Black Marriage, Attachment and Connecting In Relationships: An Observational Multi-Method Study Investigating the Effects of the Getting The Love You Want Workshop On Black Couples’ In-Session Attachment, Interactions, Marital Satisfaction and Communication

The focus of this dissertation is examining the impact of Imago Relationship Therapy (IRT), more specifically the “Getting the Love You Want” workshop (GTLYW), on five Black/African American couples’ attachment, interactions, marital satisfaction and communication. This study examines Black couples’ lived experiences through a modified version of a quantitative measure, called the Patient Attachment Client System (PACS), the Marital Satisfaction Questionnaire-Revised (MSQ-R), semi-structured questionnaires to measure qualitative data as well as direct observation of participants’ interactions. PACS analyzes how patients’ in-session discourse enables them to share present experiences and link these processes with their attachment structure. In this view, attachment was assessed by delineating distinctive acts regarding how participants communicated their internal moment- to-moment experiences. Qualitative findings reveal a majority of study participants reported an improvement in their overall ability to communicate with their partner, an increase in their ability to communicate about race and racial distress, and an increase in their understanding and ability to deal with conflict and frustrations as a result of participating in the GTLYW workshop. Qualitative findings also showed that the structure of the Imago couples dialogue itself provided the conditions necessary for secure attachment, as measured utilizing PACS discursive markers, to occur between partners who are attachment figures to each other. Quantitative results in this study revealed that frequency of participants’ secure attachment discursive markers (PACS) increased over the length of the GTLYW workshop, and that the secure attachment discursive markers were highest in the racial distress dialogue. While participation in the GTLYW increased Black couples’ perception of their own marital satisfaction, it provided less strong support for an increase in their perception of their partner’s marital satisfaction. The underestimation of the partner’s view of marital satisfaction might be due to the differences in a subjective post-analysis self/other measure and an in-the-moment dialogic combined view measure. Educational workshops such as GTLYW may be a valuable prevention tool for couples and an efficient adjunct to therapy for clinicians who are trying to help couples struggling to remain in their marital relationship. Implications of the aforementioned findings, study limitations, recommendations and future directions are also discussed

Use of automated coding methods to assess motivational behaviour in education

Teachers’ motivational behaviour is related to important student outcomes. Assessing teachers’ motivational behaviour has been helpful to improve teaching quality and enhance student outcomes. However, researchers in educational psychology have relied on self-report or observer ratings. These methods face limitations on accurately and reliably assessing teachers’ motivational behaviour; thus restricting the pace and scale of conducting research. One potential method to overcome these restrictions is automated coding methods. These methods are capable of analysing behaviour at a large scale with less time and at low costs. In this thesis, I conducted three studies to examine the applications of an automated coding method to assess teacher motivational behaviours. First, I systematically reviewed the applications of automated coding methods used to analyse helping professionals’ interpersonal interactions using their verbal behaviour. The findings showed that automated coding methods were used in psychotherapy to predict the codes of a well-developed behavioural coding measure, in medical settings to predict conversation patterns or topics, and in education to predict simple concepts, such as the number of open/closed questions or class activity type (e.g., group work or teacher lecturing). In certain circumstances, these models achieved near human level performance. However, few studies adhered to best-practice machine learning guidelines. Second, I developed a dictionary of teachers’ motivational phrases and used it to automatically assess teachers’ motivating and de-motivating behaviours. Results showed that the dictionary ratings of teacher need support achieved a strong correlation with observer ratings of need support (rfull dictionary = .73). Third, I developed a classification of teachers’ motivational behaviour that would enable more advanced automated coding of teacher behaviours at each utterance level. In this study, I created a classification that includes 57 teacher motivating and de-motivating behaviours that are consistent with self-determination theory. Automatically assessing teachers’ motivational behaviour with automatic coding methods can provide accurate, fast pace, and large scale analysis of teacher motivational behaviour. This could allow for immediate feedback and also development of theoretical frameworks. The findings in this thesis can lead to the improvement of student motivation and other consequent student outcomes