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The purpose of this study was to identify how knowledgeable the students of Mississippi are of severe weather. The state of Mississippi is ranked second in the nation with the total number of deaths per state per year. The state also ranks among the top 10 of all states with number of tornadoes per state and cost of damage per state. Considering the facts mentioned above, a study was designed to discover if the students were taught severe weather awareness while they progressed through their twelve years of elementary and secondary schooling. A survey was created through the research of the Mississippi Department of Education, and Jim Belles, National Weather Service Memphis Office Warning Coordination Meteorologist. The results of the survey were then put to statistical tests to find out the significance of the survey questions.

N/A / Thesis / Master of Science (MS)

This study employed a mixed-methods strategy to assess how narrative writing is perceived and utilized in first-year composition (FYC) courses at American 4-year institutions. The history of narrative writing instruction is reviewed in an examination of the literature of rhetoric and composition scholars, as well as those who study narrative theory. The survey tool was created for instructors and course/program designers to provide an overview of their FYC curricula, methodologies, and perceptions related to narrative writing. Findings indicate that FYC instructors and course designers perceive narrative writing as an introductory writing tool, much like the progymnasmata of antiquity, wherein narrative writing is used as scaffolding for more academic writing tasks. Though rarely assigned beyond the first project of the semester, when narrative is assigned, the learning objectives are difficult to meaningfully articulate beyond the general goals of the FYC course. The implications for future research are plentiful, as this study was necessarily limited in scope by its exploratory goals, so there are many opportunities to expand and build upon the work of this study.

Yes / We present a thorough review of hyperheuristic research to date, and analyse/compare hyperheuristic papers based on the methods used.

No / Is midwifery knowledge a relevant construct for contemporary practice? In other words, is it useful to think about the things midwives know and the skills they possess in terms of midwifery knowledge? Or, in an era of interprofessional practice, clinical governance and risk management, have midwives lost whatever was unique to their profession about how to help women give birth?

Brugha and his colleagues in this issue raise important questions about the validity of standardized diagnostic interviews of mental disorders, such as the Composite International Diagnostic Interview (CIDI) (WHO, 1990). Although their concerns refer predominantly to the use of such instruments in epidemiological research, the authors' conclusions also have significant implications for diagnostic assessments in clinical practice and research. We agree with Brugha et al. that the inflexible approach to questioning used in standardized interviews can lead to an increased risk of invalidity with regard to some diagnoses. We also agree that the use of more semi-structured clinical questions has the potential to address this problem. However, we disagree with Brugha et al. in several other respects.

Data obtained from longitudinal surveys using complex multi-stage sampling designs contain cross-sectional dependencies among units caused by inherent hierarchies in the data, and within subject correlation arising due to repeated measurements. The statistical methods used for analyzing such data should account for stratification, clustering and unequal probability of selection as well as within-subject correlations due to repeated measurements. <p>The complex multi-stage design approach has been used in the longitudinal National Population Health Survey (NPHS). This on-going survey collects information on health determinants and outcomes in a sample of the general Canadian population. <p>This dissertation compares the model-based and design-based approaches used to determine the risk factors of asthma prevalence in the Canadian female population of the NPHS (marginal model). Weighted, unweighted and robust statistical methods were used to examine the risk factors of the incidence of asthma (event history analysis) and of recurrent asthma episodes (recurrent survival analysis). Missing data analysis was used to study the bias associated with incomplete data. To determine the risk factors of asthma prevalence, the Generalized Estimating Equations (GEE) approach was used for marginal modeling (model-based approach) followed by Taylor Linearization and bootstrap estimation of standard errors (design-based approach). The incidence of asthma (event history analysis) was estimated using weighted, unweighted and robust methods. Recurrent event history analysis was conducted using Anderson and Gill, Wei, Lin and Weissfeld (WLW) and Prentice, Williams and Peterson (PWP) approaches. To assess the presence of bias associated with missing data, the weighted GEE and pattern-mixture models were used.<p>The prevalence of asthma in the Canadian female population was 6.9% (6.1-7.7) at the end of Cycle 5. When comparing model-based and design- based approaches for asthma prevalence, design-based method provided unbiased estimates of standard errors. The overall incidence of asthma in this population, excluding those with asthma at baseline, was 10.5/1000/year (9.2-12.1). For the event history analysis, the robust method provided the most stable estimates and standard errors. <p>For recurrent event history, the WLW method provided stable standard error estimates. Finally, for the missing data approach, the pattern-mixture model produced the most stable standard errors <p>To conclude, design-based approaches should be preferred over model-based approaches for analyzing complex survey data, as the former provides the most unbiased parameter estimates and standard errors.

Data obtained from longitudinal surveys using complex multi-stage sampling designs contain cross-sectional dependencies among units caused by inherent hierarchies in the data, and within subject correlation arising due to repeated measurements. The statistical methods used for analyzing such data should account for stratification, clustering and unequal probability of selection as well as within-subject correlations due to repeated measurements. <p>The complex multi-stage design approach has been used in the longitudinal National Population Health Survey (NPHS). This on-going survey collects information on health determinants and outcomes in a sample of the general Canadian population. <p>This dissertation compares the model-based and design-based approaches used to determine the risk factors of asthma prevalence in the Canadian female population of the NPHS (marginal model). Weighted, unweighted and robust statistical methods were used to examine the risk factors of the incidence of asthma (event history analysis) and of recurrent asthma episodes (recurrent survival analysis). Missing data analysis was used to study the bias associated with incomplete data. To determine the risk factors of asthma prevalence, the Generalized Estimating Equations (GEE) approach was used for marginal modeling (model-based approach) followed by Taylor Linearization and bootstrap estimation of standard errors (design-based approach). The incidence of asthma (event history analysis) was estimated using weighted, unweighted and robust methods. Recurrent event history analysis was conducted using Anderson and Gill, Wei, Lin and Weissfeld (WLW) and Prentice, Williams and Peterson (PWP) approaches. To assess the presence of bias associated with missing data, the weighted GEE and pattern-mixture models were used.<p>The prevalence of asthma in the Canadian female population was 6.9% (6.1-7.7) at the end of Cycle 5. When comparing model-based and design- based approaches for asthma prevalence, design-based method provided unbiased estimates of standard errors. The overall incidence of asthma in this population, excluding those with asthma at baseline, was 10.5/1000/year (9.2-12.1). For the event history analysis, the robust method provided the most stable estimates and standard errors. <p>For recurrent event history, the WLW method provided stable standard error estimates. Finally, for the missing data approach, the pattern-mixture model produced the most stable standard errors <p>To conclude, design-based approaches should be preferred over model-based approaches for analyzing complex survey data, as the former provides the most unbiased parameter estimates and standard errors.