Analyzing Models of Work Addiction: Single Factor and Bi-Factor Models of the Bergen Work Addiction Scale

Orosz, Gábor; Dombi, Edina; Andreassen, Cecilie Schou; Griffiths, Mark D.; Demetrovics, Zsolt

doi:10.1007/s11469-015-9613-7

Cited by 52 publications

(54 citation statements)

References 16 publications

(21 reference statements)

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“…Scoring 4 (often) or 5 (always) on four out of seven criteria indicates workaholism. This polythetic cut-off for categorization has been validated in previous research [ 13 – 15 ]. The Cronbach’s alpha for the BWAS was .86 in the present study.…”

Section: Methodsmentioning

confidence: 89%

“…Because previous workaholism scales did not cover these addiction components, the seven-item Bergen Work Addiction Scale (BWAS) was specifically developed in order to assess this behavior using the same criteria as other addictions [ 13 ]. Consequently, the BWAS is based on and embedded within general addiction theory [ 10 ], and has demonstrated robust psychometric properties across studies in different countries [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

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The Relationships between Workaholism and Symptoms of Psychiatric Disorders: A Large-Scale Cross-Sectional Study

et al. 2016

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Despite the many number of studies examining workaholism, large-scale studies have been lacking. The present study utilized an open web-based cross-sectional survey assessing symptoms of psychiatric disorders and workaholism among 16,426 workers (Mage = 37.3 years, SD = 11.4, range = 16–75 years). Participants were administered the Adult ADHD Self-Report Scale, the Obsession-Compulsive Inventory-Revised, the Hospital Anxiety and Depression Scale, and the Bergen Work Addiction Scale, along with additional questions examining demographic and work-related variables. Correlations between workaholism and all psychiatric disorder symptoms were positive and significant. Workaholism comprised the dependent variable in a three-step linear multiple hierarchical regression analysis. Basic demographics (age, gender, relationship status, and education) explained 1.2% of the variance in workaholism, whereas work demographics (work status, position, sector, and annual income) explained an additional 5.4% of the variance. Age (inversely) and managerial positions (positively) were of most importance. The psychiatric symptoms (ADHD, OCD, anxiety, and depression) explained 17.0% of the variance. ADHD and anxiety contributed considerably. The prevalence rate of workaholism status was 7.8% of the present sample. In an adjusted logistic regression analysis, all psychiatric symptoms were positively associated with being a workaholic. The independent variables explained between 6.1% and 14.4% in total of the variance in workaholism cases. Although most effect sizes were relatively small, the study’s findings expand our understanding of possible psychiatric predictors of workaholism, and particularly shed new insight into the reality of adult ADHD in work life. The study’s implications, strengths, and shortcomings are also discussed.

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Section: Methodsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

The Relationships between Workaholism and Symptoms of Psychiatric Disorders: A Large-Scale Cross-Sectional Study

et al. 2016

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“…The responses are provided on a Likert scale ranging from never (1) to always (5). A one-factor solution has been found for the BWAS (Andreassen et al., 2012), and it has been translated into several languages and has demonstrated good reliability and validity across studies (Andreassen et al., 2012, 2013, 2014; Molino, 2012; Orosz et al., 2016). In Wave 2, the current Cronbach’s α values were .85 and .84 in the Norwegian and Polish samples, respectively.…”

Section: Methodsmentioning

confidence: 99%

The relationship between study addiction and work addiction: A cross-cultural longitudinal study

et al. 2016

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AimsRecent empirical studies investigating “study addiction” have conceptualized it as a behavioral addiction, defined within the framework of work addiction. This study is the first attempt to examine the longitudinal relationship between study addiction and work addiction.MethodsThe Bergen Study Addiction Scale (BStAS), the Bergen Work Addiction Scale (BWAS), and the Ten-Item Personality Inventory were administered online together with questions concerning demographics and study-related variables in two waves. In Wave 1, a total of 2,559 students in Norway and 2,177 students in Poland participated. A year later, in Wave 2, 379 Norwegians and 401 Polish who began to work professionally completed the survey.ResultsThe intraclass correlation between BStAS and BWAS revealed that the scores were somewhat related; however, the relationship was slightly weaker than the temporal stability of both constructs. In the Norwegian sample, scoring higher on neuroticism and lower on learning time outside educational classes in Wave 1 was positively related to work addiction in Wave 2, whereas gender was unrelated to work addiction in Wave 2 when controlling for other studied variables in either samples.ConclusionStudy addiction and work addiction appear to be closely related suggesting that the former may be a precursor for (or an early form of) the latter.

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“…Participants were selected randomly from 15,000 panel members with the help of a research market company in the summer of 2015. For the preparation of the sample, a multiple‐step, proportionally stratified, probabilistic sampling method was employed, in which individuals were removed from the panel if they gave responses too quickly (i.e., without paying attention to their response) and/or had fake (unused) e‐mail addresses (see Orosz, Dombi, Andreassen, Griffiths, & Demetrovics, for similar procedures). The final sample of 505 Hungarian respondents who gave valid answers was nationally representative in terms of gender (female = 265; 52.5%), age ( M age = 44.37 years; SD age = 15.59 years; range 15–75 years), education (22.8% had primary level of education, 24.8% had vocational school degree, 31.5% graduated from high school, and 21.0% had higher education degree), and place of residence (18.8% in capital city, 19.6% in county capitals, 31.7% in towns, and 29.9% in villages).…”

Section: Methodsmentioning

confidence: 99%

“…Participants were selected randomly from 15,000 panel members with the help of a research market company in the summer of 2015. For the preparation of the sample, a multiple-step, proportionally stratified, probabilistic sampling method was employed, in which individuals were removed from the panel if they gave responses too quickly (i.e., without paying attention to their response) and/or had fake (unused) e-mail addresses (see Orosz, Dombi, Andreassen, Griffiths, & Demetrovics, 2015…”

Section: Participantsmentioning

confidence: 99%

What predicts anti‐Roma prejudice? Qualitative and quantitative analysis of everyday sentiments about the Roma

Orosz

Bruneau²,

Tropp

et al. 2018

J Applied Social Pyschol

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The present research focused on two primary goals: (a) identifying the content of sentiments about the Roma to which Hungarians are exposed during everyday family conversations, and (b) determining which types of everyday sentiments about the Roma most strongly predict Hungarian respondents’ anti‐Roma prejudice. Content analyzing open‐ended responses from a representative sample of Hungarians (N = 505), we found that more than 76% of the respondents reported being exposed to negative stereotypes about the Roma, 27% to threats posed by Roma, and 16% to overt dehumanization of Roma; additionally more than 20% reported hearing no positive sentiments about the Roma in everyday family conversations. We then examined which negative and positive sentiments most strongly predicted respondents’ anti‐Roma prejudice (using measures of social distance and modern racism). Higher social distance scores were predicted by a lack of positive sentiments, whereas lower social distance scores were most strongly predicted by unambiguously positive sentiments expressed during family conversations. Higher modern racism scores were further predicted by sentiments expressing dehumanization, threat, and violence against Roma. Together, these results attest to the extremity of anti‐Roma sentiments expressed regularly by Hungarians, and suggest how exposure to specific sentiments may foster anti‐Roma hostility. Moreover, these findings provide guidance regarding the specific negative anti‐Roma sentiments that should be combated to enhance the effectiveness of anti‐prejudice interventions.

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Analyzing Models of Work Addiction: Single Factor and Bi-Factor Models of the Bergen Work Addiction Scale

Cited by 52 publications

References 16 publications

The Relationships between Workaholism and Symptoms of Psychiatric Disorders: A Large-Scale Cross-Sectional Study

The Relationships between Workaholism and Symptoms of Psychiatric Disorders: A Large-Scale Cross-Sectional Study

The relationship between study addiction and work addiction: A cross-cultural longitudinal study

What predicts anti‐Roma prejudice? Qualitative and quantitative analysis of everyday sentiments about the Roma

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