IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-56318-8_22.html
   My bibliography  Save this book chapter

Mental Health Symptom Profiles Over Time: A Three-Step Latent Transition Cognitive Diagnosis Modeling Analysis with Covariates

In: Dependent Data in Social Sciences Research

Author

Listed:
  • Qianru Liang

    (Jinan University, Guangdong Institute of Smart Education)

  • Jimmy de la Torre

    (The University of Hong Kong, Faculty of Education)

  • Mary E. Larimer

    (University of Washington, Department of Psychiatry and Behavioral Sciences)

  • Eun-Young Mun

    (University of North Texas Health Science Center, College of Public Health)

Abstract

Cognitive diagnostic modeling (CDM) is an item-level analysis that accounts for attribute co-occurrences when characterizing attributes and classifying individuals’ attribute profiles. Tan et al. (Prev Sci 24:480–492, 2023) provided an application for mental health symptom profiles. The current study extends Tan et al. (Prev Sci 24:480–492, 2023) to demonstrate how intervention and gender affect transition probabilities from one state to another in a three-step latent transition CDM. The sample used in this study consisted of 2005 college students (34.5% men) who answered 40 items assessing four mental health symptoms (i.e., alcohol-related problems, anxiety, hostility, and depression) at baseline immediately before being randomly allocated to a brief alcohol intervention or control group (pretest) and at a 12-month follow-up following the intervention (posttest). Participants in the intervention group received personalized feedback on their alcohol use and alcohol-related problems, along with descriptive drinking norms of peers and other personalized and general information aimed at motivating students to change. Results indicated that the selected models showed adequate fit and classification outcomes. Latent logistic regression analysis showed that the intervention helped improve participants’ anxiety and depression. Those in the intervention group were more likely to transition from having anxiety and depression attribute profiles at pretest to not having them at posttest. In addition, male students were more likely to improve anxiety. Although the intervention was not associated with the transition probability from presence to absence for alcohol-related problems, it helped suppress the transition to having the attributes of alcohol-related problems (among men) and hostility (among women) at posttest. However, male students in the intervention were more likely to transition from absence to presence in their depression attribute profile state. The three-step latent transition CDM with covariates showcased in the current study may be an appealing analytical tool for examining and explaining change in mental health symptoms with informative covariates.

Suggested Citation

  • Qianru Liang & Jimmy de la Torre & Mary E. Larimer & Eun-Young Mun, 2024. "Mental Health Symptom Profiles Over Time: A Three-Step Latent Transition Cognitive Diagnosis Modeling Analysis with Covariates," Springer Books, in: Mark Stemmler & Wolfgang Wiedermann & Francis L. Huang (ed.), Dependent Data in Social Sciences Research, edition 2, chapter 0, pages 539-562, Springer.
  • Handle: RePEc:spr:sprchp:978-3-031-56318-8_22
    DOI: 10.1007/978-3-031-56318-8_22
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    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:sprchp:978-3-031-56318-8_22. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.