The purpose of the study was to examine the relationship between credit hour load and academic achievement of selected undergraduate college students. The study was designed to determine the relationship between credit hour load and grades earned by undergraduate college students in selected required courses and overall scholastic ratio during the specified quarters. Specifically, the study was designed to determine the additional contribution of the independent variable credit hour load to the overall relationship between academic achievement of the students in selected required courses, as measured by course grade, and overall scholastic ratio, and the independent variable set consisting of sex and ability as measured by SAT Verbal and SAT Quantitative scores.The population included all Ball State University undergraduate secondary education students enrolled during the academic years 1976/77, 1977/78 and 1978/79. The sample consisted of 1,007 students identified as having enrolled in and completed credit hour loads of twelve or more hours for at least one quarter. Students selected for inclusion had enrolled in either of two required secondary education courses, EDSEC 299 or EDSEC 420. The study was designed to control for sex differences and variations in ability.To accomplish specific purposes of the study, four null hypotheses were tested using multiple regression procedures. The Statistical Package for the Social Sciences (SFSS) program was used in conducting the analyses. Two regression models were used to determine the statistical significance of the addition of the independent variable, credit hour load, to a set of predictors consisting of sex, SAT Verbal and SAT Quantitative scores. In the first model only sex, SAT Verbal and SAT Quantitative scores were used as predictors of the dependent variable (reduced model). The second model involved the addition of the independent variable, credit hour lead, to the predictor set. The difference between the obtained squared multiple correlation (R2) was then computed and tested for statistical significance at the .05 level using an F statistic.The findings regarding statistical significance of the contribution provided by credit hour load to a relationship between the dependent variable and the independent variable set were:1. The level of prediction of course grades in EDSEC 299 provided by sex of student and ability data was not improved by the addition of credit hour load data.2. The level of prediction of scholastic ratio for students enrolled in EDSEC 299 provided by sex of student and ability data was improved by the addition of credit hour load data. The degree of improvement however, was so slight as to be of little importance in idiographic predictions.3. The level of prediction of course grades in EDSEC 420 provided by sex of student and ability data was improved by the addition of credit hour load data. The degree of improvement however, was so slight as to be of little practical importance in idiographic predictions.4. The level of prediction of scholastics ratio for students enrolled in EDSEC 420 provided by sex of student and ability data was not improved by the addition of credit hour load data.

The purpose of this study was to determine the relationship between selected nonintellectual traits and successful school achievement of children from low-income homes. The subjects for this study were 347 sixth-grade children who lived in a depressed urban area. Each subject was assigned to one of four achievement groups based on achievement test scores and teacher evaluations. The groups ranged from the 69 successful children in Achievement Group I to the 169 low achievers in Achievement Group IV. The California Test of Personality (CTP) was used to measure the personal and social adjustment of all subjects. Areas of academically related interests were measured by What I Like to Do, An Inventory of Children's Interests. An interview guide, developed by the researcher, was employed in unstructured interviews with eight achieving pupils. Statistical processing of the data consisted of a three factor analysis of covariance. Interactions were computed between (a) achievement groups, (b) boys and girls, and (c) ethnic groups. The analysis of covariance method was applied to partial out the effects of ability as measured by the Lorge-Thorndike Intelligence Tests. Scores the subjects made on standardized tests were converted to T-scores for the analysis, using the .05 level of confidence for significance. It was hypothesized that a group of children from low-income homes who had been identified as successful school achievers would differ significantly from groups of less successful pupils from a similar environment in areas of personal and social adjustment and in areas of academically related interests. However, the findings of this study did not strongly support this hypothesis. It was found that when ability was controlled, only the School Relations component of the CTP revealed significantly higher scores for the successful achievers. Information used in the identification of successful achievers, a review of the results of the statistical analysis, and data collected during interviews with eight of the successful achievers led to the following conclusions: The discrepancy between the actual school performance of most children from low-income homes and the expectations of teachers and the grade level norms of standardized achievement tests was clearly demonstrated. The total sample obtained below average scores on the CTP; This suggested the generally poor personal and social adjustment of children from low-income homes whatever their achievement level.White children from low-income homes who are successful school achievers appeared to be better adjusted than their minority group counterparts.Well-adjusted children are more likely to be rated as successful school achievers by their teachers. The findings of this study question the evidence that exists concerning the negative self-image of the child from a low-income home.The significantly lower scores of minority group children in all achievement groups on the Self-Reliance subtest of the CTP indicated that many minority group children are deficient in this personality variable often associated with school success. Although few differences in adjustment existed between boys and girls in this study, the two components that revealed significant differences suggested that girls from low-income homes are probably more willing than boys to subordinate their desires to the needs of the group and may be more effective in dealing with people. This group of children regardless of sex, ethnic group membership, or level of achievement, expressed resentment and hostility toward the community and toward society.The unstructured interview technique revealed some valuable information not available through a statistical approach. This approach might be a profitable one for future investigations involving children from low-income homes.

Determining the fatigue properties (Manson-Coffin and Ramberg-Osgood parameters) for a steel material requires time consuming and expensive testing. In the early stages of a design process, it is not feasible to perform this testing. To help solve this problem numerous researchers have developed estimation methods to estimate the Manson-Coffin parameters from monotonic properties data. Additionally, other researchers have compared the results from these various estimation methods for large material classifications. However, a comprehensive comparison of these estimation methods has not been made for steels in different heat treatment states. More accurate results for the best estimation method can be made with smaller classifications, which have more consistent properties. In this research, best estimation methods are determined for six steel heat treatments. In addition to looking at steel heat treatment classifications, the estimation of the Ramberg-Osgood parameters is also examined through the compatibility conditions. Without them, the approach of estimating the fatigue properties using the estimation methods would not be practically useful. Finally, in the comparison of the estimation methods, an appropriate statistical comparison methodology is utilized; multiple contrasts comparison. This methodology is implemented into the comparison of the different estimation methods, by comparing the estimated lives and the experimental lives as a regression so that the entire life range can be considered. The estimation methods can also be utilized to get estimates of the variability of the fatigue properties given the variability of the monotonic properties data, since there is a functional relationship developed between the two sets of material properties. This variability is necessary for a stochastic design process, in order to obtain a more optimally designed component or structure. Overall the estimation methods have a number of practical applications within a fatigue design process. Their use and implementation needs to be supplemented by the appropriate knowledge of their limitations and for what classifications they give the best results. An expert system is developed to summarize this knowledge to assist an engineer. This research aims to provide this knowledge and expands their use to account for variability in fatigue properties for stochastic analysis.

A study was conducted to quantify weather conditions at specific growth stages of Canadian western Spring wheat (Triticum aestivum) and relate those growing conditions to variations in wheat grade and quality characteristics and to develop pre-harvest prediction models for wheat quality using weather input data. Six Canadian western spring wheat genotypes were grown in five locations across the Canadian prairies during the 2003 and 2004 growing seasons. Intensive weather data was collected during the growing season at each location and used to calculate accumulated heat stress, useful heat, moisture demand, moisture supply, moisture use and moisture stress variables for numerous crop development stages. Grain samples were graded, milled and underwent an extensive analysis of flour, dough, and bread making quality. ANOVA indicated that genotype, environment and their interactions had significant effects on most quality parameters tested. Environmental contribution to wheat quality variance was considerably larger than the variance contribution of either genotype or GxE interaction. Using the weather and crop development stage information, significant regression equations with high regression coefficients were developed for most quality parameters using just a single independent weather variable. Multiple regression equations with even higher R2 values were developed using three complex weather variables, leading to the opportunity to predict wheat quality 2-5 weeks prior to harvest. Equally strong prediction models were developed utilizing basic weather variables which could be obtained from weather stations monitoring only daily maximum and minimum air temperature and precipitation. The development periods of planting to jointing and anthesis to soft dough were the stages most frequently exhibiting the highest correlation to wheat quality indicating weather needs to be monitored during the entire growing season to accurately predict quality. Grain quality forecast models were validated using 2005 weather and crop data. Prediction models developed from the 2003 and 2004 data required modification in order to accurately and consistently predict the grain properties in 2005.

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The identification of possible hazards in chemical plants is a very important part of the design process. This is because of the potential danger that large chemical installations pose to the public. One possible route for speeding up the identification of hazards in chemical plants is to use computers to identify hazards automatically. This will facilitate safe plant design and will avoid late design changes which can be very costly to implement. Previous research at Loughborough has concentrated on developing a model-based approach and an analysis algorithm for automating hazard identification. The results generated have demonstrated the technical feasibility of the approach. This approach requires a knowledge-base of unit models. This library of models describes how different plant equipment behaves in qualitative terms. The research described in this thesis develops a method for creating and testing the equipment models. The model library was previously achieved by an expert writing the models in a format that could be directly used by the system described above. An engineer unfamililar with the system would find this difficult. An alternative method would have been to use an intermediary (a knowledge engineer) to gather information from the engineer and convert it into the system format. This would be expensive. Both methods would take up a lot of the engineer's time. An engineer should be able to enter information personally in order to maintain efficiency and avoid information loss through the intermediary. A front end interface has been built to the system which enables an expert to enter information directly without needing to understand details of the application system. This interface incorporates ideas from the knowledge acquisition field in order to produce a tool that is simple to use. Unit-based qualitative modelling can lead to incorrect or ambiguous inference. The method developed here identifies situations where ambiguities may arise. A new modular approach is presented to overcome this type of problem. This method also presents a technique to verify that the models created are both complete and correct.

Since the publication of the Framingham algorithm for heart disease, tools that predict disease risk have been increasingly integrated into standards of practice. The utility of algorithms at the population level can serve several purposes in health care decision-making and planning. A population-based risk prediction tool for Diabetes Mellitus (DM) can be particularly valuable for public health given the significant burden of diabetes and its projected increase in the coming years. This thesis addresses various aspects related to diabetes risk in addition to incorporating methodologies that advance the practice of epidemiology. The goal of this thesis is to demonstrate and inform the methods of population-based diabetes risk prediction. This is studied in three components: (I) development and validation of a diabetes population risk tool, (II) measurement and (III) obesity risk. Analytic methods used include prediction survival modeling, simulation, and multilevel growth modeling. Several types of data were analyzed including population healthy survey, health administrative, simulation and longitudinal data. The results from this thesis reveal several important findings relevant to diabetes, obesity, population-based risk prediction, and measurement in the population setting. In this thesis a model (Diabetes Population Risk Tool or DPoRT) to predict 10-year risk for diabetes, which can be applied using commonly-collected national survey data was developed and validated. Conclusions drawn from the measurement analysis can inform research on the influence of measurement properties (error and type) on modeling and statistical prediction. Furthermore, the use of new modeling strategies to model change of body mass index (BMI) over time both enhance our understanding of obesity and diabetes risk and demonstrate an important methodology for future epidemiological studies. Epidemiologists are in need of innovative and accessible tools to assess population risk making these types of risk algorithms an important scientific advance. Population-based prediction models can be used to improve health planning, explore the impact of prevention strategies, and enhance our understanding of the distribution of diabetes in the population. This work can be extended to future studies which develop tools for disease planning at the population level in Canada and to enrich the epidemiologic literature on modeling strategies.