title: An Exploratory Factor Analysis of the Scale Structure of the Patient Care Monitor Version 2.0.
abstract: BACKGROUND
  The Patient Care Monitor (PCM), version 2.0, is an electronic patient-reported outcomes instrument designed to be embedded into oncology practices. One key psychometric component of an instrument is its factor structure.
  
  
  OBJECTIVE
  To validate the factor structure of the PCM.
  
  
  METHODS
  The PCM was administered within various oncology clinics at our institution from 2006 to 2011 as part of standard of care, yielding a large (n = 5624) and diverse data set. An exploratory factor analysis was performed.
  
  
  RESULTS
  The PCM performed well in terms of missing values and floor and ceiling effects. The three scales postulated by the PCM developers exhibited high internal consistency (Cronbach alpha 0.94-0.95); the six subscales exhibited good internal consistency (Cronbach alpha 0.80-0.95). A three-factor model approximated simple structure and was consistent with the constructs of emotional function, physical function, and physical symptoms suggested by the PCM developers. However, a six-factor model did not support the division of these three constructs into subscales of despair, distress, ambulation, impaired performance, treatment side effects, and general physical symptoms. Instead, we observed an emotional factor, a physical functioning factor, a factor including many of the treatment side effects, and three factors consisting of various clusters of physical symptoms.
  
  
  CONCLUSIONS
  Although six subscales postulated by its developers perform reasonably, allocation of the PCM items to three constructs is more accurate and likely more consistent with how symptoms and concerns are conceptualized by patients.
authors.affiliation: Center for Learning Health Care, Duke Clinical Research Institute, Durham, North Carolina, USA; Departments of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA. Electronic address: samsa001@mc.duke.edu.
authors.forename: Gregory P
authors.initials: GP
authors.lastname: Samsa
authors.affiliation: Center for Learning Health Care, Duke Clinical Research Institute, Durham, North Carolina, USA; Departments of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.
authors.forename: Steven
authors.initials: S
authors.lastname: Wolf
authors.affiliation: Center for Learning Health Care, Duke Clinical Research Institute, Durham, North Carolina, USA; Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
authors.forename: Thomas W
authors.initials: TW
authors.lastname: LeBlanc
authors.affiliation: Center for Learning Health Care, Duke Clinical Research Institute, Durham, North Carolina, USA; Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
authors.forename: Amy P
authors.initials: AP
authors.lastname: Abernethy
mesh_terms: D000368:Aged; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009369:Neoplasms / Q000175:diagnosis*; D011594:Psychometrics; D057566:Self Report; D016896:Treatment Outcome
keywords: Exploratory factor analysis; instrument development; instrument validation; oncology; psychometrics