Title |
A New Measurement of Internet Addiction Using Diagnostic Classification Models
|
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Published in |
Frontiers in Psychology, October 2017
|
DOI | 10.3389/fpsyg.2017.01768 |
Pubmed ID | |
Authors |
Dongbo Tu, Xuliang Gao, Daxun Wang, Yan Cai |
Abstract |
To obtain accurate, valid, and rich information from the questionnaires for internet addiction, a diagnostic classification test for internet addiction (the DCT-IA) was developed using diagnostic classification models (DCMs), a cutting-edge psychometric theory, based on DSM-5. A calibration sample and a validation sample were recruited in this study to calibrate the item parameters of the DCT-IA and to examine the sensitivity and specificity. The DCT-IA had high reliability and validity based on both CTT and DCMs, and had a sensitivity of 0.935 and a specificity of 0.817 with AUC = 0.919. More important, different from traditional questionnaires, the DCT-IA can simultaneously provide general-level diagnostic information and the detailed symptom criteria-level information about the posterior probability of satisfying each symptom criterion in DMS-5 for each patient, which gives insight into tailoring individual-specific treatments for internet addiction. |
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Switzerland | 1 | 33% |
Unknown | 2 | 67% |
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Members of the public | 3 | 100% |
Mendeley readers
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Unknown | 57 | 100% |
Demographic breakdown
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Student > Master | 12 | 21% |
Student > Ph. D. Student | 8 | 14% |
Student > Doctoral Student | 4 | 7% |
Professor | 3 | 5% |
Researcher | 3 | 5% |
Other | 12 | 21% |
Unknown | 15 | 26% |
Readers by discipline | Count | As % |
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Psychology | 10 | 18% |
Medicine and Dentistry | 4 | 7% |
Computer Science | 3 | 5% |
Nursing and Health Professions | 3 | 5% |
Other | 9 | 16% |
Unknown | 15 | 26% |