Self-Evaluative Salience and Motivational Salience as Predictors of Depressive Affect and Appearance Based Rejection Sensitivity.

Although the psychological effects of appearance schemas have been studied in the general population, we know little about the relation of these schemas to appearance-based rejection sensitivity. This study examined the relations among predictive variables of appearance-invested self-schemas (self-evaluative salience [SES] and motivational salience [MS]), appearance-based rejection sensitivity, and depressive affect. Self-discrepancy theory was used to theorize that when individuals experience discrepancies with self, conflict arises in self-schemas, and that this conflict relates to an increase in depressive affect and appearance-based rejection sensitivity. The sample consisted of 131 adult female college students participating in a continuing education program. Multiple regression was used to evaluate the relation between appearance-invested self-schema and depressive affect. A second multiple regression equation was conducted to evaluate the relation between appearance-invested self-schema and appearance-based rejection sensitivity. Participants with higher SES scores had significantly higher depressive affect scores and appearance-based rejection sensitivity scores. Participants with higher MS scores had significantly lower depressive affect and appearance-based rejection sensitivity scores. High SES significantly predicted more depression and sensitivity to rejection based on appearance, and high MS appeared to be a protective factor against depression and appearance based rejection. The results of the study promote positive social change by helping professionals improve treatments for individuals suffering from negative appearance-invested self-schemas, rejection sensitivity, and depression

Significant Surfaces Slides

Fereshteh Toosi\u27s slides on the project Significant Surfaces

Critical Code Studies and Poetic Computation Proposal

Fereshteh Toosi\u27s proposal for Critical Code Studies and Poetic Computation introduces audiences to critical code studies and the concept of poetic computation

Decision analysis techniques for adult learners: application to leadership

Most decision analysis techniques are not taught at higher education institutions. Leaders, project managers and procurement agents in industry have strong technical knowledge, and it is crucial for them to apply this knowledge at the right time to make critical decisions. There are uncertainties, problems, and risks involved in business processes. Decisions must be made by responsible parties to address these problems in order to sustain and grow the company business. This study investigates some of the most recognized decision analysis techniques applied by global leaders from 2006 to 2016. Several decision analysis tools are introduced such as heuristic decisions, multi-attribute rating, decision trees, Monte-Carlo simulations and influence diagrams. The theoretical development framework is presented. The approach for this research is Analyze, Design, Develop, Implement, and Evaluate (ADDIE), which included cognitive, behavioral, and constructive learning theories. Some of the top decision analysis skills needed for today’s leaders and managers from literature review over the past decade (2006 to 2016), were taught to organization leadership doctorate students. Research scheme, the method chosen for selecting the topic, group of contributors, and the method selected for collecting the data are offered. The learners were in their senior year of a leadership doctorate program and they did not need leadership training along with decision analysis technique training. Older learners had more interest in learning the fishbone and influence diagrams prior to the training. Students with intermediate math were more interested in learning about strategic planning techniques before training. The trainees with more computer skills were interested in learning the Zachman framework technique, which was surprising to the researcher since this tool does not require extensive computer skills. After the training, the researcher observed that learners with higher computer skills showed more interest in learning about group decision-making (consensus versus analytic hierarchy process). That students with intermediate math skills were more interested in top-down induction of decision trees, algorithm decision making (data mining and knowledge discovery), and strategic planning techniques. Spearman correlations with a moderate strength showed that older respondents tended to be more interested in the analytical hierarchy process, fishbone diagram, and risk analysis tool. After the training, students with stronger computer skills showed greater curiosity about learning more about the decision tree analysis, Zachman framework, and risk analysis. It made sense that students with weaker computer skills were less eager to learn about the Monte-Carlo simulation