@article{SchulzDreschHeibergeretal.2022,
  author    = {Anja Alexandra Schulz and Carolin Dresch and Andrea Heiberger and Markus Antonius Wirtz},
  title     = {Use of Item Response Models in Assessing the Health Literacy Facet Understanding Health. Information for Early Childhood Allergy Prevention and Prevention of COVID-19 Infections by Pregnant Women and Mothers of Infants},
  series   = {Diagnostica},
  volume    = {68},
  number    = {4},
  publisher = {Hogrefe Verlag},
  issn      = {0012-1924},
  doi       = {10.1026/0012-1924/a000298},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:frei129-opus4-9795},
  pages     = {172 -- 183},
  year      = {2022},
  abstract  = {Appropriate parental health literacy (HL) is essential to preventively maintain and promote child health. Understanding health information is assumed to be fundamental in HL models. We developed N = 67 items (multiple-choice format) based on information materials on early childhood allergy prevention (ECAP) and prevention of COVID-19 infections to assess the parental HL facet Understand. N = 343 pregnant women and mothers of infants completed the items in an online assessment. Using exploratory factor analysis for ordinal data (RML estimation) and item response models (1-pl and 2-pl model), we proved the psychometric homogeneity of the item pool. 57 items assess the latent dimension Understand according to the assumptions of the 1-pl model (weighted MNSQ < 1.2; separation reliability = .855). Person parameters of the latent trait Understand correlate specifically with subjective socioeconomic status (r = .27), school graduation (r = .46), allergy status (r = .11), and already infected with COVID-19 (r = .12). The calibrated item pool provides a psychometrically sound, constructvalid assessment of the HL facet Understand Health Information in the areas of ECAP and prevention of COVID-19 infections.},
  language  = {en}
}