The theory of adult romantic attachment, originating from Bowlby’s attachment theory, suggests that romantic partners create attachment bonds similar to that of the child-parent. Attachment styles...Show moreThe theory of adult romantic attachment, originating from Bowlby’s attachment theory, suggests that romantic partners create attachment bonds similar to that of the child-parent. Attachment styles can be primarily distinguished depending on the levels of anxiety and avoidance a partner exhibits in a bond. This study investigated the roles of gender and age in differences in adult romantic attachment. Male and female participants with an age range of 18 to 70 years from 171 countries completed the Experiences in Close Relationships Scale Questionnaire, a two-dimensional self-report instrument. Firstly, the results showed that females scored higher than males on anxiety and avoidance scales. Further findings demonstrated that gender and age contribute significantly to the prediction of anxiety and avoidance scores. Lastly, gender differences in avoidance scores, but not anxiety scores, were significantly moderated by age, whereby the differences between males and females in avoidance decreased as participants’ age increased (from age 18 to 70). Taken together, this study’s results provide evidence for statistically significant gender differences within adult romantic attachment and suggest attachment variability across different ages.Show less
Due to recent advances within the field of machine learning and computing power becoming more readily available, the use of machine learning within the field of psychology has increased. However,...Show moreDue to recent advances within the field of machine learning and computing power becoming more readily available, the use of machine learning within the field of psychology has increased. However, potential remains for greater use of machine learning within the field of psychology. In this study the usability and performance of 3 machine learning models namely K-nearest neighbors, the Random forest, and the Support vector machine algorithms were assessed when predicting gender, marital status, and family size from Big 5 personality measures and the Holland Code Career Test. Repeated cross-validation was combined with grid search to ensure performance measure accuracy and to optimize model accuracy and F1-score. The performances of the 3 models were compared to the performance of logistic regression to assess whether these models could outperform a model regularly used within psychology. The 3 models consistently outperformed the logistic regression under almost all conditions and proved far superior for groups sizes over 500 even outperforming logistic regression by 10 percentage points under some conditions. However, caution was advised due to wide confidence intervals for small group sizes (n ≤ 200). Therefore, a study was proposed with the aim to enhance predictions for small group sizes, focusing on feedforward neural networks, known to be able to capture complex relationships even with limited data. Addressing these aspects could improve the usability of machine learning in psychology settings involving small group sizes.Show less
