Thesis advisor: Eric Dearing / Emerging research in developmental psychology and neuroscience suggests that childhood poverty is associated with high levels of exposure to multiple contextual risks, which cumulatively lead to persistent elevated stress levels that have a direct, as well an indirect (e.g., through parental processes), impact on child cognitive, academic, and socioemotional functioning (Evans & Kim, 2013). Such research has begun to change the way that scholars and practitioners envision the context of poverty, the persistence of the income-achievement gap, and the types of interventions that may be most effective in addressing disparities in children’s long-term educational success. However, research on the relations between poverty-associated stress and child outcomes is still in its infancy and many questions remain. In particular, it is unclear whether changing family economic circumstances matter, a question of concern for developmental science and public policy. Moreover, there is little work on moderators of relations between income, stress, and child outcomes, which could help identify factors that buffer children from the harm of stressful home environments. With longitudinal data from the Panel Study of Income Dynamics’ Child Development Supplement, the present study used fixed effects models to examine within-child associations between changes in family income, cumulative risk exposure (as measured by an index that includes a range of poverty-related stressors, such as economic strain, neighborhood crime, and physical and psychological home environments), and children’s cognitive, academic and socioemotional functioning. In addition, moderators of these associations were investigated in order to identify potential protective mechanisms and crucial levers for interventions and policy development. On the whole, findings were consistent with the cumulative stress model. On average, the estimated direct effects of changes in family income (i.e., prior to examining mediation or moderators) were not significant for changes in child outcomes. Yet, changes in income were, for the sample as a whole, indirectly related via changes in cumulative risk exposure: increases in income predicted decreases in cumulative risk exposure which, in turn, predicted improvements in achievement and declines in externalizing behavior. Additionally, these relations were moderated by child age, initial level of family income, and initial level of cumulative risk. / Thesis (PhD) — Boston College, 2017. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.

In the relatively sparse field of economics of prostitution, studies have in recent years investigated the relationship between prostitution legislation and the presence of sex trafficking. This study investigates the issue further by using both a cross country model with time fixed effects and a country fixed effect model to estimate the effect on the amount of sex trafficking of changing legislation to the so-called Nordic Model, something no other known study has managed to do. In contrast to what previous research has estimated, this thesis finds no significant effect on sex trafficking from changing legislation to the Nordic Model. The obvious difficulties of presenting reliable data on an illegal activity such as sex trafficking and the fact that few countries have changed their legislation to the Nordic Model are likely reasons to the insignificant results and cautions against a causal interpretation.

Thesis advisor: Ina V. S. Mullis / TIMSS and PIRLS assess representative samples of students at regular intervals, measuring trends in student achievement and student contexts for learning. Because individual students are not tracked over time, analysis of international large-scale assessment data is usually conducted cross-sectionally. Gustafsson (2007) proposed examining the data longitudinally by analyzing relationships between country-level trends in background constructs and trends in student achievement. Through longitudinal analysis of international large-scale assessment data, it becomes possible to mitigate some of the confounding factors in the analysis. This dissertation extends this country-level approach to subpopulations within countries. Adapting a pseudo-panel approach from the econometrics literature (Deaton, 1985), the proposed approach creates subpopulations by grouping students based on demographic characteristics, such as gender or parental education. Following grouping, the subpopulations with the same demographic characteristics are linked across cycles and the aggregated subpopulation means are treated as panel data and analyzed through longitudinal data analysis techniques. As demonstrated herein the primary advantages of the subpopulation approach are that it allows for analysis of subgroup differences, and it captures within-country relationships in the data that are not possible to analyze at country level. Illustrative analysis examines the relationship between early literacy activities and PIRLS reading achievement using PIRLS 2001 and PIRLS 2011 data. Results from the subpopulation approach are compared with student-level and country-level cross-sectional results as well as country-level longitudinal results. In addition, within-country analysis examines the subpopulation-level relationship between early literacy activities and PIRLS reading achievement, multiple group analysis compares regression coefficient estimates between boys and girls and across parental education subgroups, and mediation analysis examines the extent that partaking in early literacy activities can explain differences between boys and girls in PIRLS reading achievement. / Thesis (PhD) — Boston College, 2017. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

Classic parametric statistical tests, like the analysis of variance (ANOVA), are powerful tools used for comparing population means. These tests produce accurate results provided the data satisfies underlying assumptions such as homoscedasticity and balancedness, otherwise biased results are obtained. However, these assumptions are rarely satisfied in real-life. Alternative procedures must be explored. This thesis aims at investigating the impact of heteroscedasticity and unbalancedness on effect sizes in two-way fixed-effects ANOVA models. A real-life dataset, from which three different samples were simulated was used to investigate the changes in effect sizes under the influence of unequal variances and unbalancedness. The parametric bootstrap approach was proposed in case of unequal variances and non-normality. The results obtained indicated that heteroscedasticity significantly inflates effect sizes while unbalancedness has non-significant impact on effect sizes in two-way ANOVA models. However, the impact worsens when the data is both unbalanced and heteroscedastic. / Statistics / M. Sc. (Statistics)