Matching Items (155)
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
Few studies have examined the correlations between individual characteristics and other popular forms of social media other than Facebook. This study explored the ways emerging adults use Instagram and Snapchat and examined the relationships between social media and individual characteristics. A sample of 393 participants were recruited from a large university in the Southwestern United States. The participants completed an online questionnaire that included a newly developed social media measure along with established measures that examined the individual characteristics of social comparison orientation, self-esteem, loneliness, contingent self-worth, narcissism, and life satisfaction. In the present study, more participants reported having an active Instagram account than an active Facebook or Snapchat account. Additionally, a higher number of participants also reported preferring Instagram and Snapchat compared to Facebook. Significant correlations were found between various individual characteristics and three aspects of social media use: overall time spent on social media, whether the individual felt that their time spent on social media was meaningful, and how the individual felt emotionally after comparing themselves to others' photos and posts. Potential explanations and implications of the results are discussed.
ContributorsArndorfer, Sydney (Author) / Field, Ryan (Thesis director) / Sechler, Casey (Committee member) / School of Community Resources and Development (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The town of Guadalupe, Arizona has a long history of divided residents and high poverty rates. The high levels of poverty in the town can be attributed to numerous factors, most notably high rates of drug abuse, low high school graduation rates, and teen pregnancy. The town has named one of its most pressing issues of today to be youth disengagement. There are currently a handful of residents and community members passionate about finding a solution to this issue. After working with Guadalupe's Ending Hunger Task Force and resident youth, I set out to create a program design for a Guadalupe Youth Council. This council will contribute to combating youth disengagement. The program design will assist the task force in creating a standing youth council and deciding on the structure and role the council has in the town. I will offer learning outcomes and suggestions to the Task Force, youth council staff, and the youth of the youth council. This study contains an analysis of relevant literature, youth focus group results and data, and how the information gathered has contributed to the design of the youth council. The results of this study contain recommendations about four themes within the program design of a youth council: size, recruitment, activities and engagement, and adult support. The results also explore how the youth council will impact the power, policy, and behavior of Guadalupe youth.
ContributorsBalderas, Erica Theresa (Author) / Wang, Lili (Thesis director) / Avalos, Francisco (Committee member) / School of Community Resources and Development (Contributor) / Department of Information Systems (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Television is currently in a changing state. There is no longer a singular broadcast format for series to follow. Streaming websites such as Netflix, Hulu, and Amazon Prime now release series in their entirety; this is known as a full-season release (FSR). Viewers are now able to act independently and determine the pace they wish to watch a new FSR series. This not only affects how fans engage in social television discussions on social media, but also changes the previously proposed viewer engagement model. Whereas previous research suggests that fans follow a static linear engagement model consisting of pre-communication, parallel communication, and post communication phases, fans are now able to move freely through viewer engagement phases. This creates a new type of engagement model: The Atomized Engagement Model. As fans move freely through the atomized engagement phases, they choose social media platforms to engage in fandom discussion. Research suggests that although there are distinct types of posts that occur in relation to social television discussions, the platforms used have a direct effect on the content and length of the post.
ContributorsPouls, Samantha Rae (Author) / Gilpin, Dawn (Thesis director) / Barrett, Marianne (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Community Resources and Development (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The nonprofit sector has experienced exponential growth in recent decades, thus creating a separate industry for nonprofits—an industry that requires education and training to run efficiently and successfully. As a result, Nonprofit Management Education (NME) at both graduate and undergraduate levels has steadily increased in number and demand. Recent changes in the political climate and changes in the government funding present new challenges to nonprofit professionals, thus enhancing the value of specific NME to prepare professionals for these challenges. To leverage NME and ensure that students are adequately prepared for these challenges, it is important to design curriculum that addresses the needs of the growing nonprofit industry. The Nonprofit Academic Center of Councils is the creator of the NACC Curricular Guidelines, which are currently used as a model all NME curricula should emulate. This study utilizes Arizona State University (ASU) to compare its current curriculum model to the NACC Curricular Guidelines, as well as the current challenges facing the nonprofit sector. In so doing, this study will provide an in-depth overview of NME at ASU through 1) focus groups of nonprofit leaders; 2) survey data from former students; and 3) curriculum mapping.
The comprehensive results indicated areas of opportunity for both ASU and the NACC Curricular Guidelines. According to the feedback of students, nonprofit professionals, and the current state of the ASU curriculum, ASU may wish to increase emphasis on Financial Management, Managing Staff and Volunteers, Assessment, Evaluation, and Decision Making, and Leading and Managing Nonprofit Organizations. After considering feedback from nonprofit professionals, NACC may consider amending some new competencies that reflect an emphasis on collective impact, cross sector leadership, or relationship building and the use of technology for nonprofit impact. The research team recommends accomplishing these changes through enhancing pedagogy by including case studies and an integrated curriculum into the ASU NME program. by applying the suggested changes to both the ASU curriculum and the NACC guidelines, this research prepares both ASU and NACC towards the process of accreditation and formalizing the NLM degree on a national level.
The comprehensive results indicated areas of opportunity for both ASU and the NACC Curricular Guidelines. According to the feedback of students, nonprofit professionals, and the current state of the ASU curriculum, ASU may wish to increase emphasis on Financial Management, Managing Staff and Volunteers, Assessment, Evaluation, and Decision Making, and Leading and Managing Nonprofit Organizations. After considering feedback from nonprofit professionals, NACC may consider amending some new competencies that reflect an emphasis on collective impact, cross sector leadership, or relationship building and the use of technology for nonprofit impact. The research team recommends accomplishing these changes through enhancing pedagogy by including case studies and an integrated curriculum into the ASU NME program. by applying the suggested changes to both the ASU curriculum and the NACC guidelines, this research prepares both ASU and NACC towards the process of accreditation and formalizing the NLM degree on a national level.
ContributorsFindlay, Molly Rebecca (Author) / Legg, Eric (Thesis director) / Ashcraft, Robert (Committee member) / Department of Information Systems (Contributor) / School of Community Resources and Development (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Tempe Late Night is a student run weekly variety comedy show at Arizona State University. The show tapes weekly in front of a live student audience and publishes videos online. The show specifically tackles better representing student perspectives at ASU. Additionally, Tempe Late Night also strives to provide an un-censored real take on college life. Tempe Late Night focuses on reaching a broad audience of students, local and nationwide.
ContributorsShannon, Nicholas Forbes (Author) / Knopf, Richard (Thesis director) / Talmage, Craig (Committee member) / School of Criminology and Criminal Justice (Contributor) / School of Community Resources and Development (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The loss of a loved one through suicide is a traumatic life event that brings about considerable emotional turmoil. In the present study, the term suicide loss survivor refers to an individual who is a family member or a friend of a person who died by suicide. Through the three chosen methods of gathering data, which are online surveys, in person interviews, and photography sessions, researchers highlight the personal experience of thirty-three suicide loss survivors. Supported by these various methods of data collection are the unique issues that accompany the bereavement of a suicide loss. The areas of focus are the emotional trauma, social stigma, and postvention resources utilized or made available to suicide loss survivors. Throughout interviews with suicide loss survivors, some of whom also identified as Arizona State University students, an additional opportunity for research emerged. Participants identified that Arizona State University is not effectively providing suicide awareness and prevention materials and training to its community, including staff and students. Recommendations for how Arizona State University can improve their current processes are discussed in the conclusion. By implementing the recommendations of prevention and postvention care, it is possible to educate students and staff and, in turn, allow Arizona State University to foster a culture of empathy for existing suicide loss survivors, while working on decreasing the risk of future suicides. This creative project and narrative analysis was performed by two individuals who themselves are suicide loss survivors and have taken their personal experiences as a foundation for the project's need.
ContributorsStockwell, Anna (Co-author) / Lashinske, Angela (Co-author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Economics Program in CLAS (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / W. P. Carey School of Business (Contributor) / School of Community Resources and Development (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05