This study investigates the social networks impact on mental health. Further, it examines the role of digital tools in regard to loneliness among elderly people. Several studies have investigated similar questions before, but further knowledge can be added with this study on the Swedish population. The aim of the study is to enhance the knowledge on whether social networks have an impact on individual’s mental health. The theoretical framework consists of Granovetter’s strenght-of-weak-ties and Putnam’s definition of social capital. A quantitative approach is used, and secondary analysis is made on an existing data set. The data originates from a survey used to measure social networks in Sweden. To get further insight, an ordinal regression model was built in order to sort out the potential impact of the different variables. The result shows that elderly peoplewho use digital tools are less likely to feel lonely in comparison to those who are non-users. Also, both weak and strong social ties seem to have a positive effect on mental health. Finally, social capital is also proved to have an impact on mental health. The most important result show that trusting people was the strongest correlated variable with mental health in regard to the components of social capital. Further, those who feel trust for others are more likely to report a better mental wellbeing. For future research we recommend focusing on a specific age group, for example relevant to our findings, this could be the elder population. To gain enhanced knowledge, we recommend a mixed methods approach in order toget a deeper understanding.

Botswana is a middle-income economy. It has become dependent on non-renewable resources. Agriculture and manufacturing have failed to develop in a significant way. The small population has not provided adequate demand. The Government has tried a number of strategies in order to diversify the economy. One of these is the promotion of Small and Medium Enterprises (SMEs). Unfortunately, SMEs have failed to thrive, so far. A number of researchers have concluded that SMEs will not thrive due to the fact that Botswana have low entrepreneurial skills. High failure rates are experienced where enterprises are established. The thesis identifies an alternative strategy in the development of enterprises in Botswana. It is generally believed that a franchisee does not need high levels of entrepreneurial skills to succeed. If this is so, Botswana can solve her problem of lack of sufficient enterprises by promoting franchising. The thesis uses the interview technique to assess whether existing franchisees in Botswana have low levels of entrepreneurial skills. Indeed it proves that franchisees have low skill levels compared to non-franchised entrepreneurs. Secondly, the thesis proves that franchisees in Botswana operate as employee-managers. Thirdly, the thesis establishes that franchisees perform better than non-franchised entrepreneurs, even though they have low entrepreneurial skill levels. Lastly, the thesis, using evidence from findings above, and from responses of experts interviewed, establishes that the promotion of franchising is a viable alternative strategy to one that depends solely on non-franchised enterprises.While there are some methodological limitations, like those stemming from a low and unknown franchisee population in Botswana, the use of ordinal data, use of techniques to rate their own skills, and a relatively small sample for franchised and non-franchised entrepreneurs, the statistical techniques used are powerful enough to generate reliable findings. / Graduate School of Business Leadership / D.B.L

This thesis consists of four self-contained essays in economics, all concerned with different aspects of subjective well-being. The abstracts of the four studies are as follows. Beyond Income: The Importance for Life Satisfaction of Having Access to a Cash Margin. We study how life satisfaction among adult Swedes is influenced by having access to a cash margin, i.e. a moderate amount of money that could be acquired on short notice either through own savings, by loan from family or friends, or by other means. We find that cash margin is a strong and robust predictor of life satisfaction, also when controlling for individual fixed effects and socio-economic conditions, including income. Decomposing Variation in Daily Feelings: The Role of Time Use and Individual Characteristics. I explore the potential of using time-use data for understanding variation in affective well-being. Using the Princeton Affect and Time Survey, I decompose variation in daily affect into explained and unexplained within- and between person variation. Time use is found to mostly account for within-variation. Hence, its explanatory power is largely additive to that of individual characteristics. The explanatory power of time use is small, however. Activities only account for 1–7% of the total variation and this is not increased much by adding contextual variables. The Association Between Life Satisfaction and Affective Well-Being. We estimate the correlation between life satisfaction and affect — two conceptually distinct dimensions of subjective well-being. We propose a simple model that distinguishes between a stable and a transitory component of affect, and which also accounts for measurement error in self-reports of both variables, including current-mood bias effects on life satisfaction judgments. The model is estimated using momentarily measured well-being data, from an experience sampling survey that we conducted on a population sample of Swedes aged 18–50 (n=252). Our main estimates of the correlation between life satisfaction and long-run affective well-being range between 0.78 and 0.91, indicating a stronger convergence between these variables than many previous studies that do not account for measurement issues. Do OLS and Ordinal Happiness Regressions Yield Different Results? A Quantitative Assessment. Self-reported subjective well-being scores are often viewed as ordinal variables, but the conventional wisdom has it that OLS and ordered regression models (e.g. ordered probit) produce similar results when applied to such data. This claim has rarely been assessed formally, however, in particular with respect to quantifying the differences. I shed light on this issue by comparing the results from OLS and different ordered regression models, in terms of both statistical and economic significance, and across data sets with different response scales for measuring life satisfaction. The results are mixed. The differences between OLS, probit and logit estimates are typically small when the response scale has few categories, but larger, though not huge, when an 11-point scale is used. Moreover, when the error term is assumed to follow a skewed distribution, larger discrepancies are found throughout. I find a similar pattern in simulations, in which I assess how different methods perform with respect to the true parameters of interest, rather than to each other.

Ordinal scales are commonly used to measure health status and disease related outcomes in hospital settings as well as in translational medical research. Notable examples include cancer staging, which is a five-category ordinal scale indicating tumor size, node involvement, and likelihood of metastasizing. Glasgow Coma Scale (GCS), which gives a reliable and objective assessment of conscious status of a patient, is an ordinal scaled measure. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical ordinal modeling methods based on the likelihood approach have contributed to the analysis of data in which the response categories are ordered and the number of covariates (p) is smaller than the sample size (n). With the emergence of genomic technologies being increasingly applied for obtaining a more accurate diagnosis and prognosis, a novel type of data, known as high-dimensional data where the number of covariates (p) is much larger than the number of samples (n), are generated. However, corresponding statistical methodologies as well as computational software are lacking for analyzing high-dimensional data with an ordinal or a longitudinal ordinal response. In this thesis, we develop a regularization algorithm to build a parsimonious model for predicting an ordinal response. In addition, we utilize the classical ordinal model with longitudinal measurements to incorporate the cutting-edge data mining tool for a comprehensive understanding of the causes of complex disease on both the molecular level and environmental level. Moreover, we develop the corresponding R package for general utilization. The algorithm was applied to several real datasets as well as to simulated data to demonstrate the efficiency in variable selection and precision in prediction and classification. The four real datasets are from: 1) the National Institute of Mental Health Schizophrenia Collaborative Study; 2) the San Diego Health Services Research Example; 3) A gene expression experiment to understand `Decreased Expression of Intelectin 1 in The Human Airway Epithelium of Smokers Compared to Nonsmokers' by Weill Cornell Medical College; and 4) the National Institute of General Medical Sciences Inflammation and the Host Response to Burn Injury Collaborative Study.

A common problem in regression analysis is that some covariates are measured with errors. In this dissertation we present simulation-based approach to estimation in two popular regression models with a categorical response variable and classical measurement errors in covariates. The first model is the regression model with a binary response variable. The second one is the proportional odds regression with an ordinal response variable. In both regression models we consider method of moments estimators for therein unknown parameters that are defined via minimizing respective objective functions. The later functions involve multiple integrals and make obtaining of such estimators unfeasible. To overcome this computational difficulty, we propose Simulation-Based Estimators (SBE). This method does not require parametric assumptions for the distributions of the unobserved covariates and error components. We prove consistency and asymptotic normality of the proposed SBE's under some regularity conditions. We also examine the performance of the SBE's in finite-sample situations through simulation studies and two real data sets: the data set from the AIDS Clinical Trial Group (ACTG175) study for our logistic and probit regression models and one from the Adult Literacy and Life Skills (ALL) Survey for our regression model with the ordinal response variable and mismeasured covariates. / October 2016

高級人力的培育和充分利用, 是政府施政的基本目標,當我們論及高級人 力的運用時,具有大專(含)以上學歷的工作者對目前職業的滿意程度是不 能忽略的一項因素。「職業滿意度」是指就業者在工作情境中所得到的心 理反應狀態而言。滿意程度的高低, 會影響到工作的效率,以至於整個工 作單位的績效。因此, 工作者滿意程度與否,亦為探討人力運用問題時重 要的一環。本文以實際問卷調查資料,就影響職業滿意度的因素對就業者 的職業滿意度作判別分析。傳統上的判別分析方法,都是用來處理連續性 資料, 而本文所要分析的資料都是離散資料 , 即屬質化(qualitative)的 變數。文中介紹一種離散資料的判別分析方法, 及應用在實際資料的分析 結果;另外,本文進而嘗試以一般用於處理連續性資料的常態假設及無母數 統計法來分析離散資料, 所得的判別結果與離散資料 判別法相比較。最 後本文以叢聚分析法, 來討論職業別對職業滿意度的叢聚狀況。

One of the most frequently encountered types of data is where the response variables are measured on an ordinal scale. Although there have been substantial developments in the statistical techniques for the analysis of ordinal data, methods appropriate for repeatedly assessed ordinal data collected from field experiments are limited. A series of biennial field screening trials for evaluating cultivar resistance of potato to the disease, late blight, caused by the fungus Phytophthora infestans (Mont.) de Bary has been conducted by the New Zealand Institute of Crop and Food Research since 1983. In each trial, the progression of late blight was visually assessed several times during the planting season using a nine-point ordinal scale based on the percentage of necrotic tissues. As for many other agricultural field experiments, spatial differences between the experimental units is one of the major concerns in the analysis of data from the potato late blight trial. The aim of this thesis is to construct a statistical model which can be used to analyse the data collected from the series of potato late blight trials. We review existing methodologies for analysing ordinal data with mixed effects particularly those methods in the Bayesian framework. Using data collected from the potato late blight trials we develop a Bayesian hierarchical model for the analyses of repeatedly assessed ordinal scores with spatial effects, in particular the time dependence of the scores assessed on the same experimental units was modelled by a sigmoid logistic curve. Data collected from the potato late blight trials demonstrated the importance of spatial effects in agricultural field trials. These effects cannot be neglected when analysing such data. Although statistical methods can be refined to account for the complexity of the data, appropriate trial design still plays a central role in field experiments. / Accompanying dataset is at http://hdl.handle.net/2292/5240

Wavelet methods possess versatile properties for statistical applications. We would like to explore the advantages of using wavelets in the analyses in two different research areas. First of all, we develop an integrated tool for online detection of network anomalies. We consider statistical change point detection algorithms, for both local changes in the variance and for jumps detection, and propose modified versions of these algorithms based on moving window techniques. We investigate performances on simulated data and on network traffic data with several superimposed attacks. All detection methods are based on wavelet packets transformations. We also propose a Bayesian model for the analysis of high-throughput data where the outcome of interest has a natural ordering. The method provides a unified approach for identifying relevant markers and predicting class memberships. This is accomplished by building a stochastic search variable selection method into an ordinal model. We apply the methodology to the analysis of proteomic studies in prostate cancer. We explore wavelet-based techniques to remove noise from the protein mass spectra. The goal is to identify protein markers associated with prostate-specific antigen (PSA) level, an ordinal diagnostic measure currently used to stratify patients into different risk groups.

One of the most frequently encountered types of data is where the response variables are measured on an ordinal scale. Although there have been substantial developments in the statistical techniques for the analysis of ordinal data, methods appropriate for repeatedly assessed ordinal data collected from field experiments are limited. A series of biennial field screening trials for evaluating cultivar resistance of potato to the disease, late blight, caused by the fungus Phytophthora infestans (Mont.) de Bary has been conducted by the New Zealand Institute of Crop and Food Research since 1983. In each trial, the progression of late blight was visually assessed several times during the planting season using a nine-point ordinal scale based on the percentage of necrotic tissues. As for many other agricultural field experiments, spatial differences between the experimental units is one of the major concerns in the analysis of data from the potato late blight trial. The aim of this thesis is to construct a statistical model which can be used to analyse the data collected from the series of potato late blight trials. We review existing methodologies for analysing ordinal data with mixed effects particularly those methods in the Bayesian framework. Using data collected from the potato late blight trials we develop a Bayesian hierarchical model for the analyses of repeatedly assessed ordinal scores with spatial effects, in particular the time dependence of the scores assessed on the same experimental units was modelled by a sigmoid logistic curve. Data collected from the potato late blight trials demonstrated the importance of spatial effects in agricultural field trials. These effects cannot be neglected when analysing such data. Although statistical methods can be refined to account for the complexity of the data, appropriate trial design still plays a central role in field experiments. / Accompanying dataset is at http://hdl.handle.net/2292/5240

One of the most frequently encountered types of data is where the response variables are measured on an ordinal scale. Although there have been substantial developments in the statistical techniques for the analysis of ordinal data, methods appropriate for repeatedly assessed ordinal data collected from field experiments are limited. A series of biennial field screening trials for evaluating cultivar resistance of potato to the disease, late blight, caused by the fungus Phytophthora infestans (Mont.) de Bary has been conducted by the New Zealand Institute of Crop and Food Research since 1983. In each trial, the progression of late blight was visually assessed several times during the planting season using a nine-point ordinal scale based on the percentage of necrotic tissues. As for many other agricultural field experiments, spatial differences between the experimental units is one of the major concerns in the analysis of data from the potato late blight trial. The aim of this thesis is to construct a statistical model which can be used to analyse the data collected from the series of potato late blight trials. We review existing methodologies for analysing ordinal data with mixed effects particularly those methods in the Bayesian framework. Using data collected from the potato late blight trials we develop a Bayesian hierarchical model for the analyses of repeatedly assessed ordinal scores with spatial effects, in particular the time dependence of the scores assessed on the same experimental units was modelled by a sigmoid logistic curve. Data collected from the potato late blight trials demonstrated the importance of spatial effects in agricultural field trials. These effects cannot be neglected when analysing such data. Although statistical methods can be refined to account for the complexity of the data, appropriate trial design still plays a central role in field experiments. / Accompanying dataset is at http://hdl.handle.net/2292/5240