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Background: The consequences of poor nutrition are well known and of wide concern. Governments and public health agencies utilise food and diet surveillance data to make decisions that lead to improvements in nutrition. These surveys often utilise complex sample designs for efficient data collection. There are several challenges in the statistical analysis of dietary intake data collected using complex survey designs, which have not been fully addressed by current methods. Firstly, the shape of the distribution of intake can be highly skewed due to the presence of outlier observations and a large proportion of zero observations arising from the inability of the food diary to capture consumption within the period of observation. Secondly, dietary data is subject to variability arising from day-to-day individual variation in food consumption and measurement error, to be accounted for in the estimation procedure for correct inferences. Thirdly, the complex sample design needs to be incorporated into the estimation procedure to allow extrapolation of results into the target population. This thesis aims to develop novel statistical methods to address these challenges, applied to the analysis of iron intake data from the UK National Diet and Nutrition Survey Rolling Programme (NDNS RP) and UK national prescription data of iron deficiency medication. Methods: 1) To assess the nutritional status of particular population groups a two-part model with a generalised gamma (GG) distribution was developed for intakes that show high frequencies of zero observations. The two-part model accommodated the sources of data variation of dietary intake with a random intercept in each component, which could be correlated to allow a correlation between the probability of consuming and the amount consumed. 2) To identify population groups at risk of low nutrient intakes, a linear quantile mixed-effects model was developed to model quantiles of the distribution of intake as a function of explanatory variables. The proposed approach was illustrated by comparing the quantiles of iron intake with Lower Reference Nutrient Intakes (LRNI) recommendations using NDNS RP. This thesis extended the estimation procedures of both the two-part model with GG distribution and the linear quantile mixed-effects model to incorporate the complex sample design in three steps: the likelihood function was multiplied by the sample weightings; bootstrap methods for the estimation of the variance and finally, the variance estimation of the model parameters was stratified by the survey strata. 3) To evaluate the allocation of resources to alleviate nutritional deficiencies, a quantile linear mixed-effects model was used to analyse the distribution of expenditure on iron deficiency medication across health boards in the UK. Expenditure is likely to depend on the iron status of the region; therefore, for a fair comparison among health boards, iron status was estimated using the method developed in objective 2) and used in the specification of the median amount spent. Each health board is formed by a set of general practices (GPs), therefore, a random intercept was used to induce correlation between expenditure from two GPs from the same health board. Finally, the approaches in objectives 1) and 2) were compared with the traditional approach based on weighted linear regression modelling used in the NDNS RP reports. All analyses were implemented using SAS and R. Results: The two-part model with GG distribution fitted to amount of iron consumed from selected episodically food, showed that females tended to have greater odds of consuming iron from foods but consumed smaller amounts. As age groups increased, consumption tended to increase relative to the reference group though odds of consumption varied. Iron consumption also appeared to be dependent on National Statistics Socio-Economic Classification (NSSEC) group with lower social groups consuming less, in general. The quantiles of iron intake estimated using the linear quantile mixed-effects model showed that more than 25% of females aged 11-50y are below the LRNI, and that 11-18y girls are the group at highest of deficiency in the UK. Predictions of spending on iron medication in the UK based on the linear quantile mixed-effects model showed areas of higher iron intake resulted in lower spending on treating iron deficiency. In a geographical display of expenditure, Northern Ireland featured the lowest amount spent. Comparing the results from the methods proposed here showed that using the traditional approach based on weighted regression analysis could result in spurious associations. Discussion: This thesis developed novel approaches to the analysis of dietary complex survey data to address three important objectives of diet surveillance, namely the mean estimation of food intake by population groups, identification of groups at high risk of nutrient deficiency and allocation of resources to alleviate nutrient deficiencies. The methods provided models of good fit to dietary data, accounted for the sources of data variability and extended the estimation procedures to incorporate the complex sample survey design. The use of a GG distribution for modelling intake is an important improvement over existing methods, as it includes many distributions with different shapes and its domain takes non-negative values. The two-part model accommodated the sources of data variation of dietary intake with a random intercept in each component, which could be correlated to allow a correlation between the probability of consuming and the amount consumed. This also improves existing approaches that assume a zero correlation. The linear quantile mixed-effects model utilises the asymmetric Laplace distribution which can also accommodate many different distributional shapes, and likelihood-based estimation is robust to model misspecification. This method is an important improvement over existing methods used in nutritional research as it explicitly models the quantiles in terms of explanatory variables using a novel quantile regression model with random effects. The application of these models to UK national data confirmed the association of poorer diets and lower social class, identified the group of 11-50y females as a group at high risk of iron deficiency, and highlighted Northern Ireland as the region with the lowest expenditure on iron prescriptions.

Effective nursing care can be threatened when nurses are not culturally attuned with their patients. Associate degree nursing (ADN) students receive information about diverse ethnicities in the nursing curriculum, but it may not be sufficient to provide the expertise necessary to care for patients of various cultural backgrounds. The purpose of this quantitative study was to explore the 2nd year ADN students' levels of cultural competence and their perceptions of self-efficacy in working with Caucasian, African American, Native American, Hispanic, and Asian ethnicities. The study used a cross-sectional survey design to determine if a relationship existed between the students' reported cultural competencies and their self-efficacy scores while providing care to patients of these diverse cultures. The process of cultural competence in the delivery of health care services was used as the theoretical framework for this study. A volunteer convenience sample of 64 2nd-year ADN students completed the Nurse Cultural Competence Scale and the Cultural Self-Efficacy Scale. The Pearson-Product Moment correlation revealed a significant negative, moderate relationship between self-efficacy and the students' perceptions of cultural competence. A project was designed to enhance skills and knowledge to improve the students' cultural competency while caring for patients of Asian, Native American, and Hispanic cultures because minimal familiarity of those cultures contributed most to the negative correlation. Research on methods to improve cultural competence among health care professionals should be continued. Positive social change will occur as nursing students gain proficiency in their abilities to provide culturally appropriate care to patients of diverse ethnic backgrounds.

While the importance of assessing the occupational consequences of insomnia and other sleep disorders is emphasised in clinical nosologies and research guidelines, there is little consensus on which aspects of occupational performance should be assessed, how such impairment should be measured, and how outcomes should be reported. The research programme described in this thesis aimed to address this issue. Chapter 1 presents a systematic review and methodical critique of studies reporting those aspects of occupational performance most impacted by (or most frequently associated with) insomnia symptoms and degraded sleep quality. Equivocal results, wide variations in reporting conventions, and the overall lack of comparability among studies, strongly indicated the need to develop a standardised metric able to quantify sleep related occupational performance and serve as an assessment and outcome instrument suitable for use in research and clinical settings. Informed by the literature review, Chapters 2-4 describe the development and validation of the Loughborough Occupational Impact of Sleep Scale ( LOISS ), a unidimensional 19 item questionnaire that captures sleep-related occupational impairment across a number of workplace domains over a 4-week reference period. Chapters 5-7 describe LOISS outcomes from: i) surveys in a random population sample; ii) a representative sample of the UK workforce; and iii) a clinical sample of patients with obstructive sleep apnoea (before and after treatment with CPAP). Overall, the scale showed strong internal consistency (Cronbach s alpha range=0.84-0.94) and test-retest reliability (r=0.77, r2=0.59, p<0.001), high levels of criterion validity (significantly discriminating between good and poor sleepers), and proved an effective outcome measure in OSA. From the survey data reported in Chapters 2-7, LOISS score distributions showed no consistent gender difference but did show a significant ageing gradient, with sleep-related occupational impairment declining with increasing age. In conclusion, the work presented here supports the usability, validity and reliability of the LOISS as an assessment and outcome instrument, and also demonstrates the utility of this instrument in exploring the dynamics of sleep-related occupational performance

Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2019 / This research study has demonstrated the complexity involved in complex survey sample design (CSSD). Furthermore the study has proposed methods to account for each step taken in sampling and at the estimation stage using the theory of survey sampling, CSSD-based case studies and practical implementation based on census attributes. CSSD methods are designed to improve statistical efficiency, reduce costs and improve precision for sub-group analyses relative to simple random sample(SRS).They are commonly used by statistical agencies as well as development and aid organisations. CSSDs provide one of the most challenging fields for applying a statistical methodology. Researchers encounter a vast diversity of unique practical problems in the course of studying populations. These include, interalia: non-sampling errors,specific population structures,contaminated distributions of study variables,non-satisfactory sample sizes, incorporation of the auxiliary information available on many levels, simultaneous estimation of characteristics in various sub-populations, integration of data from many waves or phases of the survey and incompletely specified sampling procedures accompanying published data. While the study has not exhausted all the available real-life scenarios, it has outlined potential problems illustrated using examples and suggested appropriate approaches at each stage. Dealing with the attributes of CSSDs mentioned above brings about the need for formulating sophisticated statistical procedures dedicated to specific conditions of a sample survey. CSSD methodologies give birth to a wide variety of approaches, methodologies and procedures of borrowing the strength from virtually all branches of statistics. The application of various statistical methods from sample design to weighting and estimation ensures that the optimal estimates of a population and various domains are obtained from the sample data.CSSDs are probability sampling methodologies from which inferences are drawn about the population. The methods used in the process of producing estimates include adjustment for unequal probability of selection (resulting from stratification, clustering and probability proportional to size (PPS), non-response adjustments and benchmarking to auxiliary totals. When estimates of survey totals, means and proportions are computed using various methods, results do not differ. The latter applies when estimates are calculated for planned domains that are taken into account in sample design and benchmarking. In contrast, when the measures of precision such as standard errors and coefficient of variation are produced, they yield different results depending on the extent to which the design information is incorporated during estimation. The literature has revealed that most statistical computer packages assume SRS design in estimating variances. The replication method was used to calculate measures of precision which take into account all the sampling parameters and weighting adjustments computed in the CSSD process. The creation of replicate weights and estimation of variances were done using WesVar, astatistical computer package capable of producing statistical inference from data collected through CSSD methods. Keywords: Complex sampling, Survey design, Probability sampling, Probability proportional to size, Stratification, Area sampling, Cluster sampling.

To remedy the negative effects of wetland drainage activities that took place in Sweden during the 1800s, measures to create, restore and recreate wetlands have gained increased attention as nature-based solutions. While wetlands provide cost-effective long-term solutions and co-benefits that help achieve national and international environmental objectives, targets relating to wetland measures have fallen short. The study aim was to investigate drivers, policy influence and barriers to wetland implementation on the local level and to discuss potential success parameters. Local stakeholder experiences were collected using survey design with open, closed, and partially close-ended questions, analysed with thematic analysis. The findings identified drivers relating to ecological and social benefits, climate change, and multifunctionality- and indicated that policy objectives on the national level influence drivers on the local level. Furthermore, barriers towards wetland implementation were identified, fuelled by a lack of communication, policies that rely on one-size-fits-all approaches, and a lack of resources for administration. Potential success parameters to address the barriers include improved collaboration between and within wetland projects, financial incentives for landowners and administration, maintenance plans, systems for follow-up outside of the policy instruments, and increased flexibility for wetland projects that take into account the natural variability of wetlands. The findings can lay the foundation for further research exploring more in-depth the identified barriers and success parameters to help set new policy directives and suggestions for how wetland measures could be implemented at a larger scale in the future.

The purpose of this study was to propose and evaluate a procedure for revising an existing self-administered survey that is in need of item revision and/or scale reduction while maximizing validity and reliability. The procedure was demonstrated using the Client Evaluation of Self and Treatment (CEST; Joe, Broome, Rowan-Szal, & Simpson, 2002), a self-administered survey used in drug and alcohol treatment agencies. The procedure included confirmatory and exploratory factor analyses of a large dataset of completed CEST surveys, a readability analysis, and cognitive interviewing of two different groups of respondents to determine what problems they might have with CEST items. The cognitive interviewing revealed a number of issues that led to confusion among respondents, including items with two distinct concepts embedded, items containing absolutes and vague qualifiers, misinterpreted items, and terms and phrases respondents had difficulty understanding. The CEST was also judged to be long and potentially burdensome to respondents. Based on the results of this evaluation, a new survey—the Brief Assessment of Self in Context (BASIC)—also intended for use by substance abuse treatment providers, was constructed. First, factor analyses of the CEST and advice from an expert panel were used to determine which scales to retain. Next, quantitative analyses and cognitive interviewing helped determine which CEST items to retain and which to revise. Readability, sound item writing principles, and response format and scale requirements were also used to determine which items to include in the initial draft of the BASIC and guided item construction when needed. After the panel of experts provided feedback on the first revision, a final draft was prepared. Another round of cognitive interviewing was followed by administration of the final draft of the survey to a representative sample. The results indicated that the BASIC’s items are clear, unambiguous, and easy to interact with and understand, and that the instrument is an improvement over the CEST. In brief, the procedure demonstrated in this study produced a psychometrically sound instrument composed of items that are easy for respondents to access. / text

This thesis describes the design, maintenance and statistical analysis involved in undertaking a Longitudinal Survey. A longitudinal survey (or study) obtains observations or responses from individuals over several times over a defined period. This enables the direct study of changes in an individual's response over time. In particular, it distinguishes an individual's change over time from the baseline differences among individuals within the initial panel (or cohort). This is not possible in a cross-sectional study. As such, longitudinal surveys give correlated responses within individuals. Longitudinal studies therefore require different considerations for sample design and selection and analysis from standard cross-sectional studies. This thesis looks at the methodology for analysing social surveys. Most social surveys comprise of variables described as categorical variables. This thesis outlines the process of sample design and selection, interviewing and analysis for a longitudinal study. Emphasis is given to categorical response data typical of a survey. Included in this thesis are examples relating to the Goodna Longitudinal Survey and the Longitudinal Survey of Immigrants to Australia (LSIA). Analysis in this thesis also utilises data collected from these surveys. The Goodna Longitudinal Survey was conducted by the Queensland Office of Economic and Statistical Research (a portfolio office within Queensland Treasury) and began in 2002. It ran for two years whereby two waves of responses were collected.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2014-11-11T12:06:48Z No. of bitstreams: 2 Tese Geiziane Tessarolo - 2014.pdf: 5275889 bytes, checksum: fb092b496eb6eae85e89c28d423c44d9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2014-11-17T15:10:55Z (GMT) No. of bitstreams: 2 Tese Geiziane Tessarolo - 2014.pdf: 5275889 bytes, checksum: fb092b496eb6eae85e89c28d423c44d9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-11-17T15:10:55Z (GMT). No. of bitstreams: 2 Tese Geiziane Tessarolo - 2014.pdf: 5275889 bytes, checksum: fb092b496eb6eae85e89c28d423c44d9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-04-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Aim Species Distribution Models (SDM) can be used to predict the location of unknown populations from known species occurrences. It follows that how the data used to calibrate the models are collected can have a great impact on prediction success. We evaluated the influence of different survey designs and their interaction with the modelling technique on SDM performance. Location Iberian Peninsula Methods We examine how data recorded using seven alternative survey designs (random, systematic, environmentally stratified by class and environmentally stratified using p-median, biased due to accessibility, biased by human density aggregation and biased towards protected areas) could affect SDM predictions generated with nine modelling techniques (BIOCLIM, Gower distance, Mahalanobis distance, Euclidean distance, GLM, MaxEnt, ENFA and Random Forest). We also study how sample size, species’ characteristics and modelling technique affected SDM predictive ability, using six evaluation metrics. Results Survey design has a small effect on prediction success. Characteristics of species’ ranges rank highest among the factors affecting SDM results: the species with lower relative occurrence area (ROA) are predicted better. Model predictions are also improved when sample size is large. Main conclusions The species modelled – particularly the extent of its distribution – are the largest source of influence over SDM results. The environmental coverage of the surveys is more important than the spatial structure of the calibration data. Therefore, climatic biases in the data should be identified to avoid erroneous conclusions about the geographic patterns of species distributions. / Aim Species Distribution Models (SDM) can be used to predict the location of unknown populations from known species occurrences. It follows that how the data used to calibrate the models are collected can have a great impact on prediction success. We evaluated the influence of different survey designs and their interaction with the modelling technique on SDM performance. Location Iberian Peninsula Methods We examine how data recorded using seven alternative survey designs (random, systematic, environmentally stratified by class and environmentally stratified using p-median, biased due to accessibility, biased by human density aggregation and biased towards protected areas) could affect SDM predictions generated with nine modelling techniques (BIOCLIM, Gower distance, Mahalanobis distance, Euclidean distance, GLM, MaxEnt, ENFA and Random Forest). We also study how sample size, species’ characteristics and modelling technique affected SDM predictive ability, using six evaluation metrics. Results Survey design has a small effect on prediction success. Characteristics of species’ ranges rank highest among the factors affecting SDM results: the species with lower relative occurrence area (ROA) are predicted better. Model predictions are also improved when sample size is large. Main conclusions The species modelled – particularly the extent of its distribution – are the largest source of influence over SDM results. The environmental coverage of the surveys is more important than the spatial structure of the calibration data. Therefore, climatic biases in the data should be identified to avoid erroneous conclusions about the geographic patterns of species distributions.

Even with clear advantages to using internet based survey research, there are still some uncertainties to which survey methods are most conducive to an online platform. Most survey method literature, whether focusing on online, telephone, or in-person formats, tend to observe little to no differences between using various survey modes and survey results. Despite this, there is little research focused on the interaction effect between survey formatting, in terms of design and framing, and public opinion on social issues, specifically child immigration policies - a recent topic of popular debate. This paper examines an anomalous result found within the 2016 Cooperative Congressional Election Study (CCES) public opinion immigration question focusing on a DACA-related policy, where support was evenly split on the typically highly favored policy. To decipher the unprecedented result, an experimental survey design was conducted via Qualtrics by comparing various survey formats (single-style, forced choice, Likert scale) and inclusionary policy details to the original CCES “select all that apply” matrix style. By comparing the experimental polls, the results indicated that the “select all that apply” matrix again produced anomalous results, while the various other methods produced a breakdown similar to typical DACA-related polling data. These findings have necessary implications for future survey designs and those examining public opinion on child immigration policies.