The therapeutic alliance, a construct representing agreement and collaboration on therapy goals, therapy tasks, and the emotional bond between client(s) and therapist, is a robust predictor of therapy outcomes in individual, couple, and family therapy. One way to track the therapeutic alliance is through ruptures and repairs. Ruptures are breaks, tensions, or tears in the therapeutic alliance. Ruptures and repairs influence the therapeutic alliance and consequently therapeutic outcomes. Currently, there is a lack of research addressing ruptures and repairs in couple therapy. The first step in researching alliance ruptures is to have a reliable way to assess alliance ruptures. This study will describe the development of the Coding Rupture Indicators in Couples Therapy (CRICT). The CRICT is an observational coding scheme that measures ruptures in couple therapy. The CRICT was developed through collaboration with researchers in marriage and family therapy, creation of items, adaptation of items from established coding schemes from individual therapy, and input and feedback as the CRICT was used and tested by undergraduates in a coding class. This paper will review foundational research of ruptures and repairs as well as the construction and use of the CRICT coding scheme.

Symptomatic spinal disorders affect a large proportion of the population and are associated with substantial morbidity, social burden, and economic impact. Spine surgery interventions can provide excellent results in carefully selected patients whose symptoms fail to improve with non-operative management, but an evidence-based approach is paramount to optimize outcomes and rigorous standards of health research methodology are critical to avoid misleading conclusions. This thesis aimed to investigate and apply modern innovations in health research methodology to the field of spine surgery. It consists of seven chapters divided between three sections: randomized controlled trials, observational studies, and systematic reviews and meta-analyses. By applying the findings of each chapter, clinicians, researchers, and other evidence users can advance the credibility of future research and enhance the care of patients with spinal disorders. / Thesis / Doctor of Philosophy (PhD)

The present study provides an example of implementing the difference in differences (DD) estimator for a two-group, pretest-posttest design with K-12 educational intervention data. The goal is to explore the basis for causal inference via Rubin's potential outcomes framework. The DD method is introduced to educational researchers, as it is seldom implemented in educational research. DD analytic methods' mathematical formulae and assumptions are explored to understand the opportunity and the challenges of using the DD estimator for causal inference in educational research. For this example, the teacher intervention effect is estimated with multi-cohort student outcome data. First, the DD method is used to detect the average treatment effect (ATE) with linear regression as a baseline model. Second, the analysis is repeated using linear regression with cluster robust standard errors. Finally, a linear mixed effects analysis is provided with a random intercept model. Resulting standard errors, parameter estimates, and inferential statistics are compared among these three analyses to explore the best holistic analytic method for this context.

Bipartite experiments have recently emerged as a focal point in causal inference. In these experiments, treatment is administered to one set of units, while outcomes of interest are gauged on a distinct set of units. Such experiments are especially valuable in scenarios where pronounced interference effects transpire between units on a bipartite network. For instance, in market experiments, designating treatment at the seller level and assessing outcomes at the buyer level (or vice-versa) can lead to causal models that more accurately reflect the inherent interference between buyers and sellers. Although bipartite experiments can enhance the precision of causal effect estimations in specific contexts, it's imperative to conduct the analysis judiciously to avoid introducing undue bias through the network. Drawing from the generalized propensity score literature, we demonstrate that it's feasible to achieve unbiased estimates of causal effects for bipartite experiments, given a conventional set of assumptions. Furthermore, we delve into the formulation of confidence sets with accurate coverage probabilities. By employing a bipartite graph from a publicly accessible dataset previously explored in bipartite experiment studies, we illustrate, via simulations, a notable reduction in bias and augmented coverage. / Statistics

Cycling is undergoing a welcome renaissance, especially in urban contexts. However, there are tensions, in relation to pedestrians, which could negatively affect cycling’s growth. This thesis observes the social interactions and physical reactions of cyclists when co-present with pedestrians at a zebra crossing in central Stockholm. It uses both in-person and video observations with the films being processed using an observiaire, a relatively novel, quantitative method of capturing human behaviours. The results were explored using ideas on the practice of cycling and the street as a social space. It seems that cyclist’s momentum and desire to save energy is clearly important as cyclists will do almost anything except stop for a pedestrian. However, they also seem to have an ability and wish to cooperate. This could be using waves, nods, or shouts, but also invisible and physical communication, what I term peripheral communication. Overall, the cyclists observed were constantly balancing their desire for efficiency against their consideration of pedestrians.

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