ScholarWorksIndianapolis
  • Communities & Collections
  • Browse ScholarWorks
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Subject

Browsing by Subject "Invariance"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Invariance of the Bifactor Structure of Mild Traumatic Brain Injury (mTBI) Symptoms on the Rivermead Post-Concussion Symptoms Questionnaire across Time, Demographic Characteristics, and Clinical Groups: A TRACK-TBI Study
    (Sage, 2021) Agtarap, Stephanie; Kramer, Mark D.; Campbell-Sills, Laura; Yuh, Esther; Mukherjee, Pratik; Manley, Geoffrey T.; McCrea, Michael A.; Dikmen, Sureyya; Giacino, Joseph T.; Stein, Murray B.; Nelson, Lindsay D.; TRACK-TBI Investigators; Psychiatry, School of Medicine
    This study aimed to elucidate the structure of the Rivermead Postconcussion Symptoms Questionnaire (RPQ) and evaluate its longitudinal and group variance. Factor structures were developed and compared in 1,011 patients with mild traumatic brain injury (mTBI; i.e., Glasgow Coma Scale score 13-15) from the Transforming Research and Clinical Knowledge in TBI study, using RPQ data collected at 2 weeks, and 3, 6, and 12 months postinjury. A bifactor model specifying a general factor and emotional, cognitive, and visual symptom factors best represented the latent structure of the RPQ. The model evinced strict measurement invariance over time and across sex, age, race, psychiatric history, and mTBI severity groups, indicating that differences in symptom endorsement were completely accounted for by these latent dimensions. While highly unidimensional, the RPQ has multidimensional features observable through a bifactor model, which may help differentiate symptom expression patterns in the future.
About IU Indianapolis ScholarWorks
  • Accessibility
  • Privacy Notice
  • Copyright © 2025 The Trustees of Indiana University