Year of Publication

2016

Season of Publication

Spring

Paper Type

Master's Thesis

College

College of Arts and Sciences

Degree Name

Master of Arts in General Psychology (MAGP)

Department

Psychology

NACO controlled Corporate Body

University of North Florida. Department of Psychology

First Advisor

Dr. Tracy Alloway

Second Advisor

Dr. Jennifer Wolff

Third Advisor

Dr. Lori Lange

Department Chair

Dr. Lori Lang

College Dean

Dr. Barbara A. Hetrick

Abstract

Major depressive disorder is a mental disorder characterized by multiple symptoms such as psychomotor retardation, sleep disturbances, and cognitive deficits in decision making. The current study explores the relationships between cognitive variables and depressive symptomology and seeks to determine what predictive relationships exist between these constructs and if items from these constructs can accurately classify depressed persons. A normal sample of N = 116 participants were administered the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977) as well as the Adult Hope Scale (ADH; Snyder et al., 1991), the Index of Autonomous Functioning(IAF; Weinstein, Przybylski, & Ryan, 2012), the Life Orientation Test-Revised(LOT-R; Scheier, Carver, & Bridges, 1994), the Zimbardo Time Perspective Inventory(ZTPI; Zimbardo & Boyd, 1999), the Rumination Reflection Questionnaire(RRQ; Trapnell & Campbell, 1999), and the Automated Working Memory Assessment-II (AWMA; Alloway, 2012b). A stepwise linear regression analysis determined that the Pessimism and Optimism subscales of the LOT-R, the Present Fatalism subscale of the ZTPI, and the Hope Agency subscale of the AHS significantly predicted depression in participants. One item each from the Optimism and Pessimism subscales, two items from the Present Fatalism subscale, and one item from the Hope Agency subscale accurately classified between 67-82% of the depressed (n = 42) and non-depressed (n = 64) persons in the sample. The implications of these findings for therapy and cognitive approaches to understanding depression as well as the relationships between the predictor variables themselves are discussed.

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