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Kazem Khalagi,Mohammad Ali Mansournia,Afarin Rahimi-Movaghar,Keramat Nourijelyani,Masoumeh Amin-Esmaeili,Ahmad Hajebi,Vandad Sharifi,Reza Radgoodarzi,Mitra Hefazi,Abbas Motevalian 한국역학회 2016 Epidemiology and Health Vol.38 No.-
Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.
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Psychometric Assessment of the Motives for Online Gaming Questionnaire Among Iranian Gamers
Marziyeh Hamzehzadeh,Arshiya Sangchooli,Rabert Farnam,Hosein Rafiemanesh,Behrang Shadloo,Kamyar Ghani,Maral Mardaneh Jobehdar,Masoumeh Amin-Esmaeili,Jamileh Rahimi,Zsolt Demetrovics,Orsolya Király,Afa 대한신경정신의학회 2022 PSYCHIATRY INVESTIGATION Vol.19 No.5
Objective Gaming motivations are crucial aspects of healthy and problematic video gaming behavior. The aim of this study was to assess the psychometric properties of the widely used Motives for Online Gaming Questionnaire (MOGQ).Methods Test-retest reliability was evaluated in a sample of 66 university students. Data from 791 participants in an online survey of Iranian online gamers were examined to assess convergent validity and construct validity using confirmatory factor analysis. Internal consistency of MOGQ factors was assessed in both samples.Results The item content validity index (I-CVI) and the scale content validity index (S-CVI) were between 0.8 and 1.0 for clarity and relevancy. The test-retest reliability of the 27-item questionnaire was 0.85 and internal consistency was 0.94. After incorporating additional error paths, model fit improved to an acceptable level. The MOGQ factors had acceptable correlations with relevant motivational scales such as Gaming Motivation Scale and Player Experience of Need Satisfaction. Recreation motives had the highest average score in the sample and social ones had the lowest, and males scored higher than females across all motivation domains except escape.Conclusion The MOGQ is a suitable instrument for the assessment of online gaming motivations in the Iranian population.
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Psychometric Properties of the Persian Version of the Gaming Motivation Scale
Kamyar Ghani,Rabert Farnam,Hosein Rafiemanesh,Behrang Shadloo,Arshiya Sangchooli,Marziyeh Hamzehzadeh,Maral Mardaneh Jobehdar,Masoumeh Amin-Esmaeili,Yekta Rahimi,Zsolt Demetrovics,Orsolya Király,Afari 대한신경정신의학회 2023 PSYCHIATRY INVESTIGATION Vol.20 No.7
Objective Gaming motivations are a central aspect of playing video games, and their importance to understanding both healthy and problematic gaming behavior has been increasingly elucidated. In this study, we aimed to translate the 18-item Gaming Motivation Scale (GAMS-18) to Persian and validate it in a population of Persian speaking gamers, specifically for the assessment of online gaming.Methods After translation from English to Persian, content validity of the questionnaire was assessed by a panel of experts and test–retest reliability was calculated in a sample of 70 students. Data from an online survey of 791 Iranian online gamers were used for the assessment of construct validity using confirmatory factor analysis.Results The item content validity index and the scale content validity index for clarity and relevance ranged from 0.80 to 1.00. Internal consistency reliability of the GAMS-18 was 0.90 and the test–retest reliability was 0.89. The test–retest reliability of the GAMS-18 was 0.89, and the internal consistency was 0.90. The GAMS factors had acceptable correlation with other motivational scale such as Player Experience of Need Satisfaction. Incorporating the proposed additional error paths improved the model fit to an acceptable level.Conclusion The Persian version of the GAMS can assess digital gaming behavior based on the six self-determination theory motivation types, and measures different aspects of motivation that other instruments. It is also demonstrated to have good reliability and validity and could be used in research on the motivations of online gamers in Persian-speaking populations.
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