Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical–statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.

Cognitive dissonance and the effect of acceptance of methodology, beliefs about religion, religious orientation, and dogmatism on the success of university students in a religious studies course

The problem investigated was negative effects on the ability of a university student to successfully complete a course in religious studies resulting from conflict between the methodologies and objectives of religious studies and the student's system of beliefs. Using Festinger's theory of cognitive dissonance as a theoretical framework, it was hypothesized that completing a course with a high level of success would be negatively affected by (1) failure to accept the methodologies and objectives of religious studies (methodology), (2) holding beliefs about religion that had potential conflicts with the methodologies and objectives (beliefs), (3) extrinsic religiousness, and (4) dogmatism. The causal comparative method was used. The independent variables were measured with four scales employing Likert-type items. An 8-item scale to measure acceptance of the methodologies and objectives of religious studies and a 16-item scale to measure holding of beliefs about religion having potential conflict with the methodologies were developed for this study. These scales together with a 20-item form of Rokeach's Dogmatism Scale and Feagin's 12-item Religious Orientation Scale to measure extrinsic religiousness were administered to 144 undergraduate students enrolled in randomly selected religious studies courses at Florida International University. Level of success was determined by course grade with the 27% of students receiving the highest grades classified as highly successful and the 27% receiving the lowest grades classified as not highly successful. A stepwise discriminant analysis produced a single significant function with methodology and dogmatism as the discriminants. Methodology was the principal discriminating variable. Beliefs and extrinsic religiousness failed to discriminate significantly. It was concluded that failing to accept the methodologies and objectives of religious studies and being highly dogmatic have significant negative effects on a student's success in a religious studies course. Recommendations were made for teaching to diminish these negative effects.

Psychosocial variables associated with the co-occurrence of risky sexual behavior with alcohol consumption among university students

This study examined the association of theoretically guided and empirically identified psychosocial variables on the co-occurrence of risky sexual behavior with alcohol consumption among university students. The study utilized event analysis to determine whether risky sex occurred during the same event in which alcohol was consumed. Relevant conceptualizations included alcohol disinhibition, self-efficacy, and social network theories. Predictor variables included negative condom attitudes, general risk taking, drinking motives, mistrust, social group membership, and gender. Factor analysis was employed to identify dimensions of drinking motives. Measured risky sex behaviors were (a) sex without a condom, (b) sex with people not known very well, (c) sex with injecting drug users (IDUs), (d) sex with people without knowing whether they had a STD, and (e) sex with using drugs. A purposive sample was used and included 222 male and female students recruited from a major urban university. Chi-square analysis was used to determine whether participants were more likely to engage in risky sex behavior in different alcohol use contexts. These contexts were only when drinking, only when not drinking, and when drinking or not. The chi-square findings did not support the hypothesis that university students who use alcohol with sex will engage in riskier sex. These results added to the literature by extending other similar findings to a university student sample. For each of the observed risky sex behaviors, discriminant analysis methodology was used to determine whether the predictor variables would differentiate the drinking contexts, or whether the behavior occurred. Results from discriminant analyses indicated that sex with people not known very well was the only behavior for which there were significant discriminant functions. Gender and enhancement drinking motives were important constructs in the classification model. Limitations of the study and implications for future research, social work practice and policy are discussed. ^

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. ^

Predictors of first semester attrition and their relation to retention of generic associate degree nursing students

In community college nursing programs the high rate of attrition was a major concern to faculty and administrators. Since first semester attrition could lead to permanent loss of students and low retention in nursing programs, it was important to identify at-risk students early and develop proactive approaches to assist them to be successful. The goal of nursing programs was to graduate students who were eligible to take the national council licensing examination (RN). This was especially important during a time of critical shortage in the nursing workforce. ^ This study took place at a large, multi-campus community college, and used Tinto's (1975) Student Integration Model of persistence as the framework. A correlational study was conducted to determine whether the independent variables, past academic achievement, English proficiency, achievement tendency, weekly hours of employment and financial resources, could discriminate between the two grade groups, pass and not pass. Establishing the relationship between the selected variables and successful course completion might be used to reduce attrition and improve retention. Three research instruments were used to collect data. A Demographic Information form developed by the researcher was used to obtain academic data, the research questionnaire Measure of Achieving Tendency measured achievement motivation, and the Test of Adult Basic Education (TABE), Form 8, Level A, Tests 1, 4, and 5 measured the level of English proficiency. The Department of Nursing academic policy, requiring a minimum course grade of “C” or better was used to determine the final course outcome. A stepwise discriminant analysis procedure indicated that college language level and pre-semester grade point average were significant predictors of final course outcome. ^ Based on the findings of the study recommendations focused on assessing students' English proficiency prior to admission into the nursing program, an intensive remediation plan in language comprehension for at-risk students, and the selection of alternate textbooks and readings that more closely matched the English proficiency level of the students. A pilot study should be conducted to investigate the benefit of raising the admission grade point average. ^

In vivo characterization of myocardial tissue post myocardial infarction using combined fluorescence and diffuse reflectance spectroscopy

Accurately assessing the extent of myocardial tissue injury induced by Myocardial infarction (MI) is critical to the planning and optimization of MI patient management. With this in mind, this study investigated the feasibility of using combined fluorescence and diffuse reflectance spectroscopy to characterize a myocardial infarct at the different stages of its development. An animal study was conducted using twenty male Sprague-Dawley rats with MI. In vivo fluorescence spectra at 337 nm excitation and diffuse reflectance between 400 nm and 900 nm were measured from the heart using a portable fiber-optic spectroscopic system. Spectral acquisition was performed on (1) the normal heart region; (2) the region immediately surrounding the infarct; and (3) the infarcted region—one, two, three and four weeks into MI development. The spectral data were divided into six subgroups according to the histopathological features associated with various degrees/severities of myocardial tissue injury as well as various stages of myocardial tissue remodeling, post infarction. Various data processing and analysis techniques were employed to recognize the representative spectral features corresponding to various histopathological features associated with myocardial infarction. The identified spectral features were utilized in discriminant analysis to further evaluate their effectiveness in classifying tissue injuries induced by MI. In this study, it was observed that MI induced significant alterations (p < 0.05) in the diffuse reflectance spectra, especially between 450 nm and 600 nm, from myocardial tissue within the infarcted and surrounding regions. In addition, MI induced a significant elevation in fluorescence intensities at 400 and 460 nm from the myocardial tissue from the same regions. The extent of these spectral alterations was related to the duration of the infarction. Using the spectral features identified, an effective tissue injury classification algorithm was developed which produced a satisfactory overall classification result (87.8%). The findings of this research support the concept that optical spectroscopy represents a useful tool to non-invasively determine the in vivo pathophysiological features of a myocardial infarct and its surrounding tissue, thereby providing valuable real-time feedback to surgeons during various surgical interventions for MI.

Paleoenvironmental reconstruction of Florida Bay, South Florida, using benthic foraminifera

Efforts that are underway to rehabilitate the Florida Bay ecosystem to a more natural state are best guided by a comprehensive understanding of the natural versus human-induced variability that has existed within the ecosystem. Benthic foraminifera, which are well-known paleoenvironmental indicators, were identified in 203 sediment samples from six sediment cores taken from Florida Bay, and analyzed to understand the environmental variability through anthropogenically unaltered and altered periods. In this research, taxa serving as indicators of (1) seagrass abundance (which is correlated with water quality), (2) salinity, and (3) general habitat change, were studied in detail over the past 120 years, and more generally over the past ~4000 years. Historical seagrass abundance was reconstructed with the proportions of species that prefer living attached to seagrass blades over other substrates. Historical salinity trends were determined by analyzing brackish versus marine faunas, which were defined based on species’ salinity preferences. Statistical methods including cluster analysis, discriminant analysis, analysis of variance and Fisher’s α were used to analyze trends in the data. The changes in seagrass abundance and salinity over the last ~120 years are attributed to anthropogenic activities such as construction of the Flagler Railroad from the mainland to the Florida Keys, the Tamiami Trail that stretches from the east to west coast, and canals and levees in south Florida, as well as natural events such as droughts and increased rainfall from hurricanes. Longer term changes (over ~4000 years) in seagrass abundance and salinity are mostly related to sea level changes. Since seawater entered the Florida Bay area around ~4000 years ago, only one probable sea level drop occurring around ~3000 years was identified.

Factors that differentiate persistence beyond the first session at a for-profit university

An increasing number of students are selecting for-profit universities to pursue their education (Snyder, Tan & Hoffman, 2006). Despite this trend, little empirical research attention has focused on these institutions, and the literature that exists has been classified as rudimentary in nature (Tierney & Hentschke, 2007). The purpose of this study was to investigate the factors that differentiated students who persisted beyond the first session at a for-profit university. A mixed methods research design consisting of three strands was utilized. Utilizing the College Student Inventory, student’s self-reported perceptions of what their college experience would be like was collected during strand 1. The second strand of the study utilized a survey design focusing on the beliefs that guided participants’ decisions to attend college. Discriminant analysis was utilized to determine what factors differentiated students who persisted from those who did not. A purposeful sample and semi-structured interview guide was used during the third strand. Data from this strand were analyzed thematically. Students’ self-reported dropout proneness, predicted academic difficulty, attitudes toward educators, sense of financial security, verbal confidence, gender and number of hours worked while enrolled in school differentiated students who persisted in their studies from those who dropped out. Several themes emerged from the interview data collected. Participants noted that financial concerns, how they would balance the demands of college with the demands of their lives, and a lack of knowledge about how colleges operate were barriers to persistence faced by students. College staff and faculty support were reported to be the most significant supports reported by those interviewed. Implications for future research studies and practice are included in this study.

A comprehensive study of bankruptcy prediction: Accounting ratios, market variables, and microstructure

Bankruptcy prediction has been a fruitful area of research. Univariate analysis and discriminant analysis were the first methodologies used. While they perform relatively well at correctly classifying bankrupt and nonbankrupt firms, their predictive ability has come into question over time. Univariate analysis lacks the big picture that financial distress entails. Multivariate discriminant analysis requires stringent assumptions that are violated when dealing with accounting ratios and market variables. This has led to the use of more complex models such as neural networks. While the accuracy of the predictions has improved with the use of more technical models, there is still an important point missing. Accounting ratios are the usual discriminating variables used in bankruptcy prediction. However, accounting ratios are backward-looking variables. At best, they are a current snapshot of the firm. Market variables are forward-looking variables. They are determined by discounting future outcomes. Microstructure variables, such as the bid-ask spread, also contain important information. Insiders are privy to more information that the retail investor, so if any financial distress is looming, the insiders should know before the general public. Therefore, any model in bankruptcy prediction should include market and microstructure variables. That is the focus of this dissertation. The traditional models and the newer, more technical models were tested and compared to the previous literature by employing accounting ratios, market variables, and microstructure variables. Our findings suggest that the more technical models are preferable, and that a mix of accounting and market variables are best at correctly classifying and predicting bankrupt firms. Multi-layer perceptron appears to be the most accurate model following the results. The set of best discriminating variables includes price, standard deviation of price, the bid-ask spread, net income to sale, working capital to total assets, and current liabilities to total assets.

Risk -taking propensity of faculty, administrators, and academic department heads at a large, urban, multi -campus community college

This research was undertaken to explore dimensions of the risk construct, identify factors related to risk-taking in education, and study risk propensity among employees at a community college. Risk-taking propensity (RTP) was measured by the 12-item BCDQ, which consisted of personal and professional risk-related situations balanced for the money, reputation, and satisfaction dimensions of the risk construct. Scoring ranged from 1.00 (most cautious) to 6.00 (most risky).^ Surveys including the BCDQ and seven demographic questions relating to age, gender, professional status, length of service, academic discipline, highest degree, and campus location were sent to faculty, administrators, and academic department heads. A total of 325 surveys were returned, resulting in a 66.7% response rate. Subjects were relatively homogeneous for age, length of service, and highest degree.^ Subjects were also homogeneous for risk-taking propensity: no substantive differences in RTP scores were noted within and among demographic groups, with the possible exception of academic discipline. The mean RTP score for all subjects was 3.77, for faculty was 3.76, for administrators was 3.83, and for department heads was 3.64.^ The relationship between propensity to take personal risks and propensity to take professional risks was tested by computing Pearson r correlation coefficients. The relationships for the total sample, faculty, and administrator groups were statistically significant, but of limited practical significance. Subjects were placed into risk categories by dividing the response scale into thirds. A 3 x 3 factorial ANOVA revealed no interaction effects between professional status and risk category with regard to RTP score. A discriminant analysis showed that a seven-factor model was not effective in predicting risk category.^ The homogeneity of the study sample and the effect of a risk-encouraging environment were discussed in the context of the community college. Since very little data on risk-taking in education is available, risk propensity data from this study could serve as a basis for comparison to future research. Results could be used by institutions to plan professional development activities, designed to increase risk-taking and encourage active acceptance of change. ^

Psychosocial variables associated with the co-occurance of risky sexual behavior with alcohol consumption among university students

This study examined the association of theoretically guided and empirically identified psychosocial variables on the co-occurrence of risky sexual behavior with alcohol consumption among university students. The study utilized event analysis to determine whether risky sex occurred during the same event in which alcohol was consumed. Relevant conceptualizations included alcohol disinhibition, self-efficacy, and social network theories. Predictor variables included negative condom attitudes, general risk taking, drinking motives, mistrust, social group membership, and gender. Factor analysis was employed to identify dimensions of drinking motives. Measured risky sex behaviors were (a) sex without a condom, (b) sex with people not known very well, (c) sex with injecting drug users (IDUs), (d) sex with people without knowing whether they had a STD, and (e) sex with using drugs. A purposive sample was used and included 222 male and female students recruited from a major urban university. Chi-square analysis was used to determine whether participants were more likely to engage in risky sex behavior in different alcohol use contexts. These contexts were only when drinking, only when not drinking, and when drinking or not. The chi-square findings did not support the hypothesis that university students who use alcohol with sex will engage in riskier sex. These results added to the literature by extending other similar findings to a university student sample. For each of the observed risky sex behaviors, discriminant analysis methodology was used to determine whether the predictor variables would differentiate the drinking contexts, or whether the behavior occurred. Results from discriminant analyses indicated that sex with people not known very well was the only behavior for which there were significant discriminant functions. Gender and enhancement drinking motives were important constructs in the classification model. Limitations of the study and implications for future research, social work practice and policy are discussed.

In Vivo Characterization of Myocardial Tissue Post Myocardial Infarction Using Combined Fluorescence and Diffuse Reflectance Spectroscopy

Accurately assessing the extent of myocardial tissue injury induced by Myocardial infarction (MI) is critical to the planning and optimization of MI patient management. With this in mind, this study investigated the feasibility of using combined fluorescence and diffuse reflectance spectroscopy to characterize a myocardial infarct at the different stages of its development. An animal study was conducted using twenty male Sprague-Dawley rats with MI. In vivo fluorescence spectra at 337 nm excitation and diffuse reflectance between 400 nm and 900 nm were measured from the heart using a portable fiber-optic spectroscopic system. Spectral acquisition was performed on - (1) the normal heart region; (2) the region immediately surrounding the infarct; and (3) the infarcted region - one, two, three and four weeks into MI development. The spectral data were divided into six subgroups according to the histopathological features associated with various degrees / severities of myocardial tissue injury as well as various stages of myocardial tissue remodeling, post infarction. Various data processing and analysis techniques were employed to recognize the representative spectral features corresponding to various histopathological features associated with myocardial infarction. The identified spectral features were utilized in discriminant analysis to further evaluate their effectiveness in classifying tissue injuries induced by MI. In this study, it was observed that MI induced significant alterations (p < 0.05) in the diffuse reflectance spectra, especially between 450 nm and 600 nm, from myocardial tissue within the infarcted and surrounding regions. In addition, MI induced a significant elevation in fluorescence intensities at 400 and 460 nm from the myocardial tissue from the same regions. The extent of these spectral alterations was related to the duration of the infarction. Using the spectral features identified, an effective tissue injury classification algorithm was developed which produced a satisfactory overall classification result (87.8%). The findings of this research support the concept that optical spectroscopy represents a useful tool to non-invasively determine the in vivo pathophysiological features of a myocardial infarct and its surrounding tissue, thereby providing valuable real-time feedback to surgeons during various surgical interventions for MI.

Risk-taking propensity of faculty, administrators and academic departament heads at large, urban, multicampus community college

This research was undertaken to explore dimensions of the risk construct, identify factors related to risk-taking in education, and study risk propensity among employees at a community college. Risk-taking propensity (RTP) was measured by the 12-item BCDQ, which consisted of personal and professional risk-related situations balanced for the money, reputation, and satisfaction dimensions of the risk construct. Scoring ranged from 1.00 (most cautious) to 6.00 (most risky). Surveys including the BCDQ and seven demographic questions relating to age, gender, professional status, length of service, academic discipline, highest degree, and campus location were sent to faculty, administrators, and academic department heads. A total of 325 surveys were returned, resulting in a 66.7% response rate. Subjects were relatively homogeneous for age, length of service, and highest degree. Subjects were also homogeneous for risk-taking propensity: no substantive differences in RTP scores were noted within and among demographic groups, with the possible exception of academic discipline. The mean RTP score for all subjects was 3.77, for faculty was 3.76, for administrators was 3.83, and for department heads was 3.64. The relationship between propensity to take personal risks and propensity to take professional risks was tested by computing Pearson r correlation coefficients. The relationships for the total sample, faculty, and administrator groups were statistically significant, but of limited practical significance. Subjects were placed into risk categories by dividing the response scale into thirds. A 3 X 3 factorial ANOVA revealed no interaction effects between professional status and risk category with regard to RTP score. A discriminant analysis showed that a seven-factor model was not effective in predicting risk category. The homogeneity of the study sample and the effect of a risk encouraging environment were discussed in the context of the community college. Since very little data on risk-taking in education is available, risk propensity data from this study could serve as a basis for comparison to future research. Results could be used by institutions to plan professional development activities, designed to increase risk-taking and encourage active acceptance of change.

Development and validation of an attitudinal-profiling tool for patients with asthma

Peer reviewed

Statistical methods for polyhedral shape classification with incomplete data - application to cryo-electron tomographic images

A certain type of bacterial inclusion, known as a bacterial microcompartment, was recently identified and imaged through cryo-electron tomography. A reconstructed 3D object from single-axis limited angle tilt-series cryo-electron tomography contains missing regions and this problem is known as the missing wedge problem. Due to missing regions on the reconstructed images, analyzing their 3D structures is a challenging problem. The existing methods overcome this problem by aligning and averaging several similar shaped objects. These schemes work well if the objects are symmetric and several objects with almost similar shapes and sizes are available. Since the bacterial inclusions studied here are not symmetric, are deformed, and show a wide range of shapes and sizes, the existing approaches are not appropriate. This research develops new statistical methods for analyzing geometric properties, such as volume, symmetry, aspect ratio, polyhedral structures etc., of these bacterial inclusions in presence of missing data. These methods work with deformed and non-symmetric varied shaped objects and do not necessitate multiple objects for handling the missing wedge problem. The developed methods and contributions include: (a) an improved method for manual image segmentation, (b) a new approach to 'complete' the segmented and reconstructed incomplete 3D images, (c) a polyhedral structural distance model to predict the polyhedral shapes of these microstructures, (d) a new shape descriptor for polyhedral shapes, named as polyhedron profile statistic, and (e) the Bayes classifier, linear discriminant analysis and support vector machine based classifiers for supervised incomplete polyhedral shape classification. Finally, the predicted 3D shapes for these bacterial microstructures belong to the Johnson solids family, and these shapes along with their other geometric properties are important for better understanding of their chemical and biological characteristics.