Assessing gambling disorder using frequency- and time-based response options: A Rasch analysis of the gambling disorder identification test

被引:2
|
作者
Molander, Olof [1 ,7 ]
Wennberg, Peter [2 ,3 ,4 ]
Dowling, Nicki A. [5 ]
Berman, Anne H. [1 ,6 ]
机构
[1] Karolinska Inst, Ctr Psychiat Res, Dept Clin Neurosci, Solna, Sweden
[2] Stockholm Univ, Dept Publ Hlth Sci, Stockholm, Sweden
[3] Karolinska Inst, Dept Global Publ Hlth, Solna, Sweden
[4] Inland Norway Univ Appl Sci, Dept Psychol, Lillehammer, Norway
[5] Deakin Univ, Sch Psychol, Geelong, Vic, Australia
[6] Uppsala Univ, Dept Psychol, Uppsala, Sweden
[7] Karolinska Inst, Ctr Psykiatriforskning, Norra Stationsgatan 69,plan 7, S-11364 Stockholm, Sweden
关键词
DSM-5; gambling disorder; item difficulty; Rasch analysis; the gambling disorder identification test; RELIABILITY; CRITERIA; VALIDITY;
D O I
10.1002/mpr.2018
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
ObjectivesThe Gambling Disorder Identification Test (GDIT) is a recently developed self-report measure. The GDIT includes items with multiple response options that are either based on frequency or time, and item response theory evaluations of these could yield vital knowledge on its measurement performance.MethodsThe GDIT was evaluated using Rasch analysis in a study involving 597 Swedish gamblers.ResultsIn a three-dimensional Rasch model, the item response difficulty range extended from -1.88 to 4.06 and increased with higher time- and frequency-based responses. Differential item functioning showed that some GDIT items displayed age and gender-related differences. Additionally, person-separation reliability indicated the GDIT could reliably be divided into three to four diagnostic levels.ConclusionsThe frequency- and time-based item response options of the GDIT offer excellent measurement, allowing for elaborate assessment across both lower and higher gambling severity. The GDIT can be used to detect DSM-5 Gambling Disorder, thereby holding significance from both epidemiological and clinical standpoints. Notably, the 3-item GDIT Gambling Behavior subscale also shows potential as a brief screening tool for identifying at-risk gambling behavior.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] Gaming disorder test: Assessing psychometric properties, prevalence, temporal stability, and invariance using a Czech two-time-point longitudinal sample
    Sirinkova, Dita
    Blinka, Lukas
    Montag, Christian
    JOURNAL OF PSYCHIATRIC RESEARCH, 2024, 175 : 192 - 199
  • [32] Diagnosis of autism spectrum disorder from EEG using a time-frequency spectrogram image-based approach
    Tawhid, M. N. A.
    Siuly, S.
    Wang, H.
    ELECTRONICS LETTERS, 2020, 56 (25) : 1372 - 1375
  • [33] Analysis of ultrasound kidney images using content descriptive multiple features for disorder identification and ANN based classification
    Raja, K. Bommanna
    Madheswaran, M.
    Thyagarajah, K.
    ICCTA 2007: INTERNATIONAL CONFERENCE ON COMPUTING: THEORY AND APPLICATIONS, PROCEEDINGS, 2007, : 382 - +
  • [34] Identification and validation of functional gastrointestinal disorder subtypes using latent class analysis: a population-based study
    Zinsmeister, Alan R.
    Herrick, Linda M.
    Loftus, Yuri A. Saito
    Schleck, Cathy D.
    Talley, Nicholas J.
    SCANDINAVIAN JOURNAL OF GASTROENTEROLOGY, 2018, 53 (05) : 549 - 558
  • [35] Assessing the influence of a rapid water drawdown on the seismic response characteristics of a reservoir rock slope using time–frequency analysis
    Danqing Song
    Xiaoli Liu
    Bin Li
    Jianmin Zhang
    Juan Jose Volcan Bastos
    Acta Geotechnica, 2021, 16 : 1281 - 1302
  • [36] Frequency response function based damage identification using principal component analysis and pattern recognition technique
    Bandara, Rupika P.
    Chan, Tommy H. T.
    Thambiratnam, David P.
    ENGINEERING STRUCTURES, 2014, 66 : 116 - 128
  • [37] Learning Based Noise Identification Techniques Using Time-Frequency Analysis and the U-Net
    Wang, Chih-Hao
    Ding, Jian-Jiun
    Chang, Chieh-Sheng
    Ouyang, Liang-Yu
    2019 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS), 2019,
  • [38] Diagnosis of Narcolepsy Sleep Disorder for Different Stages of Sleep Using Short Time Frequency Analysis of PSD Approach Applied on EEG Signal
    Siddiqui, Mohd Maroof
    Srivastava, Geetika
    Saeed, Syed Hasan
    2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL TECHNIQUES IN INFORMATION AND COMMUNICATION TECHNOLOGIES (ICCTICT), 2016,
  • [39] Assessing the influence of a rapid water drawdown on the seismic response characteristics of a reservoir rock slope using time-frequency analysis
    Song, Danqing
    Liu, Xiaoli
    Li, Bin
    Zhang, Jianmin
    Volcan Bastos, Juan Jose
    ACTA GEOTECHNICA, 2021, 16 (04) : 1281 - 1302
  • [40] Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC
    Siddiqui, Mohd Maroof
    Srivastava, Geetika
    Saeed, Syed Hasan
    SLEEP SCIENCE, 2016, 9 (03) : 186 - 191