Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.

Differential item functioning (DIF) occurs when individuals from different groups who have equal levels of a latent trait fail to earn commensurate scores on a testing instrument. Type I error occurs when DIF-detection methods result in unbiased items being excluded from the test while a Type II error occurs when biased items remain on the test after DIF-detection methods have been employed. Both errors create potential issues of injustice amongst examinees and can result in costly and protracted legal action. The purpose of this research was to evaluate two methods for detecting DIF: logistic regression (LR) and Mantel-Haenszel (MH). To accomplish this, meta-analysis was employed to summarize Monte Carlo quantitative studies that used these methods in published and unpublished literature. The criteria employed for comparing these two methods were Type I error rates, the Type I error proportion, which was also the Type I error effect size measure, deviation scores, and power rates. Monte Carlo simulation studies meeting inclusion criteria, with typically 15 Type I error effect sizes per study, were compared to assess how the LR and MH statistical methods function to detect DIF. Studied variables included DIF magnitude, nature of DIF (uniform or non-uniform), number of DIF items, and test length. I found that MH was better at Type I error control while LR was better at controlling Type II error. This study also provides a valuable summary of existing DIF methods and a summary of the types of variables that have been manipulated in DIF simulation studies with LR and MH. Consequently, this meta-analysis can serve as a resource for practitioners to help them choose between LR and MH for DIF detection with regard to Type I and Type II error control, and can provide insight for parameter selection in the design of future Monte Carlo DIF studies.

Influenza is a public health problem that causes serious illness and deaths all over the world. Vaccination has been shown to be the most effective mean to prevent infection. The primary component of influenza vaccine is the weakened strains. Vaccination triggers the immune system to develop antibodies against those strains whose viral surface glycoprotein hemagglutinin (HA) is similar to that of vaccine strains. However, influenza vaccine must be updated annually since the antigenic structure of HA is constantly mutation. Hemagglutination inhibition (HI) assay is a laboratory procedure frequently applied to evaluate the antigenic relationships of the influenza viruses. It enables the World Health Organization (WHO) to recommend appropriate updates on strains that will most likely be protective against the circulating influenza strains. However, HI assay is labour intensive and time-consuming since it requires several controls for standardization. We use two machine-learning methods, i.e. Artificial Neural Network (ANN) and Logistic Regression, and a Mixed-Integer Optimization Model to predict antigenic variety. The ANN generalizes the input data to patterns inherent in the data, and then uses these patterns to make predictions. The logistic regression model identifies and selects the amino acid positions, which contribute most significantly to antigenic difference. The output of the logistic regression model will be used to predict the antigenic variants based on the predicted probability. The Mixed-Integer Optimization Model is formulated to find hyperplanes that enable binary classification. The performances of our models are evaluated by cross validation.

本文利用1999年，行政院衛生署家庭計畫研究所之「臺灣地區老人保健與生活問題長期追蹤調查系列研究調查資料」探討台灣地區老年人健康與住宅所有權之關係。實證方法採用binary logistic regression來探討模型一與模型二。模型一以三個健康指標來衡量老人健康狀態，第一個指標為主觀健康，即老年人認為本身是否健康，第二個指標為日常生活活動之限制 (ADLs)，第三個指標為日常生活工具性活動 (IADLs)。得出結果若健康指標為IADLs和 ADLs則住宅所有權屬於自己和配偶的會比較健康。但是若以主觀健康衡量之，則住宅所有權對健康沒有顯著影響。模型二將住宅所有權分為二類，第一類為廣義自有屋，第二類為狹義自有屋。若是住宅所有權為廣義自有屋，則健康狀況對住宅所有權並沒有顯著影響。住宅所有權為狹義自有屋時，ADLs和IADLs方面越健康者，則越可能持有狹義自有屋。

Thesis (Ph. D.)--Texas State University-San Marcos, 2007. / Vita. Includes bibliographical references (leaves 111-129).

Thesis (Ph.D.)--Baylor University, 2008. / Includes bibliographical references (p. 69-71)

Thesis (M.S.)--Duquesne University, 2004. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 26).

Thesis (PH. D.)--Michigan State University. Measurement and Quantitative Methods, 2008. / Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.

Thesis (M.S.)--Brigham Young University. Dept. of Audiology and Speech-Language Pathology, 2006. / Includes bibliographical references (p. 38-43).

Thesis (MSc. (Faculty of Health Sciences))--University of Pretoria, 2007. / Summary in English and Afrikaans. Includes bibliographical references.