Psychometric evaluation of Persian Nomophobia Questionnaire: Differential item functioning and measurement invariance across gender.

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Abstract

Background and aims Research examining problematic mobile phone use has increased markedly over the past 5 years and has been related to "no mobile phone phobia" (so-called nomophobia). The 20-item Nomophobia Questionnaire (NMP-Q) is the only instrument that assesses nomophobia with an underlying theoretical structure and robust psychometric testing. This study aimed to confirm the construct validity of the Persian NMP-Q using Rasch and confirmatory factor analysis (CFA) models. Methods After ensuring the linguistic validity, Rasch models were used to examine the unidimensionality of each Persian NMP-Q factor among 3,216 Iranian adolescents and CFAs were used to confirm its four-factor structure. Differential item functioning (DIF) and multigroup CFA were used to examine whether males and females interpreted the NMP-Q similarly, including item content and NMP-Q structure. Results Each factor was unidimensional according to the Rach findings, and the four-factor structure was supported by CFA. Two items did not quite fit the Rasch models (Item 14: "I would be nervous because I could not know if someone had tried to get a hold of me;" Item 9: "If I could not check my smartphone for a while, I would feel a desire to check it"). No DIF items were found across gender and measurement invariance was supported in multigroup CFA across gender. Conclusions Due to the satisfactory psychometric properties, it is concluded that the Persian NMP-Q can be used to assess nomophobia among adolescents. Moreover, NMP-Q users may compare its scores between genders in the knowledge that there are no score differences contributed by different understandings of NMP-Q items.

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The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study.

Cecilie Andreassen, Joël Billieux, Mark Griffiths … (2016)

Over the last decade, research into "addictive technological behaviors" has substantially increased. Research has also demonstrated strong associations between addictive use of technology and comorbid psychiatric disorders. In the present study, 23,533 adults (mean age 35.8 years, ranging from 16 to 88 years) participated in an online cross-sectional survey examining whether demographic variables, symptoms of attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), anxiety, and depression could explain variance in addictive use (i.e., compulsive and excessive use associated with negative outcomes) of two types of modern online technologies: social media and video games. Correlations between symptoms of addictive technology use and mental disorder symptoms were all positive and significant, including the weak interrelationship between the two addictive technological behaviors. Age appeared to be inversely related to the addictive use of these technologies. Being male was significantly associated with addictive use of video games, whereas being female was significantly associated with addictive use of social media. Being single was positively related to both addictive social networking and video gaming. Hierarchical regression analyses showed that demographic factors explained between 11 and 12% of the variance in addictive technology use. The mental health variables explained between 7 and 15% of the variance. The study significantly adds to our understanding of mental health symptoms and their role in addictive use of modern technology, and suggests that the concept of Internet use disorder (i.e., "Internet addiction") as a unified construct is not warranted.

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Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework.

Steven Gregorich (2006)

Comparative public health research makes wide use of self-report instruments. For example, research identifying and explaining health disparities across demographic strata may seek to understand the health effects of patient attitudes or private behaviors. Such personal attributes are difficult or impossible to observe directly and are often best measured by self-reports. Defensible use of self-reports in quantitative comparative research requires not only that the measured constructs have the same meaning across groups, but also that group comparisons of sample estimates (eg, means and variances) reflect true group differences and are not contaminated by group-specific attributes that are unrelated to the construct of interest. Evidence for these desirable properties of measurement instruments can be established within the confirmatory factor analysis (CFA) framework; a nested hierarchy of hypotheses is tested that addresses the cross-group invariance of the instrument's psychometric properties. By name, these hypotheses include configural, metric (or pattern), strong (or scalar), and strict factorial invariance. The CFA model and each of these hypotheses are described in nontechnical language. A worked example and technical appendices are included.