Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Supporting data-intensive environmental science research: data science skills for scientific practitioners of statistics(Montana State University - Bozeman, College of Letters & Science, 2020) Theobold, Allison Shay; Chairperson, Graduate Committee: Stacey Hancock; Stacey Hancock was a co-author of the article, 'How environmental science graduate students acquire statistical computing skills' in the journal 'Statistics education research journal' which is contained within this dissertation.; Stacey Hancock and Sara Mannheimer were co-authors of the article, 'Designing data science workshops for data-intensive environmental science research' submitted to the journal 'Journal of statistics education ' which is contained within this dissertation.; Stacey Hancock was a co-author of the article, 'Data science skills in data-intensive environmental science research: the case of Alicia and Ellie' submitted to the journal 'Harvard data science review' which is contained within this dissertation.The importance of data science skills for modern environmental science research cannot be understated, but graduate students in these fields typically lack these integral skills. Yet, over the last 20 years statistics preparation in these fields has grown to be considered vital, and statistics coursework has been readily incorporated into graduate programs. As 'data science' is the study of extracting value from data, the field shares a great deal of conceptual overlap with the field of Statistics. Thus, many environmental science degree programs expect students to acquire these data science skills in an applied statistics course. A gap exists, however, between the data science skills required for students' participation in the entire data analysis cycle as applied to independent research, and those taught in statistics service courses. Over the last ten years, environmental science and statistics educators have outlined the shape of the data science skills specific to research in their respective disciplines. Disappointingly, however, both sides of these conversations have ignored the area at the intersection of these fields, specifically the data science skills necessary for environmental science practitioners of statistics. This research focuses on describing the nature of environmental science graduate students' need for data science skills when engaging in the data analysis cycle, through the voice of the students. In this work, we present three qualitative studies, each investigating a different aspect of this need. First, we present a study describing environmental science students' experiences acquiring the computing skills necessary to implement statistics in their research. In-depth interviews revealed three themes in these students' paths toward computational knowledge acquisition: use of peer support, seeking out a 'singular consultant,' and learning through independent research. Motivated by the need for extracurricular opportunities for acquiring data science skills, next we describe research investigating the design and implementation of a suite of data science workshops for environmental science graduate students. These workshops fill a critical hole in the environmental science and statistics curricula, providing students with the skills necessary to retrieve, view, wrangle, visualize, and analyze their data. Finally, we conclude with research that works toward identifying key data science skills necessary for environmental science graduate students as they engage in the data analysis cycle.Item Bayesian computing and sampling design for partially-surveyed spatial point process models(Montana State University - Bozeman, College of Letters & Science, 2020) Flagg, Kenneth Allen; Chairperson, Graduate Committee: Andrew Hoegh; Andrew Hoegh and John Borkowski were co-authors of the article, 'Modeling partially-surveyed point process data: inferring spatial point intensity of geomagnetic anomalies' in the journal 'Journal of agricultural, biological, and environmental statistics' which is contained within this dissertation.; Andrew Hoegh was a co-author of the article, 'The integrated nested laplace approximation applied to spatial log-Gaussian Cox process models' submitted to the journal 'Journal of applied statistics' which is contained within this dissertation.; John Borkowski and Andrew Hoegh were co-authors of the article, 'Log-Gaussian Cox processes and sampling paths: towards optimal design' submitted to the journal 'Spatial statistics' which is contained within this dissertation.Spatial point processes model situations such as unexploded ordnance, plant and animal populations, and celestial bodies, where events occur at distinct points in space. Point process models describe the number and distribution of these events. These models have been mathematically understood for many decades, but have not been widely used because of computational challenges. Computing advances in the last 30 years have kept interest alive, with several breakthroughs circa 2010 that have made Bayesian spatial point process models practical for many applications. There is now interest in sampling, where the process is only observed in part of the study site. My dissertation work deals with sampling along paths, a standard feature of unexploded ordnance remediation studies. In this dissertation, I introduce a data augmentation procedure to adapt a Dirichlet process mixture model to sampling situations and I provide the first comparison of a variety of sampling designs with regard to their spatial prediction performance for spatial log-Gaussian Cox process (LGCP) models. The Dirichlet process model remains computationally expensive in the sampling case while the LGCP performs well with low computing time. The sampling design study shows that paths with regular spacing perform well, with corners and direction changes being helpful when the path is short.Item New statistical methods for analyzing proteomics data from affinity isolation lc-ms/ms experiments(Montana State University - Bozeman, College of Letters & Science, 2007) Sharp, Julia Lynn; Chairperson, Graduate Committee: John J. BorkowskiThe field of proteomics is exploding with statistical problems waiting to be explored. To obtain information on protein complexes, interactions between protein pairs is initially examined. This exploration is performed using `bait-prey' pro- tein pull-down assays that use a protein affnity agent and an LC-MS/MS (liquid chromatography-tandem mass-spectrometry)-based protein identifcation method. An experiment generates a protein association matrix wherein each column represents a sample from one bait protein, each row represents one prey protein and each cell contains a presence/absence association indicator. The prey protein presence/absence pattern is assessed with a Likelihood Ratio Test (LRT) and simulated LRT p-values. Fisher's Exact Test and a conditional frequency distribution test using generating functions are also used to assess the prey protein observation pattern. Based on the p-value, each prey protein is assigned a category (Specific or Non-Specific) and appraised with respect to the goal and design of the experiment. The Bayes' Odds is calculated for each prey-bait pair in the `Specific' category to estimate the posterior probability that two proteins interact and compared to an approach used by Gilchrist et al. [23].Item The use of computer algebra systems in a procedural algebra course to facilitate a framework for procedural understanding(Montana State University - Bozeman, College of Letters & Science, 2007) Harper, Jonathan Lee; Chairperson, Graduate Committee: Maurice BurkeThis dissertation study evaluated the implementation and effectiveness of an introductory algebra curriculum designed around a Framework for Procedural Understanding. A Computer Algebra System (CAS) was used as a tool to focus lessons on the Framework and help students gain a deeper, well-connected understanding of algebraic procedures. This research was conducted in response to the prevalence of remedial mathematics and addresses the need for students in remedial mathematics to have a successful learning experience. The curriculum was implemented in the Spring 2007 semester at a western land-grant university. In this quasi-experimental study, one section of introductory algebra was taught using the CAS/Framework curriculum. This treatment section was determined based on a pretest used to judge equivalency of groups. Data sources included procedural understanding assessments with follow-up student interviews, procedural skill exams, classroom observations, and a debriefing interview with the treatment instructor. Qualitative analysis of student and instructor interview transcripts was done to supplement independent observation reports to evaluate the implementation of the curriculum.