolio mix. In the end, I found that Latin American coffees are popular with both returning and first-time customers, but a specific country of origin does not appear to be associated with the top coffee producing countries. Additionally, December is a critical month for Sagebrush and Sagebrush should make sure to target the states with the most sales: California, Pennsylvania, and New York. Arizona has growth potential as it is not one of the top three locations, despite the presence of a physical store. Also included in the following report is a portfolio recommendation suggesting how many of each product based on region, processing type, and roast level to carry in inventory.
ContributorsBlue, Jessica Morgan (Author) / Kellso, James (Thesis director) / Davila, Eddie (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (Contributor) / Department of Supply Chain Management (Contributor) / Morrison School of Agribusiness (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05