Graduate Theses & Dissertations

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Psychometric Properties of a Scale Developed from a Three-Factor Model of Social Competency
While existing models of emotional intelligence (EI) generally recognize the importance of social competencies (SC), there is a tendency in the literature to narrow the focus to competencies that pertain to the self. Given the experiential and perceptual differences between self- vs. other-oriented emotional abilities, this is an important limitation of existing EI models and assessment tools. This thesis explores the psychometric properties of a multidimensional model for SC. Chapter 1 describes the evolution of work on SCs in modern psychology and describes the multidimensional model of SC under review. Chapter 2 replicates this model across a variety of samples and explores the model’s construct validity via basic personality and EI constructs. Chapter 3 further explores the predictive validity of the SC measure within a group of project managers and several success and wellness variables. Chapter 4 examines potential applications for the model and suggestions for further research. Author Keywords: emotional intelligence, project management, social competency, work readiness
Cloud Versus Bare Metal
A comparison of two high performance computing clusters running on AWS and Sharcnet was done to determine which scenarios yield the best performance. Algorithm complexity ranged from O (n) to O (n3). Data sizes ranged from 195 KB to 2 GB. The Sharcnet hardware consisted of Intel E5-2683 and Intel E7-4850 processors with memory sizes ranging from 256 GB to 3072 GB. On AWS, C4.8xlarge instances were used, which run on Intel Xeon E5-2666 processors with 60 GB per instance. AWS was able to launch jobs immediately regardless of job size. The only limiting factors on AWS were algorithm complexity and memory usage, suggesting a memory bottleneck. Sharcnet had the best performance but could be hampered by the job scheduler. In conclusion, Sharcnet is best used when the algorithm is complex and has high memory usage. AWS is best used when immediate processing is required. Author Keywords: AWS, cloud, HPC, parallelism, Sharcnet
Predicting the Pursuit of Post-Secondary Education
Trait Emotional Intelligence (EI) includes competencies and dispositions related to identifying, understanding, using and managing emotions. Higher trait EI has been implicated in post-secondary success, and better career-related decision-making. However, there is no evidence for whether it predicts the pursuit of post-secondary education (PSE) in emerging adulthood. This study investigated the role of trait EI in PSE pursuit using a large, nationally-representative sample of Canadian young adults who participated in the National Longitudinal Survey for Children and Youth (NLSCY). Participants in this dataset reported on their PSE status at three biennial waves (age 20-21, 22-23, and 24-25), and completed a four-factor self-report scale for trait EI (Emotional Quotient Inventory: Mini) at ages 20-21 and 24-25. Higher trait EI subscale scores were significantly associated with greater likelihood of PSE participation both concurrently, and at 2- and 4-year follow-ups. Overall, these associations were larger for men than women. Trait EI scores also showed moderate levels of temporal stability over four years, including full configural and at least partial metric invariance between time points. This suggests that the measure stays conceptually consistent over the four years of emerging adulthood, and that trait EI is a relatively malleable attribute, susceptible to change with interventions during this age period. Author Keywords: Emerging Adulthood, Longitudinal, Post-Secondary Pursuit, Trait Emotional Intelligence
An Ethical Analysis of Bell's Targeted Ad Prorgram
Online behavioural advertising (OBA) is an advertising technique which relies on collected customer information and online activity to serve people with more relevant ads. On November 16th, 2013, Bell Canada launched their first OBA program via Bell Mobility: the Bell Targeted Ads Program, or BTAP. My thesis provides an ethical analysis of BTAP and shows that Bell undermined and violated customer privacy, stifled customer autonomy, and harmed customer identity. Relevant moral problems include typification, a disrespecting of customer autonomy, and identity commodification. I show that BTAP was unethical by grounding my arguments within the moral framework of Information Ethics (IE). IE is an ethical system which focuses on the role of information in the ethical dilemmas. IE also justifies the self-constitutive theory of privacy (SCP) which argues that our information and privacy are entangled with our identities. This gives us strong reason to defend our privacy/identity within BTAP. After making several arguments which demonstrate that BTAP was unethical, I will then turn my attention to showing how it is possible to rectify and mitigate many of BTAP’s ethical problems by installing a two-stage opt-in (TSOI) which provides customers with a greater deal of autonomy, and the ability to remove themselves from BTAP. Author Keywords: Bell Canada, Ethics, Identity, Online Behavioural Advertising, Privacy, Targeted Advertising
Range-Based Component Models for Conditional Volatility and Dynamic Correlations
Volatility modelling is an important task in the financial markets. This paper first evaluates the range-based DCC-CARR model of Chou et al. (2009) in modelling larger systems of assets, vis-à-vis the traditional return-based DCC-GARCH. Extending Colacito, Engle and Ghysels (2011), range-based volatility specifications are then employed in the first-stage of DCC-MIDAS conditional covariance estimation, including the CARR model of Chou et al. (2005). A range-based analog to the GARCH-MIDAS model of Engle, Ghysels and Sohn (2013) is also proposed and tested - which decomposes volatility into short- and long-run components and corrects for microstructure biases inherent to high-frequency price-range data. Estimator forecasts are evaluated and compared in a minimum-variance portfolio allocation experiment following the methodology of Engle and Colacito (2006). Some consistent inferences are drawn from the results, supporting the models proposed here as empirically relevant alternatives. Range-based DCC-MIDAS estimates produce efficiency gains over DCC-CARR which increase with portfolio size. Author Keywords: asset allocation, DCC MIDAS, dynamic correlations, forecasting, portfolio risk management, volatility
Agro-Ecological Zoning (AEZ) of Southern Ontario and the Projected Shifts Caused by Climate Change in the Long-term Future
This thesis proposes an agro-ecological zoning (AEZ) methodology of southern Ontario for the characterization and mapping of agro-ecological zones during the historical term (1981-2010), and their shifts into the long-term (2041-2070) projected climate period. Agro-ecological zones are homogenous areas with a unique combination of climate, soil, and landscape features that are important for crop growth. Future climate variables were derived from Earth System Models (EMSs) using a high emission climate forcing scenario from the Intergovernmental Panel on Climate Change 5th Assessment Report. The spatiotemporal shifts in agro-ecological zones with projected climate change are analyzed using the changes to the length of growing period (LGP) and crop heat units (CHU), and their manifestation in agro-climatic zones (ACZ). There are significant increases to the LGP and CHU into the long-term future. Two historical ACZs exist in the long-term future, and have decreased in area and shifted northward from their historical locations. Author Keywords: Agro-climatic Zones, Agro-ecological Zones, Agro-ecological Zoning, Climate Change, Crop Heat Units, Length of Growing Period
Disability-Mitigating Effects of Education on Post-Injury Employment Dynamics
Using data drawn from the Workplace Safety and Insurance Board’s (WSIB) Survey of Workers with Permanent Impairments, this thesis explores if and how the human capital associated with education mitigates the realized work-disabling effects of permanent physical injury. Using Cater’s (2000) model of post-injury adaptive behaviour and employment dynamics as the structural, theoretical, and interpretative framework, this thesis jointly studies, by injury type, the effects of education on both the post-injury probability of transitioning from non-employment into employment and the post-injury probability of remaining in employment once employed. The results generally show that, for a given injury type, other things being equal, higher levels of education are associated with higher probabilities of both obtaining and sustaining employment. Author Keywords: permanent impairment, permanent injury, post-injury employment
Machine Learning Using Topology Signatures For Associative Memory
This thesis presents a technique to produce signatures from topologies generated by the Growing Neural Gas algorithm. The generated signatures have the following characteristics: The signature's memory footprint is smaller than the "real object" and it represents a point in the n x m multidimensional space. Signatures can be compared based on Euclidean distance and distances between signatures provide measurements of differences between models. Signatures can be associated with a concept and then be used as a learning step for a classification algorithm. The signatures are normalized and vectorized to be used in a multidimensional space clustering. Although the technique is generic in essence, it was tested by classifying alphabet and numerical handwritten characters and 2D figures obtaining a good accuracy and precision. It can be used for many other purposes related to shapes and abstract typologies classification and associative memory. Future work could incorporate other classifiers. Author Keywords: Associative memory, Character recognition, Machine learning, Neural gas, Topological signatures, Unsupervised learning
Utilizing Class-Specific Thresholds Discovered by Outlier Detection
We investigated if the performance of selected supervised machine-learning techniques could be improved by combining univariate outlier-detection techniques and machine-learning methods. We developed a framework to discover class-specific thresholds in class probability estimates using univariate outlier detection and proposed two novel techniques to utilize these class-specific thresholds. These proposed techniques were applied to various data sets and the results were evaluated. Our experimental results suggest that some of our techniques may improve recall in the base learner. Additional results suggest that one technique may produce higher accuracy and precision than AdaBoost.M1, while another may produce higher recall. Finally, our results suggest that we can achieve higher accuracy, precision, or recall when AdaBoost.M1 fails to produce higher metric values than the base learner. Author Keywords: AdaBoost, Boosting, Classification, Class-Specific Thresholds, Machine Learning, Outliers
Modeling drought derivatives in arid regions
We propose a stochastic weather model based on temperature, precipitation, humidity and wind speed for Qatar, as a representative arid region, in order to obtain simulated values for a drought index. As a drought index, the Reconnaissance Drought Index (RDI) is commonly accepted in agriculture and is used to measure drought severity. It can be used to price weather derivatives to help farmers reduce nancial losses from drought. RDI, which is the ratio of precipitation to evapotranspiration, is calculated by considering crop growth stages. The use of dierent crop coecient value depending on the growth stage to calculate evapotranspiration can provide improved values for RDI. Additionally, six calculation methods for evapotranspiration using weather data are investigated to obtain accurate values for RDI. Author Keywords: Evapotranspiration, Markov chains, Mean reversion processes, Reconnaissance Drought Index, Stochastic dierential equations, Stochastic weather models
Effect of Listing a Stock on the S&P 500 Index on the Stock’s Volatility
This paper investigates the effect of listing a stock on the S&P 500 Index on the stock’s volatility, using various econometrics models: GARCH and EGARCH. The study mainly addresses three issues; firstly, it analyzes stock volatility in two sub-periods, secondly, it determines whether the announcement can account for the fluctuations in the price of the stock, and finally, it investigates the change in the stock’s variance. After isolating the effects of external and industry shock by using the returns on the S&P 500 Index as a proxy, the author finds evidence of structural change in the volatility of stocks after that stock is added to the index. Additionally, the existence of a dominant symmetric effect, which captures the response of volatility to news, indicate that following the onset of including the stock on the index, information flowing into the market increased. However, the rate at which old news is captured in price falls. The empirical evidence also suggests that on average a stocks variance falls and that the announcement to list a stock on the index has little effect on the stock’s price. Author Keywords: EGARCH, GARCH, S&P 500 Index, Symmetric Effect, Volatility
Smote and Performance Measures for Machine Learning Applied to Real-Time Bidding
In the context of Real-Time Bidding (RTB) the machine learning problems of imbalanced classes and model selection are investigated. Synthetic Minority Oversampling Technique (SMOTE) is commonly used to combat imbalanced classes but a shortcoming is identified. Use of a distance threshold is identified as a solution and testing in a live RTB environment shows significant improvement. For model selection, the statistical measure Critical Success Index (CSI) is modified to add emphasis on recall. This new measure (CSI-R) is empirically compared with other measures such as accuracy, lift, efficiency, true skill score, Heidke's skill score and Gilbert's skill score. In all cases CSI-R is shown to provide better application to the RTB industry. Author Keywords: imbalanced classes, machine learning, online advertising, performance measures, real-time bidding, SMOTE

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Graduate student theses produced at Trent University. You may also browse theses and dissertations by year, program, or author.

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