abstract: Despite the strong link between pain and depressive symptoms, the mechanisms by which they are connected in the everyday lives of individuals with chronic pain are not well understood. In addition, previous investigations have tended to ignore biopsychosocial individual difference factors, assuming that all individuals respond to pain-related experiences and affect in the same manner. The present study tried to address these gaps in the existing literature. Two hundred twenty individuals with Fibromyalgia completed daily diaries during the morning, afternoon, and evening for 21 days. Findings were generally consistent with the hypotheses. Multilevel structural equation modeling revealed that morning pain and positive and negative affect are uniquely associated with morning negative pain appraisal, which in turn, is positively related to pain’s activity interference in the afternoon. Pain’s activity interference was the strongest predictor of evening depressive symptoms. Latent profile analysis using biopsychosocial measures identified three theoretically and clinically important subgroups (i.e., Low Functioning, Normative, and High Functioning groups). Although the daily pain-depressive symptoms link was not significantly moderated by these subgroups, individuals in the High Functioning group reported the lowest levels of average morning pain, negative affect, negative pain appraisal, afternoon pain’s activity interference, and evening depressive symptoms, and the highest levels of average morning positive affect across 21 days relative to the other two groups. The Normative group fared better on all measures than did the Low Functioning group. The findings of the present study suggest the importance of promoting morning positive affect and decreasing negative affect in disconnecting the within-day pain-depressive symptoms link, as well as the potential value of tailoring chronic pain interventions to those individuals who are in the greatest need. / Dissertation/Thesis / Doctoral Dissertation Psychology 2018

Introdução: Muitos estudos epidemiológicos utilizam amostragem complexa para coleta de dados. A amostragem complexa pode ter uma ou mais das seguintes características: estratos, conglomerados e probabilidades desiguais de seleção. Se estas características não forem incorporadas na análise de dados, as estimativas pontuais e erros-padrões são incorretos. Assim é necessário ampliar a compreensão do impacto de cada característica nos resultados para incentivar os pesquisadores a utilizarem metodologias adequadas para análise dos dados, obtendo conclusões válidas para a população de onde provém a amostra. Para tratar as estruturas complexas do plano amostral existem duas principais metodologias: abordagem da amostragem complexa e abordagem de modelos multinível. Objetivos: Descrever e comparar métodos para tratamento de dados provindos de planos amostrais complexos através de duas abordagens: amostragem complexa e modelos multinível, utilizando dados de dois estudos epidemiológicos. Métodos: Para avaliar o impacto do plano amostral complexo, assim como de cada característica do plano amostral nas estimativas de média, proporção, coeficientes da regressão de Poisson e seus correspondentes erros padrões utilizaram-se os dados da busca ativa domiciliar dos participantes na Campanha Nacional de Detecção de Diabetes Mellitus – CNDDM de 2001, obtidos por amostragem estratificada com conglomerado em três estágios. Para comparar a abordagem da amostragem complexa e a abordagem de modelos multinível ajustaram-se modelos de regressão linear com e sem pesos amostrais utilizando os dados de um estudo do desempenho das crianças na avaliação de conhecimento, percepções e crenças sobre aleitamento materno, realizado com escolares da quinta série do ensino fundamental, no município de Ijuí/RS, estudo aleatorizado, com amostra estratificada por conglomerados. Resultados: As estimativas pontuais de média e proporção são semelhantes comparando-se amostragem complexa e amostragem aleatória simples, porém observou-se grande diferença nos erros padrões. O mesmo foi observado nas estimativas dos coeficientes da regressão de Poisson com menor efeito do plano amostral. Na comparação da abordagem da amostragem complexa com modelos multinível observou-se diferença nos erros padrões dos coeficientes da regressão entre as duas abordagens, sendo que os mesmos são maiores na amostragem complexa. Também, na análise não ponderada, as significâncias dos coeficientes no modelo final foram semelhantes entre as duas abordagens, porém houve diferença na análise ponderada para um dos coeficientes. Conclusões: Os resultados encontrados a partir dos dois estudos evidenciaram a necessidade de incorporar a complexidade do plano amostral na análise dos dados. A questão de pesquisa poderá ser um fator importante na escolha entre a abordagem da amostragem complexa e a abordagem de modelos multinível. / Introduction: Many epidemiological studies use complex samples for data collection. Complex sampling may have one or more of the following characteristics: stratification, clustering and unequal selection probabilities. If these characteristics are not incorporated into data analysis, point estimates and standard errors are incorrect. Greater understanding of the effect of each characteristic on results should stimulate researchers to use adequate methods for data analysis and, therefore, to reach conclusions that are valid for the population that generated the sample. Two major methods are used to deal with complex sampling designs: the complex sample approach and the multilevel model approach. Objective: To describe and compare methods to deal with data in complex sampling designs using complex sample and multilevel model approaches in two epidemiological studies. Method: Data retrieved from a house-to-house survey of participants in the 2001 Brazilian Diabetes Detection Campaign (Campanha Nacional de Detecção de Diabetes Melitus - CNDDM) and collected by stratified clustering sampling in three stages were used to evaluate the impact of complex sampling designs, as well as of each of their characteristics, on the estimates of means, proportions, Poisson regression coefficients and their corresponding standard errors. To compare the complex sample and the multilevel model approaches, linear regression models were adjusted with and without sample weights using data from a random study that used stratified cluster sampling and investigated the performance of children in the evaluation of knowledge, perceptions and beliefs about maternal breastfeeding conducted with fifth grade students in Ijuí, Brazil. Results: Mean and proportion point estimates were similar when complex sampling and simple random sampling were compared, but there was a great difference in standard errors. The same was found for estimates of Poisson regression coefficients that were less affected by sampling design. The complex sample approach showed significantly greater standard errors of the regression coefficients than the multilevel model approach. Also, unweighted analysis showed that the significance of coefficients in the final models was similar in the two approaches, but there was a difference in one of the coefficients in weighted analysis. Conclusions: Results of the two studies showed that sampling design complexity should be incorporated into data analysis. Research questions seem to be a determinant factor in the choice of either a complex sample or a multilevel model approach.

The purpose of this study was to examine the longitudinal impacts of the Science Writing Heuristic (SWH) approach on student science achievement measured by the Iowa Test of Basic Skills (ITBS). A number of studies have reported positive impact of an inquiry-based instruction on student achievement, critical thinking skills, reasoning skills, attitude toward science, etc. So far, studies have focused on exploring how an intervention affects student achievement using teacher/researcher-generated measurement. Only a few studies have attempted to explore the long-term impacts of an intervention on student science achievement measured by standardized tests. The students' science and reading ITBS data was collected from 2000 to 2011 from a school district which had adopted the SWH approach as the main approach in science classrooms since 2002. The data consisted of 12,350 data points from 3,039 students. The multilevel model for change with discontinuity in elevation and slope technique was used to analyze changes in student science achievement growth trajectories prior and after adopting the SWH approach. The results showed that the SWH approach positively impacted students by initially raising science achievement scores. The initial impact was maintained and gradually increased when students were continuously exposed to the SWH approach. Disadvantaged students who were at risk of having low science achievement had bigger benefits from experience with the SWH approach. As a result, existing problematic achievement gaps were narrowed down. Moreover, students who started experience with the SWH approach as early as elementary school seemed to have better science achievement growth compared to students who started experiencing with the SWH approach only in high school. The results found in this study not only confirmed the positive impacts of the SWH approach on student achievement, but also demonstrated additive impacts found when students had longitudinal experiences with the approach. By engaging in the argument-based classrooms where teachers value students' prior knowledge, encourage students to take control of their learning, and provide non-threatening environment for students to developing big ideas through negotiation, student's achievement can be enhanced. The results also started to shed some light on sustainability of the SWH approach within the school district.

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Thesis (M. Sc. (Statistics) / Most social and health science data are longitudinal and additionally multilevel in nature, which means that response data are grouped by attributes of some cluster. Ignoring the differences and similarities generated by these clusters results to misleading estimates, hence motivating for a need to assess variance components (VCs) using multilevel models (MLMs) or generalised linear mixed models (GLMMs). This study has explored and fitted teenage pregnancy census data that were gathered from 2011 to 2015 by the Africa Centre at Kwa-Zulu Natal, South Africa. The exploration of these data revealed a two level pure hierarchy data structure of teenage pregnancy status for some years nested within female teenagers. To fit these data, the effects that census year (year) and three female characteristics (namely age (age), number of household membership (idhhms), number of children before observation year (nch) have on teenage pregnancy were examined. Model building of this work, firstly, fitted a logit gen eralised linear model (GLM) under the assumption that teenage pregnancy measurements are independent between females and secondly, fitted a GLMM or MLM of female random effect. A better fit GLMM indicated, for an additional year on year, a 0.203 decrease on the log odds of teenage pregnancy while GLM suggested a 0.21 decrease and 0.557 increase for each additional year on age and year, respectively. A GLM with only year effect uncovered a fixed estimate which is higher, by 0.04, than that of a better fit GLMM. The inconsistency in the effect of year was caused by a significant female cluster variance of approximately 0.35 that was used to compute the VCs. Given the effect of year, the VCs suggested that 9.5% of the differences in teenage pregnancy lies between females while 0.095 similarities (scale from 0 to 1) are for the same female. It was also revealed that year does not vary within females. Apart from the small differences between observed estimates of the fitted GLM and GLMM, this work produced evidence that accounting for cluster effect improves accuracy of estimates. Keywords: Multilevel Model, Generalised Linear Mixed Model, Variance Components, Hier archical Data Structure, Social Science Data, Teenage Pregnancy

The increase in right wing populist parties in Europe combined with the sudden influx of asylum applicants has given rise to the debate regarding immigration both politically and within research. This paper sets out to examine the relation between asylum seeker group size and people’s attitudes towards immigration. Based on group threat theory and ethnic competition theory we hypothesize that countries´ increases in asylum seekers is correlated with decreases in attitudinal support for immigration. We test this hypothesis using cross- national time series survey data from the Eurobarometer from 2014 to 2017 and conducting a multilevel analysis. Despite the extensive theoretical arguments that strengthen the hypothesis, we find no evidence that the group size of asylum seekers is related to attitudes towards immigration from outside EU.

Vehicles being driven for work purposes represent a large proportion of road collision and deaths in the workplace. These observations mean that people driving for work can impose a large burden on organisations and on society. In addition, previous studies identified a fleet driver effect for which there was greater collision risk for those who drive for work compared to the general driving population, even after controlling for exposure. This accentuates the need for both organisational and government policy makers to take steps to reduce the impact of these collisions. No single intervention has been found to solve issues around work-related road safety therefore a range of initiatives have been directed towards the risks associated with drivers, vehicles, journeys and organisations. Many of the interventions, however, lack robust evidence to support their use. The aim of this thesis is to assess organisational interventions to improve work-related road safety by using econometric models on real-world data. The data represents driving claims made between 2005 and 2012 by employees of a large UK company, with a fleet of approximately 35,000 vehicles. The drivers were employed in a variety of roles such as working in technical positions at customer sites or making sales visits. The company has applied a range of strategies to road safety resulting in annual claim reductions of 7.7% compared to only a 4.5% reduction in collisions nationally. The company s data are used to undertake three studies which focused on driver training, manager training and claim segmentation. Statistical models were employed to investigate the effect of two different driver training courses on the frequency of claims while controlling for other factors. The results indicated that driver training courses significantly reduced both the total number of claims and the claim types targeted by the training. The impacts of the interventions were also adjusted for the effects of non-random driver selection and other safety improvements initiated by the company or other agencies. An important finding of this work was that randomly inflated pre-training events accounted for between a third and a quarter of the observed reduction in claims following training. The second study evaluated the impact of management training on claims using multilevel models which allowed for correlation between observations. The study could not confirm that this training was an effective safety intervention. This null result provides an incentive to re-evaluate the implementation of the scheme. The final study identified homogeneous claim segments using statistical models and the impact of training was evaluated on these segments. Such claims were estimated to be reduced by between 32% to 55% following existing driver training courses. This thesis has helped close important gaps and contributed to knowledge in terms of both intervention methodology and the understanding of the effectiveness of work-related road safety interventions. The results, which are already being applied in the case study organisation, demonstrated that training employees in either safe and fuel efficient driving, or low speed manoeuvring, reduced vehicle insurance claims. Further work is necessary to verify the safety value of manager training including gathering detailed information on interactions between managers and drivers.

This thesis consists of an introductory part and the following four self-contained papers: In Paper [I] we analyze the implications of social identity and self-categorization for optimal redistributive income taxation. A two-type model is supplemented by an assumption that individuals select themselves into social categories, in which norms are formed and education effort choices partly depend on these norms. The results show, among other things, that externality correction by a welfarist government leads to an element of tax progression that serves to reduce the discrepancy between the effort norm and the actual effort chosen by low-productivity individuals in the high-effort group. Furthermore, if the preference for social identity is sufficiently strong, increased wage-inequality leads to higher social welfare through a relaxation of the selection constraint. It may thus be desirable to use publicly provided education to induce more wage-inequality, even if higher wage-inequality increases the intrinsic utility of a potential mimicker. In Paper [II] I employ high quality register data to present new facts about income mobility in Sweden. The focus of the paper is regional differences in mobility, using a novel approach based on a multilevel model. This method is well-suited when regions differ greatly in population size as is the case in Sweden. The maximum likelihood estimates are substantially more precise than those obtained by running separate OLS regressions. I find small regional differences in income mobility when measured in relative terms. Regional differences are large when adopting an absolute measure and focusing on children with below-median parent income. On the national level I find that the association between parent and child income ranks has decreased over time, implying increased mobility. In Paper [III] I study the long term effects of inter-municipal moving during childhood on income using Swedish register data. Due to the richness of the data I am able to control for important sources of selection into moving, such as parent separation, parents' unemployment, education, long run income, and immigration background. I find that children's long run incomes are significantly negatively affected by moving during childhood, and the effect is larger for those who move more often. For children who move once, I also estimate the effect of the timing and the quality of the move. I measure the quality of each neighborhood based on the adult outcomes for individuals who never move. The quality of a move is defined as the difference in quality between the origin and the destination. Given that a family moves, I find that the negative effect of childhood moving on adult income is increasing in age at move. Children benefit economically from the quality of the region they move to only if they move before age 12 (sons) and age 16 (daughters). In Paper [IV] I study the bias of IGE estimates for different missing-data scenarios based on simulated income processes. Using an income process from the income dynamics and risks literature to generate two linked generations’ complete income histories, I use Monte Carlo methods to study the relationship between available data patterns and the bias of the IGE. I find that the traditional approach using the average of the typically available log income observations leads to IGE estimates that are around 40 percent too small. Moreover, I show that the attenuation bias is not reduced by averaging over many father income observations. Using just one income observation for each generation at the optimal age (as discussed in the paper) or using weighted instead of unweighted averages can reduce the bias. In addition, the rank-rank slope is found to be clearly less sensitive to missing data.