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GamTest: Psychometric Evaluation and the Role of Emotions in an Online Self-Test for Gambling Behavior

Overview of attention for article published in Journal of Gambling Studies, March 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

blogs
1 blog
facebook
1 Facebook page

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
62 Mendeley
Title
GamTest: Psychometric Evaluation and the Role of Emotions in an Online Self-Test for Gambling Behavior
Published in
Journal of Gambling Studies, March 2017
DOI 10.1007/s10899-017-9676-4
Pubmed ID
Authors

Jakob Jonsson, Ingrid Munck, Rachel Volberg, Per Carlbring

Abstract

Recent increases in the number of online gambling sites have made gambling more available, which may contribute to an increase in gambling problems. At the same time, online gambling provides opportunities to introduce measures intended to prevent problem gambling. GamTest is an online test of gambling behavior that provides information that can be used to give players individualized feedback and recommendations for action. The aim of this study is to explore the dimensionality of GamTest and validate it against the Problem Gambling Severity Index (PGSI) and the gambler's own perceived problems. A recent psychometric approach, exploratory structural equation modeling (ESEM) is used. Well-defined constructs are identified in a two-step procedure fitting a traditional exploratory factor analysis model as well as a so-called bifactor model. Using data collected at four Nordic gambling sites in the autumn of 2009 (n = 10,402), the GamTest ESEM analyses indicate high correspondence with the players' own understanding of their problems and with the PGSI, a validated measure of problem gambling. We conclude that GamTest captures five dimensions of problematic gambling (i.e., overconsumption of money and time, and monetary, social and emotional negative consequences) with high reliability, and that the bifactor approach, composed of a general factor and specific residual factors, reproduces all these factors except one, the negative consequences emotional factor, which contributes to the dominant part of the general factor. The results underscore the importance of tailoring feedback and support to online gamblers with a particular focus on how to handle emotions in relation to their gambling behavior.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 15%
Student > Bachelor 8 13%
Student > Master 6 10%
Student > Doctoral Student 6 10%
Student > Ph. D. Student 5 8%
Other 11 18%
Unknown 17 27%
Readers by discipline Count As %
Psychology 16 26%
Medicine and Dentistry 8 13%
Social Sciences 6 10%
Nursing and Health Professions 4 6%
Computer Science 2 3%
Other 7 11%
Unknown 19 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 03 April 2017.
All research outputs
#6,476,524
of 25,382,440 outputs
Outputs from Journal of Gambling Studies
#290
of 990 outputs
Outputs of similar age
#97,447
of 324,513 outputs
Outputs of similar age from Journal of Gambling Studies
#5
of 17 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 990 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one has gotten more attention than average, scoring higher than 70% 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 324,513 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.