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Title: Investigating a Structural Model of Addiction Stigma related to Student Perceptions towards Persons Addicted to Heroin
Author(s): Marecki, John
Supervisor(s): Yates, Rowdy
Rutherford, Alasdair
Keywords: Addction Stigma
Exploratory Structural Equation Modeling (ESEM)
Persons Addicted to Heroin
Issue Date: 15-Sep-2015
Publisher: University of Stirling
Abstract: Heroin addiction is inclined to arouse fear, rejection and discriminatory behavior among the general public. Evidence shows that the public perceives heroin as harmful and addictive. Heroin is ranked as the most stigmatized condition. While there is robust literature on mental illness stigma, there is limited research concerning addiction-related stigma. There are very few standardized stigma measures related to perceptions toward persons addicted to heroin. The overall aim of the dissertation was to validate an attribution measurement model toward persons addicted to heroin and to determine its psychometric properties. The dissertation’s study employed an adapted 7-factor measurement model (Corrigan et al., 2002) to examine stigmatizing perceptions towards persons addicted to heroin. This is the first study to systematically evaluate model fit by implementing Exploratory Structural Equation Modeling (ESEM). A total of 657 Sociology students were analyzed over four stages: questionnaire review by expert panel, pilot-test, validation and replication. The study tested multiple incremental models and successfully determined that the results met multiple goodness-of-fit indices. Through ESEM, Sociology-Social Control students supported the hypothesis that the adapted 7-factor attribution measurement model would fit data. The model included: Personal Responsibility, Pity, Anger, Helping Behavior, Dangerousness, Fear and Social Distance factors. Adequate power and sample size was demonstrated to support acceptance of the null hypothesis. In addition to conducting Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), ESEM tested the psychometric properties of the attribution measurement model. Implementing maximum likelihood extraction with oblique geomin rotation using Mplus software, the Sociology-Social Control students’ validation and replication datasets showed an excellent model fit to the data. Results confirmed support for the superiority of the ESEM solution. The ESEM attribution measurement model fit better than the CFA model. Compared to the ESEM model, elevated factor correlations found in the CFA model were caused by the exclusion of meaningful cross-loadings. Strong psychometric properties for the ESEM attribution model were evidenced, with good internal consistency and excellent test-retest reliability. The factor structure was replicable across the two groups of Sociology-Social Control students. Adequate ESEM incremental and convergent validity was supported by the simultaneous examination of the Social Distance scale and the Personal Consequences of Criminal Stigma measures with the measurement model. In the replication sample, familiarity demonstrated less stigmatizing perceptions than the SOC313 Course. Our findings highlight marked differences between the Sociology-Social Control students and the general population’s perceptions of heroin addicts. The Sociology-Social Control students are not afraid of persons addicted to heroin, nor do they hold them responsible for their condition. To conclude, the study provides newly validated measures with adequate reliability to allow investigators to assess other students’ level of addiction stigma. It is anticipated that the dissertation’s study will lead to further comparative psychometric testing with healthcare students that are directly involved with the care and treatment of persons addicted to heroin to provide a better understanding of the factorial structure of the attribution measurement model. Longitudinal data is also needed to examine our model and how levels of perceptions change over time.
Type: Thesis or Dissertation

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Chapters 1,2,3,4,5June 2015 FinalExam.WithLabelRef (2).pdfJohn Marecki PhD Dissertation with Appendices and References4.65 MBAdobe PDFView/Open

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