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A New Measurement of Internet Addiction Using Diagnostic Classification Models

Overview of attention for article published in Frontiers in Psychology, October 2017
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Title
A New Measurement of Internet Addiction Using Diagnostic Classification Models
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
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 %
Social Sciences 13 23%
Psychology 10 18%
Medicine and Dentistry 4 7%
Computer Science 3 5%
Nursing and Health Professions 3 5%
Other 9 16%
Unknown 15 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 October 2018.
All research outputs
#15,842,647
of 25,528,120 outputs
Outputs from Frontiers in Psychology
#15,994
of 34,606 outputs
Outputs of similar age
#183,534
of 334,105 outputs
Outputs of similar age from Frontiers in Psychology
#377
of 601 outputs
Altmetric has tracked 25,528,120 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 34,606 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.3. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 334,105 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 601 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.