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TitlePredictors of depressive symptoms in persons with deafness and hearing loss
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Page 1

Predictors of Depression 1





PREDICTORS OF DEPRESSIVE SYMPTOMS IN PERSONS WITH DEAFNESS


AND HEARING LOSS


A Thesis

Submitted to the Faculty

of

Drexel University

by

Jill Friedman

In partial fulfillment of the

Requirements for the degree

of

Doctor of Philosophy

Drexel University


April 2008

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Predictors of Depression 2

ABSTRACT
Predictors of Depressive Symptoms in Persons with Deafness and Hearing Loss

Jill Friedman, M.S.
Arthur M. Nezu, Ph.D., ABPP


To date, there are very few studies that that have assessed predictors of depression in

persons with deafness and hearing loss. The present study addressed this fundamental gap

in the literature by predicting depressive symptoms with the following two constructs:

loneliness and problem-solving coping. Just as loneliness and problem solving are

thought to be important in predicting depressive symptoms in the hearing, it was

hypothesized that these constructs would also be important predictors of depressive

symptoms in the deaf and hard-of-hearing. The literature, while inconsistent, suggests

that objective severity of one’s disability, speech discrimination, and the number of years

one is deaf or hard-of-hearing, is related to psychopathology. This study included these

factors as covariates. One hundred and twenty six women from the Drexel University

Department of Otolaryngology completed a series of self-report questionnaires. The

results indicated that loneliness was a significant, positive predictor of depressive

symptoms. Additionally, the results indicated that problem solving was a significant,

inverse predictor of depressive symptoms. A series of correlations designed to

deconstruct the relationship between problem-solving and depression revealed a

significant relationship between depressive symptoms and negative problem orientation,

impulsiveness/carelessness style, and avoidance style. A multiple linear regression, in

which components of problem solving were regressed on depressive symptoms, revealed

a significant relationship between negative problem orientation and depressive symptoms

and avoidance style and depressive symptoms. A hierarchical linear regression, which

was employed to test the hypothesis that loneliness and problem solving predict

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Predictors of Depression 82

subsequent to completing the questionnaires. Participants were given a telephone number

to use for contacting this author with any questions after meeting with the first author of

this study.

Moreover, it was indicated that if a participant were to become emotionally

distressed during their participation in the study, they would be invited to discontinue

participation and referral information would be made available for those who expressed

interest in receiving treatment for depression. It was indicated that the data for these

individuals would be excluded from the analyses.

Statistical Analysis

“Data analysis is the process of bringing order, structure, and interpretation to the

mass of data collected” (Marshall and Rossman, 1999, p. 150). In this study, the data

were analyzed by using SPSS 13.0 statistical package. Significance levels for all data

analyses were set at a 0.05 alpha level. In this study, the independent variables comprised

loneliness and problem-solving. The covariates were objective severity of deafness and

hearing loss, speech discrimination, and number of years deafened or hard-of-hearing.

Depression was the dependent variable. Descriptive statistics, such as age, age at onset of

loss in hearing, years hearing impaired or deafened, number of hearing impaired or deaf

people in the family, primary method of communication, gender, level of education,

marital status, degree of hearing loss, and medical conditions, were analyzed. Multiple

linear regression analyses were computed, as were mediational and a moderator analyses.

Power Analysis

An a-priori power analysis was conducted to determine the number of participants

needed to ensure an 80% chance of detecting a medium effect size. It was estimated that,

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Predictors of Depression 83

at an alpha level of 0.05, and with a medium effect size of 0.6, the power would be 80%

with 126 participants. Power at a medium effect size has been viewed as an acceptable

level in order to detect significant differences while avoiding drawing the conclusion that

the independent variable had no effect when there was one (Type II error) (Cohen, 1992).

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Predictors of Depression 164


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