Social learning theory continues to be one of the most enduring theories of crime. Psychological criminology, on the other hand, tends to explain crime in terms of behavioral propensities. This research is specifically focused on the generality of social learning theory as it varies across a measure of criminal propensity- in this case, psychopathy. Prior studies have tested various theories with the use of measures of propensity, but the theory is rarely social learning, and the measure of propensity has never been psychopathy. The current study examines three components of social learning theory (definitions, differential association, and differential reinforcement) to determine whether or not its influence is dependent on an individual's level of psychopathy. Data used in this research is from the Pathways to Desistance Project, a serious juvenile delinquent sample. Standard ordinary least-squares and Tobit regressions (a method of analyses designed to correct for linear relationships between variables when there is censoring in the dependent variable) are modeled. Results indicate that definitions, differential association, differential reinforcement, and both measures of psychopathy exerted significant main effects on antisocial behavior. In addition, the social learning variables interacted differently across varying levels of psychopathy. Conclusions and policy implications for future social science research are discussed within.

Much health related research depends heavily on the analysis of a rapidly expanding universe of observational data. A challenge in analysis of such data is the lack of sound statistical methods and tools that can address multiple facets of estimating treatment or exposure effects in observational studies with a large number of covariates. We sought to advance methods to improve analysis of large observational datasets with an end goal of understanding the effect of treatments or exposures on health. First we compared existing methods for propensity score (PS) adjustment, specifically Bayesian propensity scores. This concept had previously been introduced (McCandless et al., 2009) but no rigorous evaluation had been done to evaluate the impact of feedback when fitting the joint likelihood for both the PS and outcome models. We determined that unless specific steps were taken to mitigate the impact of feedback, it has the potential to distort estimates of the treatment effect. Next, we developed a method for accounting for uncertainty in confounding adjustment in the context of multiple exposures. Our method allows us to select confounders based on their association with the joint exposure and the outcome while also accounting for the uncertainty in the confounding adjustment. Finally, we developed two methods to combine het- erogenous sources of data for effect estimation, specifically information coming from a primary data source that provides information for treatments, outcomes, and a limited set of measured confounders on a large number of people and smaller supplementary data sources containing a much richer set of covariates. Our methods avoid the need to specify the full joint distribution of all covariates.

Syftet med studien är att identifiera om det finns någon skillnad i risk mellan svenska män och kvinnors aktieportföljer. I undersökningen av individernas riskbenägenhet används tre riskmått, total risk, marknadsrisk och unik risk, som enligt portföljteorin går att koppla till en individs aktieportfölj. Ålder och inkomst används som kontrollvariabler för att studera om eventuella skillnader i riskbenägenhet mellan könen kan förklaras av andra faktorer än kön. Studien baseras på en kvantitativ undersökning och sekundärdata från en unik databas. Urvalet för studien består av knappt 900 000 observationer av svenska individers aktieportföljer, med kontroll för kön, ålder och förvärvsinkomst. Resultatet av studien visar att det föreligger signifikanta skillnader mellan könens riskbenägenhet. Studien finner dock inga tydliga resultat för att män skulle vara mer riskbenägna än kvinnor, då könen tar olika hög risk beroende på vilket riskmått som avses. Resultaten visar på att skillnader mellan könens riskbenägenhet även existerar efter att variablerna ålder och inkomst studerats. / The purpose of this study is to identify whether there is any difference in risk between the stock portfolios of Swedish men and women. In the investigation of individual’ risk propensity, three measures of risk are used, total risk, market risk and unique risk, which according to the portfolio theory can be linked to an individual’s stock portfolio. Age and income are used as control variables to study whether any differences in risk propensity between the genders can be explained by other factors than gender. The study is based on a quantitative study and secondary data obtained from a unique database. The sample for the study consists of nearly 900 000 observations of Swedish individual’s stock portfolios, controlling for gender, age and income. The results of the study show that there are significant differences between the genders’ risk propensity. The study finds, however, no clear evidence that the men would be more risk-prone than women. The genders take different levels of high risk depending on which measure of risk involved. The results show that differences between the genders’ risk propensity also exists after studying the control variables age and income.

What is the developmental trajectory of the skills that underlie emergent literacy during the preschool years? Are there individual characteristics which predict whether a child will be at-risk for difficulties in acquiring literacy skills? Does a child’s experience in a high-quality early care and education environment enhance the development of his or her emergent literacy? The present study is an investigation of the individual and environmental factors relevant to children’s emergent literacy skills as they unfold in time. Using a combination of principal components analysis, growth modeling with a multi-level approach, and propensity score analysis, the trajectories of growth in emergent literacy were examined. In addition to child characteristics, the effects of early child environments on emergent literacy were also examined. The effects of home literacy environment and of high-quality early care and education environments were investigated using propensity score matching techniques. The growth in emergent literacy was examined using a nationally representative dataset, the Early Childhood Longitudinal Study – Birth cohort (ECLS-B). Child characteristics, such as primary home language and poverty, were associated with lower initial abilities and suppressed growth in emergent literacy. A high-quality home literacy environment had a strong effect on the growth of children’s emergent abilities, even after controlling for child characteristics. High-quality early care and education environments, as defined by structural attributes of the program such as class size, had a modest impact on the growth of emergent literacy skills for some but not all children. When high-quality early education was defined in terms of teacher interaction, children who are exposed to such care experienced an increase in growth of their emergent literacy abilities. This study provides an examination of individual and group paths toward literacy as an element of school readiness, including the role of environment in the development of literacy skills. These findings have implications for early education policy, especially relevant to state-funded preschool programs and Early Head Start, to provide insight into contexts in which policy and the investment of resources can contribute most effectively to early literacy development.

When researchers are unable to randomly assign students to treatment conditions, selection bias is introduced into the estimates of treatment effects. Random assignment to treatment conditions, which has historically been the scientific benchmark for causal inference, is often impossible or unethical to implement in educational systems. For example, researchers cannot deny services to those who stand to gain from participation in an academic program. Additionally, students select into a particular treatment group through processes that are impossible to control, such as those that result in a child dropping-out of high school or attending a resource-starved school. Propensity score methods provide valuable tools for removing the selection bias from quasi-experimental research designs and observational studies through modeling the treatment assignment mechanism. The utility of propensity scores has been validated for the purposes of removing selection bias when the observations are assumed to be independent; however, the ability of propensity scores to remove selection bias in a multilevel context, in which group membership plays a role in the treatment assignment, is relatively unknown. A central purpose of the current study was to begin filling in the gaps in knowledge regarding the performance of propensity scores for removing selection bias, as defined by covariate balance, in multilevel settings using a Monte Carlo simulation study. The performance of propensity scores were also examined using a large-scale national dataset. Results from this study provide support for the conclusion that multilevel characteristics of a sample have a bearing upon the performance of propensity scores to balance covariates between treatment and control groups. Findings suggest that propensity score estimation models should take into account the cluster-level effects when working with multilevel data; however, the numbers of treatment and control group individuals within each cluster must be sufficiently large to allow estimation of those effects. Propensity scores that take into account the cluster-level effects can have the added benefit of balancing covariates within each cluster as well as across the sample as a whole.

Att vara ideellt engagerad ses av många som en faktor som leder till positiva effekter för individer. Problemet med detta resonemang är att de flesta som engagerar sig aktivt även ärde som redan har dessa fördelaktigaegenskaper.Frågan är därför om det är en kausal effekt eller en stark korrelation som lett till bilden av att ideellt engagemang är positivt för individers livsstandard.Syftet med denna uppsats är att undersöka om ett aktivt engagemang inom ideella organisationer i sig gereffekter hos individerna. Studiengörs med utgång från hypotesen att socialt kapital skapas inom grupper av aktivt ideellt engagerade medlemmar och att detta i sin tur leder till positiva yttringarhos individen.Undersökningen sker genomen kvantitativ longitudinell studie som använder sig av propensity score matchingför att uppskatta den kausala effekten aktivt engagemang kan ha.Studiens resultat indikerar att ingen statistisk signifikant skillnad existerar mellan en person som varitaktiv i en ideell organisationoch en person som ej varit det, vilket tyder på att det aktiva engagemanget inom en ideell organisation i sig inte ger någon effekt. Dessa resultat förkastar därmed hypotesen att aktivt engagemang inom ideella organisationer i sig leder till positiva effekter genom det sociala kapital som skaffas inom denna form av nätverk.

Conventional charcoal and firewood are the main source of energy in Haiti. They provide up to 90% of the country’s energy for domestic and industrial use, resulting in severe environmental and health issues. The present study is initiated to better understand the reasons why two promising alternative technologies (improved cookstoves and alternative charcoal briquettes) have experienced low adoption in Haiti. The research was carried out in two districts in southern Haiti where the improved stoves and briquettes production units exist and where households benefited from a program distributing the improved stoves. This project contributes to the literature by gauging interest in the improved stove and briquettes, as well as their specific characteristics. It helps understand factors that affect the adoption and dis-adoption of the technologies. Additionally, the research measures tangible benefits for households that adopted the improved stoves. The study reveals that the use of the improved stoves lowers fuel expenditures by 14.6 cents/day to 23.6 cents/day. Haitian consumers are interested in both the stove and briquettes, but their willingness-to-pay depends on their personal characteristics such as location and income. The study has revealed two surprising results as well: Unnecessary dis-adoption of the stoves occurs because the two technologies were needlessly marketed together. Despite the target audience, which is poor and rural consumers, the improved stove is perceived as a rich, urban user’s technology.

No description available.

觀察性研究資料中，透過傾向分數的使用，可以使基準變數在實驗與對照兩組間達到某種程度的平衡，並可視同為一隨機試驗，進而進行有效的統計推論。文獻中有關平衡與否的診斷，大多聚焦於平均數與變異數的比較。本文中我們提出使用KLIC(Kullback-Leibler Information Criterion)及KS(Kolmogorov and Simonov)兩種比較分配函數差異的統計量，作為另一種平衡診斷工具的構想，並針對其可行性進行探討與評比。此外，數據顯示KLIC及KS與透過傾向分數配對的成功比例呈現負相關。由於配對成功比例過低將導致後續統計推論結果的侷限性，因此本文也就KLIC及KS作為是否進行配對的一個先行指標之可行性作探討。模擬結果顯示，二者的答案均是肯定的。 / In observational studies, propensity scores are frequently used as tools to balance the distribution of baseline covariates between treated and untreated groups to some extent so that the data could be treated as if they were from a randomized controlled trial (RCT) and causal inferences could thus be made. In the past, balance or not was usually diagnosed in terms of the means and/or the variances. In this study, we proposed using either Kullback-Leibler Information Criterion (KLIC) or Kolmogorov and Simonov (KS) statistic as a diagnostic measure, and evaluated its feasibility. In addition, since low propensity score matching rate decreases the power of the statistical inference and a pilot study showed that the matching rate was negatively correlated with KLIC and KS; thus, we also discussed the possibilities of using KLIC and KS to be pre-indices before implementing propensity score matching. Both considerations appear to be positive through our simulation study.

In this thesis we develop a theoretical framework for the identification of situations where the equal frequency (EF) or equal variance (EV) subclassification may produce lower bias and/or variance of the estimator. We conduct simulation studies to examine the EF and EV approaches under different types of model misspecification. We apply two weighting schemes in our simulations: equal weights (EW) and inverse variance (IV) weights. Our simulation results indicate that under the quadratic term misspecification, the EF-IV estimator provides the lowest bias and root mean square error as compared to the ordinary least square estimator and other propensity score estimators. Our theorem development demonstrates that if higher variation occurs with larger bias for within subclass treatment effect estimates then the EF-IV estimator has a smaller overall bias than the EF-EW estimator. We show that the EF-IV estimator always has a smaller variance than the EF-EW estimator. We also propose a novel method of subclassification that focuses on creating homogeneous propensity score subclasses to produce an estimator with reduced biased in some circumstances. We feel our research contributes to the field of propensity score adjustments by providing new theorems to compare the overall bias and variance between different propensity score estimators. / Graduation date: 2012