Thesis advisor: Mary E. Walsh / Non-academic factors significantly impact the achievement of students living in poverty (Ladd, 2012; Rothstein, 2010), with experts arguing for a systemic approach to address the complex factors associated with the growing opportunity gap (Bryk et al., 2010; Walsh & Murphy, 2003). In response, the integrated student support (ISS) model has emerged as one effective school-based method (Moore et al., 2017). Notably, both school social workers (SSWs) and school counselors are uniquely positioned to implement ISS models (Olsen, 2016; Teasley & Richard, 2017). School social work reflects a history of working primarily within the family and mental health domains, utilizing community partnerships to deliver intensive interventions to individual, at-risk students (Kelly et al., 2015a). Meanwhile, school counseling reflects a history of emphasizing the academic and social-emotional domains, utilizing individual, whole-school prevention and early intervention (Galassi & Akos, 2012). There is, however, little research on how these distinct professional histories influence systemic intervention implementation. Sequential explanatory mixed methods analyses examined how SSWs and school counselors implemented one systemic ISS model, City Connects, in high-poverty urban schools. Quantitative analyses examined the developmental domain (academic, mental health, family), intensity level (prevention/early, intensive intervention), and provider (school, community, Coordinator) of service referrals to determine differences depending on profession, while controlling for years of practice with City Connects and number of students served. Qualitative analyses bolstered quantitative findings. Post-hoc quantitative analyses further explored outcomes. Findings revealed no differences between school social workers’ and school counselors’ referrals across developmental domains, intensity levels, and service providers, irrespective of the number of years of practice and number of students served. Both professions believed the City Connects model increased the range of their responsibilities, impact on students, and satisfaction compared to their previous, more traditional roles. SSWs reported spending more time supporting families than school counselors in post-hoc analyses. Codified models of practice, like City Connects, may reduce potential practice differences associated with profession, develop and shift professional dispositions, encourage mindset shifts, and increase perceptions of increased impact and satisfaction. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.

Thesis advisor: Henry I. Braun / City Connects is a school-based model that identifies the strengths and needs of every student and links each child to a tailored set of intervention, prevention, and enrichment services in the school or community. The purpose of this study was to conduct a comprehensive evaluation of the City Connects treatment effects on academic performance (both MCAS scores and grade point average (GPA) grades) in middle school using student longitudinal records. Parallel analyses were conducted: one evaluated the City Connects elementary intervention (serving kindergarten to fifth grades) and the other one evaluated the City Connects middle school intervention (serving sixth to eighth grades). A series of two-level hierarchical linear models with middle school achievement scores adjusted and/or propensity score weights applied were used to answer the research questions of interest. In addition, to make a causal inference, a sensitivity analysis was conducted to examine whether or not the estimated treatment effects resulted from the first two analyses were robust to the presence of unobserved selection bias. The results showed that students who were exposed to the City Connects elementary intervention significantly outperformed their counterparts, who graduated from the comparison elementary schools, on academic achievement in all middle school grades. However, in the case of the City Connects intervention schools that served middle school grades, since all students only received a maximum of one year of City Connects middle school intervention, it was still too soon to expect any significant changes. Moreover, the estimated treatment effects of the City Connects elementary intervention were only mildly sensitive to the presence of some forms of hidden bias, which made the causal inference of City Connects on middle school academic achievement quite plausible. / Thesis (PhD) — Boston College, 2015. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

Thesis advisor: Mary E. Walsh / With an increase in emphasis on individual uniqueness and multi-contextual influences, developmental and intervention/prevention science along with similar fields of research (e.g., personalized medicine, personalized learning, health communication, business marketing) have promoted the design and implementation of interventions that would tailor responses and strategies to optimize targeted outcomes based on individual needs and variability (Joyner & Paneth, 2019; Kreuter et al., 1999; Vesanen, 2007). However, in spite of the effort and resources invested in personalization in the past decades, evidence for the realization and utility of tailored interventions have been more anecdotal than quantitatively empirical. The majority of person-centered studies have been qualitative (Lerner et al., 2019). While there is little agreement on what “tailoring” means across the different fields of study, there is a consensus that the term “tailoring” and tailoring-related terms (e.g., personalization, individualization, differentiation, and customization) lack a common and feasible theoretical foundation. Consequently, this semantic crisis has made the construct increasingly difficult to conceptualize and operationalize (e.g. Economist Group, 2021; Shemshack & Spector, 2020). Drawing on insights from the Specificity Principle, Orthogenetic Principle, and Developmental Contextualism in developmental science, this dissertation proposed a provisional definition of “tailoring”: the process of matching unique patterns of services based on each student’s cumulative strengths and needs and the availability of services (e.g., Bornstein, 2015; Lerner et al., 1998; Walsh et al., 2002; Werner & Kaplan, 1956). Guided by this definition, this dissertation sought to find evidence of “tailoring” in one “whole-child,” school-based/evidence-based Integrated Student Support (ISS): City Connects. City Connects partners with school personnel and multiple community agencies to systematically and cost-effectively allocate services/resources to students and their families from low-income communities in order to promote strengths, address needs, and mitigate risks (Moore & Emig, 2014; Dearing et al. 2016; Walsh & Theodorakakis, 2017). After establishing a theoretically-informed basis for “tailoring” as an operationalizable construct, this dissertation employed a comprehensive, three-dimensional approach to data analysis: nomothetic (for finding general/ “universal” trends), differential (for finding differences between groups), and idiographic (for finding differences between individuals) (e.g., Lerner et al., 2019; Overton, 2015; Salvatore & Valsiner, 2010). This was to magnify the descriptive power of the data and findings. In order to accomplish this, the two exploratory substudies in this dissertation employed 1) descriptive analysis, 2) a novel approach for comparing the service patterns matched to each student’s unique sets of strengths and needs, and 3) Latent Class Analysis (LCA). The major findings suggest that “tailoring” in City Connects schools is occurring in five ways: 1) students with higher needs receive more support than students with fewer needs; 2) City Connects is adaptive in responding to the emerging needs of individuals as circumstances change in the course of time; 3) there are unique patterns of services that are either shared (two more students have the same combination of services/types of services) or unshared (only one student has a particular service pattern); 4) service patterns are related to students’ developmental needs (i.e., higher risk level->higher percentages of individualized service patterns); and 5) service pattern matching is purposeful and does not occur randomly. The implications that these findings have on theory, research, and practice are discussed. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.

Thesis advisor: Laura M. O'Dwyer / Education researchers are frequently interested in examining the causal impact of academic services and interventions; however, it is often not feasible to randomly assign study elements to treatment conditions in the field of education (Adelson, 2013). When assignment to treatment conditions is non-random, the omission of any variables relevant to treatment selection creates a correlation between the treatment variable and the error in regression models. This is termed endogeneity (Ebbes, 2004). In the presence of endogeneity, treatment effect estimates from traditionally used regression approaches may be biased. The purpose of this study was to investigate the causal impact of an integrated student support model, namely City Connects, on student academic achievement. Given that students are not randomly assigned to the City Connects intervention, endogeneity bias may be present. To address this issue, two novel and underused statistical approaches were used with school admissions lottery data, namely Gaussian copula regression developed by Park and Gupta (2012), and Latent Instrumental Variable (LIV) regression developed by Peter Ebbes (2004). The use of real-world school admissions lottery data allowed the first-ever comparison of the two proposed methods with Instrumental Variable (IV) regression under a large-scale randomized control (RCT) trial. Additionally, the researcher used simulation data to investigate both the performance and boundaries of the two proposed methods compared with that of OLS and IV regression. Simulation study findings suggest that both Gaussian copula and LIV regression are useful approaches for addressing endogeneity bias across a range of research conditions. Furthermore, simulation findings suggest that the two proposed methods have important differences in their set of identifying assumptions, and that some assumptions are more crucial than others. Results from the application of the Gaussian copula and LIV regression in the City Connects school lottery admissions study demonstrated that receiving the City Connects model of integrated student support during elementary school has a positive impact on mathematics achievement. Such findings underscore the importance of addressing out-of-school barriers to learning. / Thesis (PhD) — Boston College, 2019. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.