IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v48y2014i6p3025-3036.html
   My bibliography  Save this article

The dimensional assessment of personality in drug addicts: a mixed-effects Rasch model approach

Author

Listed:
  • Annalina Sarra
  • Lara Fontanella
  • Fausto D’Egidio
  • Paolo Frattone

Abstract

Drug abuse results from a series of different factors, such as social and family issues. Subjects more vulnerable to develop an addiction are, for instance, people living in high-stress environments who may resort to addiction in order to cope with their circumstances, such as demanding jobs, family crisis or other situations or people living in low-income households where violence occurs, who may be triggered into addiction as a way to escape negative emotions or ignore any underlying problems or issues. Psychometric research in the field of drug dependence has focused on identifying certain personality characteristics. It is now generally agreed that personality may influence, precipitate or perpetuate substance abuse. The aim of this paper is to perform a dimensional assessment of personality in a sample of drug addicts. To better understand the complexity of addictive behaviours of substance-using individuals, the Cloninger’s temperament and character inventory test is employed while the item response data analysis is performed by mixed-effects Rasch models. These models combine the advantages both of Rasch measurement framework for latent variables and of models with hierarchical data. To evaluate the differences in dimensions of temperament and character inventory test in subjects with drug addiction, we fit and compare a sequence of mixed-effects Rasch models. Results from models fitting are compared and discussed for a data set of 84 participants. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Annalina Sarra & Lara Fontanella & Fausto D’Egidio & Paolo Frattone, 2014. "The dimensional assessment of personality in drug addicts: a mixed-effects Rasch model approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3025-3036, November.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:6:p:3025-3036
    DOI: 10.1007/s11135-013-9938-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-013-9938-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-013-9938-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Doran, Harold & Bates, Douglas & Bliese, Paul & Dowling, Maritza, 2007. "Estimating the Multilevel Rasch Model: With the lme4 Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i02).
    2. Paul Boeck, 2008. "Random Item IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 533-559, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yaniv Efrati & Shane W. Kraus & Gal Kaplan, 2021. "Common Features in Compulsive Sexual Behavior, Substance Use Disorders, Personality, Temperament, and Attachment—A Narrative Review," IJERPH, MDPI, vol. 19(1), pages 1-18, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:jss:jstsof:39:i12 is not listed on IDEAS
    2. Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
    3. Quinn N. Lathrop & Ying Cheng, 2017. "Item Cloning Variation and the Impact on the Parameters of Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 245-263, March.
    4. Antonio Caronni & Marina Ramella & Pietro Arcuri & Claudia Salatino & Lucia Pigini & Maurizio Saruggia & Chiara Folini & Stefano Scarano & Rosa Maria Converti, 2023. "The Rasch Analysis Shows Poor Construct Validity and Low Reliability of the Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 (QUEST 2.0) Questionnaire," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    5. Sun-Joo Cho & Allan S. Cohen, 2010. "A Multilevel Mixture IRT Model With an Application to DIF," Journal of Educational and Behavioral Statistics, , vol. 35(3), pages 336-370, June.
    6. Sun-Joo Cho & Sarah Brown-Schmidt & Woo-yeol Lee, 2018. "Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 751-771, September.
    7. Sijia Huang & Li Cai, 2024. "Cross-Classified Item Response Theory Modeling With an Application to Student Evaluation of Teaching," Journal of Educational and Behavioral Statistics, , vol. 49(3), pages 311-341, June.
    8. Mariagiulia Matteucci & Bernard Veldkamp, 2015. "The approach of power priors for ability estimation in IRT models," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 917-926, May.
    9. Hanneke Geerlings & Cees Glas & Wim Linden, 2011. "Modeling Rule-Based Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 337-359, April.
    10. Sun-Joo Cho & Paul Boeck & Susan Embretson & Sophia Rabe-Hesketh, 2014. "Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 84-104, January.
    11. Royce Anders & William Batchelder, 2015. "Cultural Consensus Theory for the Ordinal Data Case," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 151-181, March.
    12. Murray Aitkin & Duy Vu & Brian Francis, 2017. "Statistical modelling of a terrorist network," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 751-768, June.
    13. Li, Kai & Chen, Pei-Ying & Yan, Erjia, 2019. "Challenges of measuring software impact through citations: An examination of the lme4 R package," Journal of Informetrics, Elsevier, vol. 13(1), pages 449-461.
    14. Joshua B. Gilbert & James S. Kim & Luke W. Miratrix, 2023. "Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 889-913, December.
    15. repec:jss:jstsof:20:i01 is not listed on IDEAS
    16. Sarra, Annalina & Di Zio, Simone & Cappucci, Marianna, 2015. "A quantitative valuation of tourist experience in Lisbon," Annals of Tourism Research, Elsevier, vol. 53(C), pages 1-16.
    17. Timo Bechger & Gunter Maris, 2015. "A Statistical Test for Differential Item Pair Functioning," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 317-340, June.
    18. Minjeong Jeon & Sophia Rabe-Hesketh, 2012. "Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models With Factor Structures," Journal of Educational and Behavioral Statistics, , vol. 37(4), pages 518-542, August.
    19. de Leeuw, Jan & Mair, Patrick, 2007. "An Introduction to the Special Volume on "Psychometrics in R"," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i01).
    20. repec:jss:jstsof:36:c01 is not listed on IDEAS
    21. Sarrias, Mauricio, 2016. "Discrete Choice Models with Random Parameters in R: The Rchoice Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i10).
    22. Jean-Paul Fox & Joris Mulder & Sandip Sinharay, 2017. "Bayes Factor Covariance Testing in Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 979-1006, December.
    23. Alexander Robitzsch, 2020. "L p Loss Functions in Invariance Alignment and Haberman Linking with Few or Many Groups," Stats, MDPI, vol. 3(3), pages 1-38, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:48:y:2014:i:6:p:3025-3036. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.