Association between mean residual life (MRL) and failure rate functions for continuous and discrete lifetime distributions

The purpose of this study was to correct some mistakes in the literature and derive a necessary and sufficient condition for the MRL to follow the roller-coaster pattern of the corresponding failure rate function. It was also desired to find the conditions under which the discrete failure rate function has an upside-down bathtub shape if corresponding MRL function has a bathtub shape. The study showed that if discrete MRL has a bathtub shape, then under some conditions the corresponding failure rate function has an upside-down bathtub shape. Also the study corrected some mistakes in proofs of Tang, Lu and Chew (1999) and established a necessary and sufficient condition for the MRL to follow the roller-coaster pattern of the corresponding failure rate function. Similarly, some mistakes in Gupta and Gupta (2000) are corrected, with the ensuing results being expanded and proved thoroughly to establish the relationship between the crossing points of the failure rate and associated MRL functions. The new results derived in this study will be useful to model various lifetime data that occur in environmental studies, medical research, electronics engineering, and in many other areas of science and technology.

The relationship of student and teacher variables on academic achievement in Florida schools

Since 1995, Florida has been one of the leading states in the country initiating a high-stakes school accountability system. Public schools in Florida receive letter grades based on their performance on the Florida Comprehensive Assessment Test (FCAT). These school grades have significant effects on schools' reputations and funding. Consequently, the plan has been criticized for grading all schools in the same manner, without taking into account such variables as student poverty and mobility rates which are beyond the control of the school. ^ The purpose of this study was to examine the relationship of student variables (poverty and mobility rates) and teacher variables (average years of teacher experience and attained degree level) on FCAT math and reading performance. This research utilized an education production function model to examine which set of inputs (student or teacher) has a stronger influence on student academic output as measured by the FCAT. ^ The data collected for this study was from over 1500 public elementary schools in Florida that listed all pertinent information for 2 school years (1998/1999 & 1999/2000) on the Florida Department of Education's website. ^ It was concluded that student poverty, teacher average years of experience and student mobility taken together, provide a strong predictive measure of FCAT reading and math performance. However, the set of student inputs was significantly stronger than the teacher inputs. High student poverty was highly correlated with low FCAT scores. Teacher experience and student mobility rates were not nearly as strongly related to FCAT scores as was student poverty. The results of this study provide evidence for educators and other school stakeholders of the relative degree to which student and teacher variables are related to student academic achievement. The underlying reasons for these relationships will require further examination in future studies. These results raise questions for Florida's school policymakers about the educational equity of the state's accountability system and its implementation. ^

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.

Class placement and academic and behavioral variables as predictors of graduation for students with disabilities

Dropout rates impacting students with high-incidence disabilities in American schools remain staggering (Bost, 2006; Hehir, 2005). Of this group, students with Emotional Behavioral Disorders (EBD) are at greatest risk. Despite the mandated national propagation of inclusion, students with EBD remain the least included and the least successful when included (Bost). Accordingly, this study investigated the potential significance of inclusive settings and other school-related variables within the context of promoting the graduation potential of students with Specific Learning Disabilities (SLD) or EBD. This mixed-methods study investigated specified school-related variables as likely dropout predictors, as well as the existence of first-order interactions among some of the variables. In addition, it portrayed the perspectives of students with SLD or EBD on the school-related variables that promote graduation. Accordingly, the sample was limited to students with SLD or EBD who had graduated or were close to graduation. For the quantitative component the numerical data were analyzed using linear and logistic regressions. For the qualitative component guided student interviews were conducted. Both strands were subsequently analyzed using Ridenour and Newman’s (2008) model where the quantitative hypotheses are tested and are later built-upon by the related qualitative meta-themes. Results indicated that a successful academic history, or obtaining passing grades was the only significant predictor of graduation potential when statistically controlling all the other variables. While at a marginal significance, results also yielded that students with SLD or EBD in inclusive settings experienced better academic results and behavioral outcomes than those in self-contained settings. Specifically, students with SLD or EBD in inclusive settings were found to be more likely to obtain passing grades and less likely to be suspended from school. Generally, the meta-themes yielded during the student interviews corroborated these findings as well as provided extensive insights on how students with disabilities view school within the context of promoting graduation. Based on the results yielded, provided the necessary academic accommodations and adaptations are in place, along with an effective behavioral program, inclusive settings can be utilized as drop-out prevention tools in special education.

Parental involvement and group cognitive behavioral treatment for anxiety disorders in children and adolescents: Treatment specificity and mediation effects of parent and peer variables

Phobic and anxiety disorders are one of the most common, if not the most common and debilitating psychopathological conditions found among children and adolescents. As a result, a treatment research literature has accumulated showing the efficacy of cognitive behavioral treatment (CBT) for reducing anxiety disorders in youth. This dissertation study compared a CBT with parent and child (i.e., PCBT) and child group CBT (i.e., GCBT). These two treatment approaches were compared due to the recognition that a child’s context has an effect on the development, course, and outcome of childhood psychopathology and functional status. The specific aims of this dissertation were to examine treatment specificity and mediation effects of parent and peer contextual variables. The sample consisted of 183 youth and their mothers. Research questions were analyzed using analysis of variance for treatment outcome, and structural equation modeling, accounting for clustering effects, for treatment specificity and mediation effects. Results indicated that both PCBT and GCBT produced positive treatment outcomes across all indices of change (i.e., clinically significant improvement, anxiety symptom reduction) and across all informants (i.e., youths and parents) with no significant differences between treatment conditions. Results also showed partial treatment specific effects of positive peer relationships in GCBT. PCBT also showed partial treatment specific effects of parental psychological control. Mediation effects were only observed in GCBT; positive peer interactions mediated treatment response. The results support the use CBT with parents and peers for treating childhood anxiety. The findings’ implications are further discussed in terms of the need to conduct further meditational treatment outcome designs in order to continue to advance theory and research in child and anxiety treatment.

Employee engagement: An examination of antecedent and outcome variables

This nonexperimental, correlational study (N = 283) examined the relation among job fit, affective commitment, psychological climate, discretionary effort, intention to turnover, and employee engagement. An internet-based self-report survey battery of six scales were administered to a heterogeneous sampling of organizations from the fields of service, technology, healthcare, retail, banking, nonprofit, and hospitality. Hypotheses were tested through correlational and hierarchical regression analytic procedures. Job fit, affective commitment, and psychological climate were all significantly related to employee engagement and employee engagement was significantly related to both discretionary effort and intention to turnover. For the discretionary effort model, the hierarchical regression analysis results suggested that the employees who reported experiencing a positive psychological climate were more likely to report higher levels of discretionary effort. As for the intention to turnover model, the hierarchical regression analysis results indicated that affective commitment and employee engagement predicted lower levels of an employee’s intention to turnover. The regression beta weights ranged from to .43 to .78, supporting the theoretical, empirical, and practical relevance of understanding the impact of employee engagement on organizational outcomes. Implications for HRD theory, research, and practice are highlighted as possible strategic leverage points for creating conditions that facilitate the development of employee engagement as a means for improving organizational performance.

Class Placement and Academic and Behavioral Variables as Predictors of Graduation for Students with Disabilities

This study investigated the impact of specified variables related to academic history, behavioral history, and availability of inclusive systems as potential risk factors for dropouts, impacting students with disabilities. Results indicated that a successful academic history was the only significant predictor of graduation potential when statistically controlling all the other variables.

Class Placement and Academic and Behavioral Variables as Predictors of Graduation for Students with Disabilities

Dropout rates impacting students with high-incidence disabilities in American schools remain staggering (Bost, 2006; Hehir, 2005). Of this group, students with Emotional Behavioral Disorders (EBD) are at greatest risk. Despite the mandated national propagation of inclusion, students with EBD remain the least included and the least successful when included (Bost). Accordingly, this study investigated the potential significance of inclusive settings and other school-related variables within the context of promoting the graduation potential of students with Specific Learning Disabilities (SLD) or EBD. This mixed-methods study investigated specified school-related variables as likely dropout predictors, as well as the existence of first-order interactions among some of the variables. In addition, it portrayed the perspectives of students with SLD or EBD on the school-related variables that promote graduation. Accordingly, the sample was limited to students with SLD or EBD who had graduated or were close to graduation. For the quantitative component the numerical data were analyzed using linear and logistic regressions. For the qualitative component guided student interviews were conducted. Both strands were subsequently analyzed using Ridenour and Newman’s (2008) model where the quantitative hypotheses are tested and are later built-upon by the related qualitative meta-themes. Results indicated that a successful academic history, or obtaining passing grades was the only significant predictor of graduation potential when statistically controlling all the other variables. While at a marginal significance, results also yielded that students with SLD or EBD in inclusive settings experienced better academic results and behavioral outcomes than those in self-contained settings. Specifically, students with SLD or EBD in inclusive settings were found to be more likely to obtain passing grades and less likely to be suspended from school. Generally, the meta-themes yielded during the student interviews corroborated these findings as well as provided extensive insights on how students with disabilities view school within the context of promoting graduation. Based on the results yielded, provided the necessary academic accommodations and adaptations are in place, along with an effective behavioral program, inclusive settings can be utilized as drop-out prevention tools in special education.

Minimizing makespan for hybrid flowshops with batch, discrete processing machines and arbitrary job sizes

This research aims at a study of the hybrid flow shop problem which has parallel batch-processing machines in one stage and discrete-processing machines in other stages to process jobs of arbitrary sizes. The objective is to minimize the makespan for a set of jobs. The problem is denoted as: FF: batch1,sj:Cmax. The problem is formulated as a mixed-integer linear program. The commercial solver, AMPL/CPLEX, is used to solve problem instances to their optimality. Experimental results show that AMPL/CPLEX requires considerable time to find the optimal solution for even a small size problem, i.e., a 6-job instance requires 2 hours in average. A bottleneck-first-decomposition heuristic (BFD) is proposed in this study to overcome the computational (time) problem encountered while using the commercial solver. The proposed BFD heuristic is inspired by the shifting bottleneck heuristic. It decomposes the entire problem into three sub-problems, and schedules the sub-problems one by one. The proposed BFD heuristic consists of four major steps: formulating sub-problems, prioritizing sub-problems, solving sub-problems and re-scheduling. For solving the sub-problems, two heuristic algorithms are proposed; one for scheduling a hybrid flow shop with discrete processing machines, and the other for scheduling parallel batching machines (single stage). Both consider job arrival and delivery times. An experiment design is conducted to evaluate the effectiveness of the proposed BFD, which is further evaluated against a set of common heuristics including a randomized greedy heuristic and five dispatching rules. The results show that the proposed BFD heuristic outperforms all these algorithms. To evaluate the quality of the heuristic solution, a procedure is developed to calculate a lower bound of makespan for the problem under study. The lower bound obtained is tighter than other bounds developed for related problems in literature. A meta-search approach based on the Genetic Algorithm concept is developed to evaluate the significance of further improving the solution obtained from the proposed BFD heuristic. The experiment indicates that it reduces the makespan by 1.93 % in average within a negligible time when problem size is less than 50 jobs.

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.

Variables affecting government debt: A comparison of certificates of participation, tax -exempt revenue bonds and tax -exempt general obligation bonds

This dissertation analyzed and compared variables affecting interest rate and yield of certificates of participation, tax-exempt revenue bonds and tax-exempt general obligation bonds. The study employed qualitative and quantitative analysis methods. ^ Qualitative research methods included surveys, interviews and focus groups. The survey solicited debt load information from 67 Florida school districts (21 responded) and addressed the question which districts used certificates of participation and why. Eight individuals with experience dealing with all three debt instruments were interviewed. A follow-up focus group of six school district financial officers gathered additional data. Results from the qualitative methods revealed school districts used certificates of participation based on millage authority amount available relative to overall tax base. Also identified was the belief of a significant difference in certificates of participation costs and the other two debt instrument types. ^ The study's quantitative methods analyzed 1998 and 1999 initial issues of Moody's AAA rated certificates of participation, tax-exempt revenue bonds and tax-exempt general obligation bonds. Through an analysis of covariance (ANCOVA), the study examined interest rates and yields while controlling for the covariates of credit enhancement, issue size, and maturity date. The analysis identified no significant difference between interest rates of certificates of participation and tax-exempt general obligation bonds (p < 0.05). There was a significant difference between interest rates of tax-exempt revenue bonds and tax-exempt general obligation bonds. This study discerned no significant difference between yield on certificates of participation and tax-exempt general obligation bonds. It identified a difference in yield between both certificates of participation and tax-exempt general obligation bonds compared with tax-exempt revenue bonds. ^ The study found COPs to have lesser overall costs than RV bonds. COPs also have a quicker entry into the market resulting in construction cost savings. The study found policy implications such as investment portfolio limitations and public choice issues about using COPs as a mechanism to grow government. ^