Previous research has tested the lasso method for DIF detection in dichotomous items, but limited research is available on this technique for polytomous items. This simulation study compares the lasso method to hybrid ordinal logistic regression to test performance in terms of TP and FP rates when considering sample size, test length, number of response categories, group balance, DIF proportion, and DIF magnitude. Results showed better Type I error control with the lasso, with smaller sample sizes, unbalanced groups, and weak DIF. The lasso also exhibited more stable Type I error control when DIF was weak, and groups were unbalanced. Lastly, low DIF proportion contributed to better Type I error control and higher TP rates with both methods.

This thesis is focused on extracting meaningful statistics for the characterization and classification of biological, medical, and financial data and contains four chapters. The first chapter contains theoretical background on scaling and wavelets, which supports the work in chapters two and three. In the second chapter, we outline a methodology for representing sequences of DNA nucleotides as numeric matrices in order to analytically investigate important structural characteristics of DNA. This methodology involves assigning unit vectors to nucleotides, placing the vectors into columns of a matrix, and accumulating across the rows of this matrix. Transcribing the DNA in this way allows us to compute the 2-D wavelet transformation and assess regularity characteristics of the sequence via the slope of the wavelet spectra. In addition to computing a global slope measure for a sequence, we can apply our methodology for overlapping sections of nucleotides to obtain an evolutionary slope. In the third chapter, we describe various ways wavelet-based scaling may be used for cancer diagnostics. There were nearly half of a million new cases of ovarian, breast, and lung cancer in the United States last year. Breast and lung cancer have highest prevalence, while ovarian cancer has the lowest survival rate of the three. Early detection is critical for all of these diseases, but substantial obstacles to early detection exist in each case. In this work, we use wavelet-based scaling on metabolic data and radiography images in order to produce meaningful features to be used in classifying cases and controls. Computer-aided detection (CAD) algorithms for detecting lung and breast cancer often focus on select features in an image and make a priori assumptions about the nature of a nodule or a mass. In contrast, our approach to analyzing breast and lung images captures information contained in the background tissue of images as well as information about specific features and makes no such a priori assumptions. In the fourth chapter, we investigate the value of social media data in building commercial default and activity credit models. We use random forest modeling, which has been shown in many instances to achieve better predictive accuracy than logistic regression in modeling credit data. This result is of interest, as some entities are beginning to build credit scores based on this type of publicly available online data alone. Our work has shown that the addition of social media data does not provide any improvement in model accuracy over the bureau only models. However, the social media data on its own does have some limited predictive power.

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This thesis explores and describes the relationship between emotional intelligence, job satisfaction and job self-efficacy. The sample was collected between 2007 and 2010 and consists of 1336 South Africans within the workplace. Trait emotional intelligence was assessed using the Trait Emotional Intelligence Questionnaire (TEIQue), while job satisfaction and job self-efficacy were assessed from the biographical questions asked during the TEIQue assessment process. The first hypothesis investigated whether there is a statistically significant relationship between job satisfaction and trait emotional intelligence. A relationship was found that is statistically, but not practically, significant. The second hypothesis centred on the relationship between job self-efficacy and emotional intelligence, with statistically significant results (p<0.001), and a weaker relationship than the one found between job satisfaction and scores on the TEIQue. The third hypothesis, investigating a possible interaction effect between job satisfaction and job self-efficacy, was rejected. In addition to the study’s three hypotheses, exploratory IRT analysis was conducted on a section of the TEIQue items in order to further explore the functioning of the test within the South African context. Findings suggest that there is a relationship between the constructs within the study, but that this relationship is more complex than first assumed, being affected by issues such as social desirability and central tendency bias. / Psychology / M.A. (Psychology)

Le sujet principal de cette thèse porte sur l'étude de l'estimation de la variance d'une statistique basée sur des données d'enquête imputées via le bootstrap (ou la méthode de Cyrano). L'application d'une méthode bootstrap conçue pour des données d'enquête complètes (en absence de non-réponse) en présence de valeurs imputées et faire comme si celles-ci étaient de vraies observations peut conduire à une sous-estimation de la variance. Dans ce contexte, Shao et Sitter (1996) ont introduit une procédure bootstrap dans laquelle la variable étudiée et l'indicateur de réponse sont rééchantillonnés ensemble et les non-répondants bootstrap sont imputés de la même manière qu'est traité l'échantillon original. L'estimation bootstrap de la variance obtenue est valide lorsque la fraction de sondage est faible. Dans le chapitre 1, nous commençons par faire une revue des méthodes bootstrap existantes pour les données d'enquête (complètes et imputées) et les présentons dans un cadre unifié pour la première fois dans la littérature. Dans le chapitre 2, nous introduisons une nouvelle procédure bootstrap pour estimer la variance sous l'approche du modèle de non-réponse lorsque le mécanisme de non-réponse uniforme est présumé. En utilisant seulement les informations sur le taux de réponse, contrairement à Shao et Sitter (1996) qui nécessite l'indicateur de réponse individuelle, l'indicateur de réponse bootstrap est généré pour chaque échantillon bootstrap menant à un estimateur bootstrap de la variance valide même pour les fractions de sondage non-négligeables. Dans le chapitre 3, nous étudions les approches bootstrap par pseudo-population et nous considérons une classe plus générale de mécanismes de non-réponse. Nous développons deux procédures bootstrap par pseudo-population pour estimer la variance d'un estimateur imputé par rapport à l'approche du modèle de non-réponse et à celle du modèle d'imputation. Ces procédures sont également valides même pour des fractions de sondage non-négligeables. / The aim of this thesis is to study the bootstrap variance estimators of a statistic based on imputed survey data. Applying a bootstrap method designed for complete survey data (full response) in the presence of imputed values and treating them as true observations may lead to underestimation of the variance. In this context, Shao and Sitter (1996) introduced a bootstrap procedure in which the variable under study and the response status are bootstrapped together and bootstrap non-respondents are imputed using the imputation method applied on the original sample. The resulting bootstrap variance estimator is valid when the sampling fraction is small. In Chapter 1, we begin by doing a survey of the existing bootstrap methods for (complete and imputed) survey data and, for the first time in the literature, present them in a unified framework. In Chapter 2, we introduce a new bootstrap procedure to estimate the variance under the non-response model approach when the uniform non-response mechanism is assumed. Using only information about the response rate, unlike Shao and Sitter (1996) which requires the individual response status, the bootstrap response status is generated for each selected bootstrap sample leading to a valid bootstrap variance estimator even for non-negligible sampling fractions. In Chapter 3, we investigate pseudo-population bootstrap approaches and we consider a more general class of non-response mechanisms. We develop two pseudo-population bootstrap procedures to estimate the variance of an imputed estimator with respect to the non-response model and the imputation model approaches. These procedures are also valid even for non-negligible sampling fractions.

The research in the field of travel demand modeling is driven by the need to understand individuals’ behavior in the context of travel-related decisions as accurately as possible. In this regard, the activity-based approach to modeling travel demand has received substantial attention in the past decade, both in the research arena as well as in practice. At the same time, recent efforts have been focused on more fully realizing the potential of activity-based models by explicitly recognizing the multi-dimensional nature of activity-travel decisions. However, as more behavioral elements/dimensions are added, the dimensionality of the model systems tends to explode, making the estimation of such models all but infeasible using traditional inference methods. As a result, analysts and practitioners often trade-off between recognizing attributes that will make a model behaviorally more representative (from a theoretical viewpoint) and being able to estimate/implement a model (from a practical viewpoint). An alternative approach to deal with the estimation complications arising from multi-dimensional choice situations is the technique of composite marginal likelihood (CML). This is an estimation technique that is gaining substantial attention in the statistics field, though there has been relatively little coverage of this method in transportation and other fields. The CML approach is a conceptually and pedagogically simpler simulation-free procedure (relative to traditional approaches that employ simulation techniques), and has the advantage of reproducibility of the results. Under the usual regularity assumptions, the CML estimator is consistent, unbiased, and asymptotically normally distributed. The discussion above indicates that the CML approach has the potential to contribute in the area of travel demand modeling in a significant way. For example, the approach can be used to develop conceptually and behaviorally more appealing models to examine individuals’ travel decisions in a joint framework. The overarching goal of the current research work is to demonstrate the applicability of the CML approach in the area of activity-travel demand modeling and to highlight the enhanced features of the choice models estimated using the CML approach. The goal of the dissertation is achieved in three steps as follows: (1) by evaluating the performance of the CML approach in multivariate situations, (2) by developing multidimensional choice models using the CML approach, and (3) by demonstrating applications of the multidimensional choice models developed in the current dissertation. / text

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This study examined the level of trust of information on social media. Specifically, I investigated the factors of performance expectancy with information-seeking motives that appear to influence the level of trust of information on various social network sites. This study utilized the following theoretical models: elaboration likelihood model (ELM), the uses and gratifications theory (UGT), the unified theory of acceptance and use of technology model (UTAUT), the consumption value theory (CVT), and the Stimulus-Organism-Response (SOR) Model to build a conceptual research framework for an exploratory study. The research investigated the extent to which information quality and source credibility influence the level of trust of information by visitors to the social network sites. The inductive content analysis on 189 respondents’ responses carefully addressed the proposed research questions and then further developed a comprehensive framework. The findings of this study contribute to the current research stream on information quality, fake news, and IT adoption as they relate to social media.

This thesis explores and describes the relationship between emotional intelligence, job satisfaction and job self-efficacy. The sample was collected between 2007 and 2010 and consists of 1336 South Africans within the workplace. Trait emotional intelligence was assessed using the Trait Emotional Intelligence Questionnaire (TEIQue), while job satisfaction and job self-efficacy were assessed from the biographical questions asked during the TEIQue assessment process. The first hypothesis investigated whether there is a statistically significant relationship between job satisfaction and trait emotional intelligence. A relationship was found that is statistically, but not practically, significant. The second hypothesis centred on the relationship between job self-efficacy and emotional intelligence, with statistically significant results (p<0.001), and a weaker relationship than the one found between job satisfaction and scores on the TEIQue. The third hypothesis, investigating a possible interaction effect between job satisfaction and job self-efficacy, was rejected. In addition to the study’s three hypotheses, exploratory IRT analysis was conducted on a section of the TEIQue items in order to further explore the functioning of the test within the South African context. Findings suggest that there is a relationship between the constructs within the study, but that this relationship is more complex than first assumed, being affected by issues such as social desirability and central tendency bias. / Psychology / M.A. (Psychology)

No description available.