Sequential Logistic Regression: A Method to Reveal Subtlety in Self-Efficacy

This paper uses self-efficacy to predict the success of women in introductory physics. We show how sequential logistic regression demonstrates the predictive ability of self-efficacy, and reveals variations with type of physics course. Also discussed are the sources of self-efficacy that have the largest impact on predictive ability.

Comparison of Some Improved Estimators for Linear Regression Model under Different Conditions

Multiple linear regression model plays a key role in statistical inference and it has extensive applications in business, environmental, physical and social sciences. Multicollinearity has been a considerable problem in multiple regression analysis. When the regressor variables are multicollinear, it becomes difficult to make precise statistical inferences about the regression coefficients. There are some statistical methods that can be used, which are discussed in this thesis are ridge regression, Liu, two parameter biased and LASSO estimators. Firstly, an analytical comparison on the basis of risk was made among ridge, Liu and LASSO estimators under orthonormal regression model. I found that LASSO dominates least squares, ridge and Liu estimators over a significant portion of the parameter space for large dimension. Secondly, a simulation study was conducted to compare performance of ridge, Liu and two parameter biased estimator by their mean squared error criterion. I found that two parameter biased estimator performs better than its corresponding ridge regression estimator. Overall, Liu estimator performs better than both ridge and two parameter biased estimator.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

Secondary stakeholders as agents of influence: Three essays on political risk, reputation, and multinational performance

Organizational researchers have recently taken an interest in the ways in which social movements, non-governmental organizations (NGOs), and other secondary stakeholders attempt to influence corporate behavior. Scholars, however, have yet to carefully probe the link between secondary stakeholder legal action and target firm stock market performance. This is puzzling given the sharp rise in NGO-initiated civil lawsuits against corporations in recent years for alleged overseas human rights abuses and environmental misconduct. Furthermore, few studies have considered how such lawsuits impact a target firm’s intangible assets, namely its image and reputation. Structured in the form of three essays, this dissertation examined the antecedents and consequences of secondary stakeholder legal activism in both conceptual and empirical settings. ^ Essay One argued that conventional approaches to understanding political risk fail to account for the reputational risks to multinational enterprises (MNEs) posed by transnational networks of human rights NGOs employing litigation-based strategies. It offered a new framework for understanding this emerging challenge to multinational corporate activity. Essay Two empirically tested the relationship between the filing of human rights-related civil lawsuits and corporate stock market performance using an event study methodology and regression analysis. The statistical analysis performed showed that target firms experience a significant decline in share price upon filing and that both industry and nature of the lawsuit are significantly and negatively related to shareholder wealth. Essay Three drew upon social movement and social identity theories to develop and test a set of hypotheses on how secondary stakeholder groups select their targets for human rights-related civil lawsuits. The results of a logistic regression model offered support for the proposition that MNE targets are chosen based on both interest and identity factors. The results of these essays suggest that legal action initiated by secondary stakeholder groups is a new and salient threat to multinational business and that firms doing business in countries with weak political institutions should factor this into corporate planning and take steps to mitigate their exposure to such risks.^

Gender differences in behavioral and psychosocial correlates of substance abuse among adolescents in residential treatment

This dissertation reports the results of a study that examined differences between genders in a sample of adolescents from a residential substance abuse treatment facility. The sample included 72 males and 65 females, ages 12 through 17. The data were archival, having been originally collected for a study of elopement from treatment. The current study included 23 variables. The variables were from multiple dimensions, including socioeconomic, legal, school, family, substance abuse, psychological, social support, and treatment histories. Collectively, they provided information about problem behaviors and psychosocial problems that are correlates of adolescent substance abuse. The study hypothesized that these problem behaviors and psychosocial problems exist in different patterns and combinations between genders.^ Further, it expected that these patterns and combinations would constitute profiles important for treatment. K-means cluster analysis identified differential profiles between genders in all three areas: problem behaviors, psychosocial problems, and treatment profiles. In the dimension of problem behaviors, the predominantly female group was characterized as suicidal and destructive, while the predominantly male group was identified as aggressive and low achieving. In the dimension of psychosocial problems, the predominantly female group was characterized as abused depressives, while the male group was identified as asocial, low problem severity. A third group, neither predominantly female or male, was characterized as social, high problem severity. When these dimensions were combined to form treatment profiles, the predominantly female group was characterized as abused, self-harmful, and social, and the male group was identified as aggressive, destructive, low achieving, and asocial. Finally, logistic regression and discriminant analysis were used to determine whether a history of sexual and physical abuse impacted problem behavior differentially between genders. Sexual abuse had a substantially greater influence in producing self-mutilating and suicidal behavior among females than among males. Additionally, a model including sexual abuse, physical abuse, low family support, and low support from friends showed a moderate capacity to predict unusual harmful behavior (fire-starting and cruelty to animals) among males. Implications for social work practice, social work research, and systems science are discussed. ^

The effects of cultural distance on student socialization and departure decisions

Organizational socialization theory and university student retention literature support the concept that social integration influences new recruits' level of satisfaction with the organization and their decision to remain. This three-phase study proposes and tests a Cultural Distance Model of student retention based on Tinto's (1975) Student Integration Model, Louis' (1980) Model of Newcomer Experience, and Kuh and Love's (2000) theory relating cultural distance to departure from the organization. ^ The main proposition tested in this study was that the greater the cultural distance, the greater the likelihood of early departure from the organization. Accordingly, it was inferred that new recruits entering the university culture experience some degree of social and psychological distance. The extent of the distance correspondingly influences satisfaction with the institution and intent to remain for subsequent years. ^ The model was tested through two freshman student surveys designed to examine the effects of cultural distance on non-Hispanics at a predominantly Hispanic, urban, public university. The first survey was administered eight weeks into their first Fall semester and the second at the end of their first year. Student retention was determined through their re-enrollment for the second Fall semester. Path analysis tested the viability of the hypothesis relating cultural distance to satisfaction and retention as suggested in the model. Logistic regression tested the model's predictive power. ^ Correlations among variables were significant, accounting for 54% of variance in students' decisions to return for the second year with 96% prediction accuracy. Initial feelings of high cultural distance were related to increased dissatisfaction with social interactions and institutional choice at the end of the first year and students' intention not to re-enroll. Path analysis results supported the view that the construct of culture distance incorporates both social and psychological distance, and is composed of beliefs of institutional fit with one's cultural expectations, individual comfort with the fit, and the consequent sense of “belonging” or identifying with the institution. ^

A study of moderating influences of adolescent alcohol drinking conditioned by perception of peer alcohol drinking

Despite a long history of prevention efforts and federal laws prohibiting the consumption of alcohol for those below the age of 21 years, underage drinking continues at both a high prevalence rate and high incidence rate. The purpose of this research study is to explain underage drinking of alcohol conditioned by perception of peer drinking. An acquisition model is conjectured and then a relationship within the model is explained with a national sample of students. From a developmental perspective, drinking alcohol is acquired in a reasonably ordered fashion that reflects the influences over time of the culture, family, and peers. The study measures perceptions of alcohol drinking during early adolescence when alcohol use begins the maintenance phase of the behavior. The correlation between drinking alcohol and perception of classmate drinking can be described via social learning theory. Simultaneously the moderating effects of grade level, gender, and race/ethnicity are used to explain differences between groups. Multilevel logistic regression was used to analyze the relations. The researcher found support for an association between adolescent drinking and perceptions of classmate drinking. Gender and grade level moderated the relation. African-Americans consistently demonstrated less drinking and less perception of classmate drinking than either whites or other students not white nor African-American. The importance of a better understanding of the process of acquiring drinking behaviors is discussed in relation to future research models with longitudinal data. ^

The theory of margin as a predictor of success on the National Council Licensure Examination for Registered Nurses

The National Council Licensure Examination for Registered Nurses (NCLEX-RN) is the examination that all graduates of nursing education programs must pass to attain the title of registered nurse. Currently the NCLEX-RN passing rate is at an all-time low (81%) for first-time test takers (NCSBN, 2004); amidst a nationwide shortage of registered nurses (Glabman, 2001). Because of the critical need to supply greater numbers of professional nurses, and the potential accreditation ramifications that low NCLEX-RN passing rates can have on schools of nursing and graduates, this research study tests the effectiveness of a predictor model. This model is based upon the theoretical framework of McClusky's (1959) theory of margin (ToM), with the hope that students found to be at-risk for NCLEX-RN failure can be identified and remediated prior to taking the actual licensure examination. To date no theory based predictor model has been identified that predicts success on the NCLEX-RN. ^ The model was tested using prerequisite course grades, nursing course grades and scores on standardized examinations for the 2003 associate degree nursing graduates at a urban community college (N = 235). Success was determined through the reporting of pass on the NCLEX-RN examination by the Florida Board of Nursing. Point biserial correlations tested model assumptions regarding variable relationships, while logistic regression was used to test the model's predictive power. ^ Correlations among variables were significant and the model accounted for 66% of variance in graduates' success on the NCLEX-RN with 98% prediction accuracy. Although certain prerequisite course grades and nursing course grades were found to be significant to NCLEX-RN success, the overall model was found to be most predictive at the conclusion of the academic program of study. The inclusion of the RN Assessment Examination, taken during the final semester of course work, was the most significant predictor of NCLEX-RN success. Success on the NCLEX-RN allows graduates to work as registered nurses, reflects positively on a school's academic performance record, and supports the appropriateness of the educational program's goals and objectives. The study's findings support potential other uses of McClusky's theory of margin as a predictor of program outcome in other venues of adult education. ^

Alcohol use and sexual victimization among Hispanic college women

Sexual victimization of young women typically occurs within a context of alcohol use, such that women are more likely to be victimized on days on which they consume alcohol compared to days on which no alcohol is consumed. Additionally, most research on sexual victimization of women has focused on forced sexual acts; consequently, little is known about forms sexual victimization that college women typically experience, such as brief (e.g., unwanted touching) or verbally coerced experiences (e.g., doing sexual things to prevent a partner from leaving). Finally, there is a need for more research on the processes underlying college women's drinking and the specific mechanisms through which drinking increases risk for sexual victimization. This dissertation sought to replicate recent findings of a temporal association between alcohol use and sexual victimization, and to investigate whether or not binge use increased risk for victimization, within a sample of young Hispanic college women, using repeated-measures logistic regression. This study also aimed to identify and explore typologies of victimization experiences in order to better understand types of sexual victimization common among young college women. Finally, the validity of a model of alcohol use and sexual victimization was investigated using structural equation modeling techniques. The results confirmed and extended previous research by demonstrating an increase in the conditional probability of sexual victimization on days of alcohol consumption compared with days of no alcohol consumption, and on days of binge alcohol consumption compared with days of moderate alcohol consumption. Sexual victimization experiences reported in this study were diverse, and cluster analysis was used to identify and explore specific typologies of victimization experiences, including intimate relationship victimization, brief victimization with stranger, prolonged victimization with acquaintance, and workplace victimization. The results from structural equation modeling (SEM) analyses were complex and helped to illuminate the relationships between reasons for drinking, alcohol use, childhood sexual abuse, sexual victimization, psychopathology, and acculturation-related factors among Hispanic college women. These findings have implications for the design of university-based prevention and intervention efforts aimed at reducing rates of alcohol-related sexual victimization within Hispanic populations.

Correlates of voluntary HIV testing and counseling among middle aged and older Latinas

This study identifies and describes HIV Voluntary Counseling and Testing (VCT) of middle aged and older Latinas. The rate of new cases of HIV in people age 45 and older is rapidly increasing, with a 40.6% increase in the numbers of older Latinas infected with HIV between 1998 and 2002. Despite this increase, there is paucity of research on this population. This research seeks to address the gap through a secondary data analysis of Latina women. The aim of this study is twofold: (1) Develop and empirically test a multivariate model of VCT utilization for middle aged and older Latinas; (2) To test how the three individual components of the Andersen Behavioral Model impact VCT for middle aged and older Latinas. The study is organized around the three major domains of the Andersen Behavioral Model of service use that include: (a) predisposing factors; (b) enabling characteristics and (c) need. Logistic regression using structural equation modeling techniques were used to test multivariate relationships of variables on VCT for a sample of 135 middle age and older Latinas residing in Miami-Dade County, Florida. Over 60% of participants had been tested for HIV. Provider endorsement was found to he the strongest predictor of VCT (odds ration [OR] 6.38), followed by having a clinic as a regular source of healthcare (OR=3.88). Significant negative associations with VCT included self rated health status (OR=.592); Age (OR=.927); Spanish proficiency (OR=.927); number of sexual partners (OR=.613) and consumption of alcohol during sexual activity (.549). As this line of inquiry provides a critical glimpse into the VCT of older Latinas, recommendations for enhanced service provision and research will he offered.

Cognitive and noncognitive variables that predict Florida community college radiography program graduates' success on the Registry

This study examined the predictive merits of selected cognitive and noncognitive variables on the national Registry exam pass rate using 2008 graduates (n = 175) from community college radiography programs in Florida. The independent variables included two GPAs, final grades in five radiography courses, self-efficacy, and social support. The dependent variable was the first-attempt results on the national Registry exam. The design was a retrospective predictive study that relied on academic data collected from participants using the self-report method and on perceptions of students' success on the national Registry exam collected through a questionnaire developed and piloted in the study. All independent variables except self-efficacy and social support correlated with success on the national Registry exam ( p < .01) using the Pearson Product-Moment Correlation analysis. The strongest predictor of the national Registry exam success was the end-of-program GPA, r = .550, p < .001. The GPAs and scores for self-efficacy and social support were entered into a logistic regression analysis to produce a prediction model. The end-of-program GPA (p = .015) emerged as a significant variable. This model predicted 44% of the students who failed the national Registry exam and 97.3% of those who passed, explaining 45.8% of the variance. A second model included the final grades for the radiography courses, self efficacy, and social support. Three courses significantly predicted national Registry exam success; Radiographic Exposures, p < .001; Radiologic Physics, p = .014; and Radiation Safety & Protection, p = .044, explaining 56.8% of the variance. This model predicted 64% of the students who failed the national Registry exam and 96% of those who passed. The findings support the use of in-program data as accurate predictors of success on the national Registry exam.

Predicting hurricane evacuation decisions: When, how many, and how far

Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. ^ Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. ^ Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. ^ All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation. ^

Contemporary aspects of dividends: Before and during the financial crisis

The number of dividend paying firms has been on the decline since the popularity of stock repurchases in the 1980s, and the recent financial crisis has brought about a wave of dividend reductions and omissions. This dissertation examined the U.S. firms and American Depository Receipts that are listed on the U.S. equity exchanges according to their dividend paying history in the previous twelve quarters. While accounting for the state of the economy, the firm’s size, profitability, earned equity, and growth opportunities, it determines whether or not the firm will pay a dividend in the next quarter. It also examined the likelihood of a dividend change. Further, returns of firms were examined according to their dividend paying history and the state of the economy using the Fama-French three-factor model. Using forward, backward, and step-wise selection logistic regressions, the results show that firms with a history of regular and uninterrupted dividend payments are likely to continue to pay dividends, while firms that do not have a history of regular dividend payments are not likely to begin to pay dividends or continue to do so. The results of a set of generalized polytomous logistic regressions imply that dividend paying firms are more likely to reduce dividend payments during economic expansions, as opposed to recessions. Also the analysis of returns using the Fama-French three factor model reveals that dividend paying firms are earning significant abnormal positive returns. As a special case, a similar analysis of dividend payment and dividend change was applied to American Depository Receipts that trade on the NYSE, NASDAQ, and AMEX exchanges and are issued by the Bank of New York Mellon. Returns of American Depository Receipts were examined using the Fama-French two-factor model for international firms. The results of the generalized polytomous logistic regression analyses indicate that dividend paying status and economic conditions are also important for dividend level change of American Depository Receipts, and Fama-French two-factor regressions alone do not adequately explain returns for these securities.