Measurement Invariance Of The Short Version Of The Problematic Mobile Phone Use Questionnaire (Pmpuq-Sv) Across Eight Languages

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH(2018)

引用 35|浏览21
暂无评分
摘要
The prevalence of mobile phone use across the world has increased greatly over the past two decades. Problematic Mobile Phone Use (PMPU) has been studied in relation to public health and comprises various behaviours, including dangerous, prohibited, and dependent use. These types of problematic mobile phone behaviours are typically assessed with the short version of the Problematic Mobile Phone Use Questionnaire (PMPUQ-SV). However, to date, no study has ever examined the degree to which the PMPU scale assesses the same construct across different languages. The aims of the present study were to (i) determine an optimal factor structure for the PMPUQ-SV among university populations using eight versions of the scale (i.e., French, German, Hungarian, English, Finnish, Italian, Polish, and Spanish); and (ii) simultaneously examine the measurement invariance (MI) of the PMPUQ-SV across all languages. The whole study sample comprised 3038 participants. Descriptive statistics, correlations, and Cronbach's alpha coefficients were extracted from the demographic and PMPUQ-SV items. Individual and multigroup confirmatory factor analyses alongside MI analyses were conducted. Results showed a similar pattern of PMPU across the translated scales. A three-factor model of the PMPUQ-SV fitted the data well and presented with good psychometric properties. Six languages were validated independently, and five were compared via measurement invariance for future cross-cultural comparisons. The present paper contributes to the assessment of problematic mobile phone use because it is the first study to provide a cross-cultural psychometric analysis of the PMPUQ-SV.
更多
查看译文
关键词
mobile phone use, smartphone use, Problematic Mobile Phone Use, Problematic Mobile Phone Use Questionnaire, psychometric testing, measurement invariance
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要