A latent class method for the selection of prototypes using expert ratings

Details

• Personal Author:
  Miller, William E.
• Description:
  Latent class analysis can be applied to the outcomes of expert ratings to select objects or subjects that are regarded as prototypical of a category in an ordinal classification system. During a pilot study, Monte Carlo simulations demonstrated that the probability of correct selection is larger when using latent class analysis than when usingmethods that rely on agreement statistics. Further improvements in the latent class results can also be achieved by applying affine transformations to latent class estimates of sensitivity and specificity. An application is presented that involves the selection of prototypical radiographs. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Affine Transformation Author Keywords: Monte Carlo Simulation Methodology Ordinal Classification Qualitative-analysis Random Block Design Simulation-methods Statistical-analysis Surveillance
• ISSN:
  0277-6715
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DRDS - Division of Respiratory Disease Studies
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  80-92
• Volume:
  30
• Issue:
  1
• NIOSHTIC Number:
  nn:20040245
• Citation:
  Stat Med 2012 Jan; 30(1):80-92
• Contact Point Address:
  William E. Miller, Division of Respiratory Disease Studies, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), 1095 Willowdale Rd., Morgantown,WV 26505-2888, USA
• Email:
  wem0@cdc.gov
• Federal Fiscal Year:
  2012
• Peer Reviewed:
  True
• Source Full Name:
  Statistics in Medicine
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:cd4d8e6f0167d2ab8b81ed36992a5a725d569c837732f717beeef82887c1e5d747376eafd75e2e3a68329903cba2790c691035fc36bd73bb2b3330b8c4224a60
• Download URL:
  https://stacks.cdc.gov/view/cdc/193186/cdc_193186_DS1.pdf
• File Type:
  [PDF - 455.33 KB ]