Internet Addiction and the Psychometric Properties of the Nine-item Internet Disorder Scale–Short Form: An Application of Rasch Analysis

Document Type : Original Article


1 Department of Biostatistics and Medical Informatics, Cerrahpasa Faculty of Medicine, Istanbul University, Istanbul, Turkey AND Department of Evidence for Population Health Unit, School of Epidemiology and Health Sciences, University of Manchester, Manchester, UK

2 Department of Psychology, School of Social Sciences, Nottingham Trent University, Nottingham, UK

3 Department of Public Health, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey

4 Department of Biostatistics and Medical Informatics, Cerrahpasa Faculty of Medicine, Istanbul University, Istanbul, Turkey University of Kastamonu, Kastamonu, Turkey

5 Department of Public Health, Capa Faculty of Medicine, Istanbul University, Istanbul, Turkey


Background: The aim of the present study was to determine the prevalence of disordered internet use among
adolescent university students and its association with various health complaints and behaviours, and most
importantly to examine the psychometric properties of 9-item Internet Disorder Scale-Short Form (IDS9-SF)
using factor analyses and Rasch analysis.
Methods: A total of 1988 university students aged 18 to 25 years were selected via a multi-stage stratified
random sampling technique among university students in Istanbul, Turkey (September 2017 to February
2018). Data collected included socio-demographics, lifestyle and dietary habits, and the 9-item IDS9-SF.
Statistical analysis included descriptive statistics, multivariate analyses, factor analyses, path analysis, and
Rasch analysis.
Findings: Using confirmatory factor analysis (CFA), the study investigated the latent structure of the IDS9-SF
instrument and results supported its reliability and validity. The prevalence of disordered internet use was
18.3% in the sample. There were significant differences between those who had disordered internet use and
those who did not in gender, family income, school performance, number of bedrooms at home, and number
of people living at home, as well as internet use duration. Using multivariate regression analysis, key
predictors of disordered internet use included (among others): gender, body mass index (BMI), household
income, number of people living at home, having a computer at home, internet facilities, duration of internet
use, sleeping hours, frequency of eating fast food, watching television, headache, hurting eyes, tired eyes, and
hearing problems. Rash analysis demonstrated that four of the nine items (2, 3, 6, and 7) were more difficult
for individuals to endorse compared to other items.
Conclusion: Problems arising from excessive internet use were apparent among the study sample and the
IDS9-SF is a valid and reliable measure for assessing disordered internet use among Turkish adolescent


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