Quality of Life Research

, Volume 18, Issue 4, pp 447–460 | Cite as

Having a fit: impact of number of items and distribution of data on traditional criteria for assessing IRT’s unidimensionality assumption

  • Karon F. CookEmail author
  • Michael A. Kallen
  • Dagmar Amtmann



Confirmatory factor analysis fit criteria typically are used to evaluate the unidimensionality of item banks. This study explored the degree to which the values of these statistics are affected by two characteristics of item banks developed to measure health outcomes: large numbers of items and nonnormal data.


Analyses were conducted on simulated and observed data. Observed data were responses to the Patient-Reported Outcome Measurement Information System (PROMIS) Pain Impact Item Bank. Simulated data fit the graded response model and conformed to a normal distribution or mirrored the distribution of the observed data. Confirmatory factor analyses (CFA), parallel analysis, and bifactor analysis were conducted.


CFA fit values were found to be sensitive to data distribution and number of items. In some instances impact of distribution and item number was quite large.


We concluded that using traditional cutoffs and standards for CFA fit statistics is not recommended for establishing unidimensionality of item banks. An investigative approach is favored over reliance on published criteria. We found bifactor analysis to be appealing in this regard because it allows evaluation of the relative impact of secondary dimensions. In addition to these methodological conclusions, we judged the items of the PROMIS Pain Impact bank to be sufficiently unidimensional for item response theory (IRT) modeling.


Psychometrics Item response theory Outcome measures Pain measurement Factor analysis Methodology 



Computer adaptive testing


Confirmatory factor analyses


Comparative Fit Index


Expected a priori


Exploratory factor analyses


General Educational Development


Graded response model


Item response theory


National Institutes of Health


Nonnormed Fit Index


Patient-Reported Outcomes Measurement Information System


Patient-reported outcomes


Root-mean-square error of approximation


Standard deviation


Standardized root-mean-square error


Tucker–Lewis index


Weighted least squares with mean and variance adjustment


Weighted root-mean-square residual



The Patient-Reported Outcomes Measurement Information System (PROMIS) is a National Institutes of Health (NIH) roadmap initiative to develop a computerized system measuring patient-reported outcomes in respondents with a wide range of chronic diseases and demographic characteristics. PROMIS was funded by cooperative agreements to a Statistical Coordinating Center (Evanston Northwestern Healthcare, PI: David Cella, PhD, U01AR52177) and six Primary Research Sites (Duke University, PI: Kevin Weinfurt, PhD, U01AR52186; University of North Carolina, PI: Darren DeWalt, MD, MPH, U01AR52181; University of Pittsburgh, PI: Paul A. Pilkonis, PhD, U01AR52155; Stanford University, PI: James Fries, MD, U01AR52158; Stony Brook University, PI: Arthur Stone, PhD, U01AR52170; and University of Washington, PI: Dagmar Amtmann, PhD, U01AR52171). NIH Science Officers on this project are Deborah Ader, PhD, Susan Czajkowski, PhD, Lawrence Fine, MD, DrPH, Louis Quatrano, PhD, Bryce Reeve, PhD, William Riley, PhD, and Susana Serrate-Sztein, PhD. This manuscript was reviewed by the PROMIS Publications Subcommittee prior to external peer review. See the web site at for additional information on the PROMIS cooperative group.


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Karon F. Cook
    • 1
    • 2
    Email author
  • Michael A. Kallen
    • 3
  • Dagmar Amtmann
    • 1
  1. 1.Department of Rehabilitation MedicineUniversity of WashingtonSeattleUSA
  2. 2.HoustonUSA
  3. 3.Department of General Internal MedicineUniversity of Texas M. D. Anderson Cancer CenterHoustonUSA

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