Matching Items (43,913)
Description
The objective of this study was to understand domestic and foreign-born housekeeper's individual perceptions of labor mobility and job satisfaction related to their jobs within the hospitality industry. Literature regarding the bridging of tourism, immigration, and labor supply was addressed to expose broad conceptual frameworks that lead to the development of this study. More specifically, literature regarding labor mobility within tourism industries, migrant decision making, and barriers to mobility and immigration helped to construct a narrowed conceptual framework specific to hospitality labor in Phoenix, Arizona. Similar and previous studies focused on perceived labor mobility during significant economic or industry shifts. This study included the addition of a policy factor to help determine to what degree state policy change effected hospitality workers' perceived labor mobility. Arizona's recently passed and implemented legislative act SB1070 regards immigrant identification and employment, and enforcement of the act in the state of Arizona; this serves as the implicated policy change. Data were collected via on-site survey administered February to May 2011. An overall score was created for the five motivational dimensions: 1 — Status; 2 — Economic; 3 — Refugee; 4 — Entrepreneurial; and, 5 — Political using principle component factor analysis using a varimax rotation with Kaiser normalization. Theory and literature suggest that the economic advancement, status advancement, and the refugee orientation are effective explanatory variables for motivating a career move into the tourism industry. A total of 82 questionnaires were delivered and completed (N = 82), and none were eliminated. The statistically-determined Economic Dimension was characterized by eleven statements explained 51% of the variation and was the overwhelming motivational force. The average coded response for change in job satisfaction was very positive at .75. Ten features of changes in job satisfaction were used as the basis of the second measure of change in job satisfaction. The first Principle Component of the ten features of job satisfaction change explained 45% of the variation in these features and loadings were positive near or above 0.60 for all items. The relationship between variations in each of the measurements of change in job satisfaction and motivating factors was explored using regression analysis. The two dependent variables were Overall Change and First Principle Component, and the independent variables for both regressions included the four motivating factors as measured by the rotated factors scores to represent dimensions of Economic, Status, Refugee and Entrepreneurial. In addition to the motivational factors, four demographic variables were included as independent variables to account for personal and situational differences. None of the regression coefficients were significant at even the 10% level. Although this result was expected, the positive sign of regression coefficients suggest that expectations of working as a housekeepers results in a positive outcome. Understanding this relationship further is necessary, and seeking larger sample sizes over a longer period of time would be most beneficial to this field of research.
ContributorsCasson, Mallory (Author) / Tyrrell, Timothy (Thesis advisor) / Budruk, Megha (Committee member) / Li, Wei (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study explores how newspapers framed the weight-loss drugs Xenical®(orlistat) and Alli® (over-the-counter orlistat) during the time period of three months prior to their approvals by the U.S. Food and Drug Administration until one year after each became available on the market. As of June 2011, orlistat is the only weight-loss drug available for long-term use in the U.S. Newspapers are influential sources of information about health issues. Agenda-setting, framing, and priming in news articles can have a powerful effect on public perceptions and behaviors. To conduct the content analysis, researchers first developed a codebook containing variables that described the sources of attribution and the features of each drug. They tested the codebook in a series of pilot tests to ensure inter-rater reliability. The sample of texts for the content analysis, drawn from LexisNexis Academic, contained 183 newspaper articles composed of 85 Xenical articles and 98 Alli articles. The overlap was 25% for inter-rater reliability as well as intra-rater reliability. Frequencies were tabulated using Predictive Analytics SoftWare, version 18.0.3. Results demonstrated that Xenical and Alli were framed differently in some critical ways. For example, there were twice as many quotes from the manufacturer for Alli than for Xenical. Researchers concluded that the reporting on Alli was heavily influenced by the manufacturer's multi-media public relations campaign in the months prior to the market-release date.
ContributorsLehmann, Jessica (Author) / Hampl, Jeffrey S. (Thesis advisor) / Bramlett-Solomon, Sharon (Committee member) / Hall, Richard (Committee member) / Arizona State University (Publisher)
Created2011
Description
A perceived link between illegal immigration and crime continues to exist. Citizens continue to believe that immigration creates crime and fear that as the immigrant population grows, their safety is jeopardized. Not much research in the field of criminology, however, has focused on examining this perceived relationship between immigration and crime. Those studies which have examined the relationship have mainly relied on official data to conduct their analysis. The purpose of this thesis is to examine the relationship between immigration and crime by examining self report data as well as some official data on immigration status and criminal involvement. More specifically, this thesis examines the relationship between immigration status and four different types of criminal involvement; property crimes, violent crimes, drug sales, and drug use. Data from a sample of 1,990 arrestees in the Maricopa County, Arizona, was used to conduct this analysis. This data was collected through the Arizona Arrestee Reporting Information Network over the course of a year. The results of the logistic regression models indicate that immigrants tend to commit less crime than U.S. citizens. Furthermore, illegal immigrants are significantly less likely than U.S. citizens to commit any of the four types of crimes, with the exception of powder cocaine use.
ContributorsNuño, Lidia E (Author) / Katz, Charles M. (Thesis advisor) / White, Michael D. (Committee member) / Decker, Scott H. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The current study analyzed existing data, collected under a previous U.S. Department of Education Reading First grant, to investigate the strength of the relationship between scores on the first- through third-grade Dynamic Indicators of Basic Early Literacy Skills - Oral Reading Fluency (DIBELS-ORF) test and scores on a reading comprehension test (TerraNova-Reading) administered at the conclusion of second- and third-grade. Participants were sixty-five English Language Learners (ELLs) learning to read in a school district adjacent to the U.S.-Mexico border. DIBELS-ORF and TerraNova-Reading scores were provided by the school district, which administers the assessments in accordance with state and federal mandates to monitor early literacy skill development. Bivariate correlation results indicate moderate-to-strong positive correlations between DIBELS-ORF scores and TerraNova-Reading performance that strengthened between grades one and three. Results suggest that the concurrent relationship between oral reading fluency scores and performance on standardized and high-stakes measures of reading comprehension may be different among ELLs as compared to non-ELLs during first- and second-grade. However, by third-grade the correlations approximate those reported in previous non-ELL studies. This study also examined whether the Peabody Picture Vocabulary Test (PPVT), a receptive vocabulary measure, could explain any additional variance on second- and third-grade TerraNova-Reading performance beyond that explained by the DIBELS-ORF. The PPVT was individually administered by researchers collecting data under a Reading First research grant prior to the current study. Receptive vocabulary was found to be a strong predictor of reading comprehension among ELLs, and largely overshadowed the predictive ability of the DIBELS-ORF during first-grade. Results suggest that receptive vocabulary scores, used in conjunction with the DIBELS-ORF, may be useful for identifying beginning ELL readers who are at risk for third-grade reading failure as early as first-grade.
ContributorsMillett, Joseph Ridge (Author) / Atkinson, Robert (Thesis advisor) / Blanchard, Jay (Committee member) / Thompson, Marilyn (Committee member) / Christie, James (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genomic and proteomic sequences, which are in the form of deoxyribonucleic acid (DNA) and amino acids respectively, play a vital role in the structure, function and diversity of every living cell. As a result, various genomic and proteomic sequence processing methods have been proposed from diverse disciplines, including biology, chemistry, physics, computer science and electrical engineering. In particular, signal processing techniques were applied to the problems of sequence querying and alignment, that compare and classify regions of similarity in the sequences based on their composition. However, although current approaches obtain results that can be attributed to key biological properties, they require pre-processing and lack robustness to sequence repetitions. In addition, these approaches do not provide much support for efficiently querying sub-sequences, a process that is essential for tracking localized database matches. In this work, a query-based alignment method for biological sequences that maps sequences to time-domain waveforms before processing the waveforms for alignment in the time-frequency plane is first proposed. The mapping uses waveforms, such as time-domain Gaussian functions, with unique sequence representations in the time-frequency plane. The proposed alignment method employs a robust querying algorithm that utilizes a time-frequency signal expansion whose basis function is matched to the basic waveform in the mapped sequences. The resulting WAVEQuery approach is demonstrated for both DNA and protein sequences using the matching pursuit decomposition as the signal basis expansion. The alignment localization of WAVEQuery is specifically evaluated over repetitive database segments, and operable in real-time without pre-processing. It is demonstrated that WAVEQuery significantly outperforms the biological sequence alignment method BLAST for queries with repetitive segments for DNA sequences. A generalized version of the WAVEQuery approach with the metaplectic transform is also described for protein sequence structure prediction. For protein alignment, it is often necessary to not only compare the one-dimensional (1-D) primary sequence structure but also the secondary and tertiary three-dimensional (3-D) space structures. This is done after considering the conformations in the 3-D space due to the degrees of freedom of these structures. As a result, a novel directionality based 3-D waveform mapping for the 3-D protein structures is also proposed and it is used to compare protein structures using a matched filter approach. By incorporating a 3-D time axis, a highly-localized Gaussian-windowed chirp waveform is defined, and the amino acid information is mapped to the chirp parameters that are then directly used to obtain directionality in the 3-D space. This mapping is unique in that additional characteristic protein information such as hydrophobicity, that relates the sequence with the structure, can be added as another representation parameter. The additional parameter helps tracking similarities over local segments of the structure, this enabling classification of distantly related proteins which have partial structural similarities. This approach is successfully tested for pairwise alignments over full length structures, alignments over multiple structures to form a phylogenetic trees, and also alignments over local segments. Also, basic classification over protein structural classes using directional descriptors for the protein structure is performed.
ContributorsRavichandran, Lakshminarayan (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Spanias, Andreas S (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created2011
Description
Meter-resolution topography gathered by LiDAR (Light Detection and Ranging) has become an indispensable tool for better understanding of many surface processes including those sculpting landscapes that record information about earthquake hazards for example. For this reason, and because of the spectacular representation of the phenomena that these data provide, it is appropriate to integrate these data into Earth science educational materials. I seek to answer the following research question: "will using the LiDAR topography data instead of, or alongside, traditional visualizations and teaching methods enhance a student's ability to understand geologic concepts such as plate tectonics, the earthquake cycle, strike-slip faults, and geomorphology?" In order to answer this question, a ten-minute introductory video on LiDAR and its uses for the study of earthquakes entitled "LiDAR: Illuminating Earthquake Hazards" was produced. Additionally, LiDAR topography was integrated into the development of an undergraduate-level educational activity, the San Andreas fault (SAF) earthquake cycle activity, designed to teach introductory Earth science students about the earthquake cycle. Both the LiDAR video and the SAF activity were tested in undergraduate classrooms in order to determine their effectiveness. A pretest and posttest were administered to introductory geology lab students. The results of these tests show a notable increase in understanding LiDAR topography and its uses for studying earthquakes from pretest to posttest after watching the video on LiDAR, and a notable increase in understanding the earthquake cycle from pretest to posttest using the San Andreas Fault earthquake cycle exercise. These results suggest that the use of LiDAR topography within these educational tools is beneficial for students when learning about the earthquake cycle and earthquake hazards.
ContributorsRobinson, Sarah Elizabeth (Author) / Arrowsmith, Ramon (Thesis advisor) / Reynolds, Stephen J. (Committee member) / Semken, Steven (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis research attempts to observe, measure and visualize the communication patterns among developers of an open source community and analyze how this can be inferred in terms of progress of that open source project. Here I attempted to analyze the Ubuntu open source project's email data (9 subproject log archives over a period of five years) and focused on drawing more precise metrics from different perspectives of the communication data. Also, I attempted to overcome the scalability issue by using Apache Pig libraries, which run on a MapReduce framework based Hadoop Cluster. I described four metrics based on which I observed and analyzed the data and also presented the results which show the required patterns and anomalies to better understand and infer the communication. Also described the usage experience with Pig Latin (scripting language of Apache Pig Libraries) for this research and how they brought the feature of scalability, simplicity, and visibility in this data intensive research work. These approaches are useful in project monitoring, to augment human observation and reporting, in social network analysis, to track individual contributions.
ContributorsMotamarri, Lakshminarayana (Author) / Santanam, Raghu (Thesis advisor) / Ye, Jieping (Thesis advisor) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
The antioxidant, antihistamine, and chemotactic properties of vitamin C provide the theoretical basis linking vitamin C supplementation to combating the common cold; yet, the clinical evidence is mixed. To date, vitamin C intervention trials have not systematically recorded cold symptoms daily or looked at fluctuations in plasma histamine over an extended period. Also, trials have not been conducted in individuals with marginal vitamin C status. This study examined the impact of vitamin C supplementation during cold season on specific cold symptoms in a population with low plasma vitamin C concentrations. Healthy young males who were not regular smokers or training for competitive sports between the ages of 18 and 35 with below average plasma vitamin C concentrations were stratified by age, body mass index, and vitamin C status into two groups: VTC (500 mg vitamin C capsule ingested twice daily) or CON (placebo capsule ingested twice daily). Participants were instructed to fill out the validated Wisconsin Upper Respiratory Symptom Survey-21 daily for 8 weeks. Blood was sampled at trial weeks 0, 4, and 8. Plasma vitamin C concentrations were significantly different by groups at study week 4 and 8. Plasma histamine decreased 4.2% in the VTC group and increased 17.4% in the CON group between study weeks 0 and 8, but these differences were not statistically significant (p>0.05). Total cold symptom scores averaged 43±15 for the VTC group compared to 148±36 for the CON group, a 244% increase in symptoms for CON participants versus VTC participants (p=0.014). Additionally, recorded symptom severity and functional impairment scores were lower in the VCT group than the CON group (p=0.031 and 0.058, respectively). Global perception of sickness was 65% lower in the VTC group compared to the CON group (p=0.022). These results suggest that 1000 mg of vitamin C in a divided dose daily may lower common cold symptoms, cold symptom severity, and the perception of sickness. More research is needed to corroborate these findings.
ContributorsOsterday, Gillean (Author) / Johnston, Carol (Thesis advisor) / Beezhold, Bonnie (Committee member) / Vaughan, Linda (Committee member) / Arizona State University (Publisher)
Created2012
Description
In recent years environmental life-cycle assessments (LCA) have been increasingly used to support planning and development of sustainable infrastructure. This study demonstrates the application of LCA to estimate embedded energy use and greenhouse gas (GHG) emissions related to materials manufacturing and construction processes for low and high density single-family neighborhoods typically found in the Southwest. The LCA analysis presented in this study includes the assessment of more than 8,500 single family detached units, and 130 miles of related roadway infrastructure. The study estimates embedded and GHG emissions as a function of building size (1,500 - 3000 square feet), number of stories (1 or 2), and exterior wall material composition (stucco, brick, block, wood), roof material composition (clay tile, cement tile, asphalt shingles, built up), and as a function of roadway typology per mile (asphalt local residential roads, collectors, arterials). While a hybrid economic input-out life-cycle assessment is applied to estimate the energy and GHG emissions impacts of the residential units, the PaLATE tool is applied to determine the environmental effects of pavements and roads. The results indicate that low density single family neighborhoods are 2 - 2.5 X more energy and GHG intensive, per residential dwelling (unit) built, than high density residential neighborhoods. This relationship holds regardless of whether the functional unit is per acre or per capita. The results also indicate that a typical low density neighborhood (less than 2 dwellings per acre) requires 78 percent more energy and resource in roadway infrastructure per residential unit than a traditional small lot high density (more than 6 dwelling per acre). Also, this study shows that new master planned communities tend to be more energy intensive than traditional non master planned residential developments.
ContributorsFrijia, Stephane (Author) / Guhathakurta, Subhrajit (Committee member) / Williams, Eric D. (Committee member) / Pijawka, David K (Committee member) / Arizona State University (Publisher)
Created2011