Academic Pediatrics
Volume 11, Issue 2 , Pages 128-135 , March 2011

Internal Psychometric Properties of the Children with Special Health Care Needs Screener

  • Adam C. Carle, PhD

      Affiliations

    • Corresponding Author InformationAddress correspondence to Adam C. Carle, PhD, Department of Pediatrics, Division of Health Policy and Clinical Effectiveness, Cincinnati Children’s Hospital and Medical Center, 3333 Burnett Ave MLC 7014, Cincinnati, Ohio 45229.
  • ,
  • Stephen J. Blumberg, PhD
  • ,
  • Charlie Poblenz

Received 18 February 2009 ,Accepted 13 November 2009.

References 

  1. Stein REK. Measurement of children’s health. Ambul Pediatr. 2004;4:365–370
  2. Department of Health and Human Services. Healthy People 2010. Available at: http://www.health.gov/healthypeople/document/html/uih/uih_2.htm. Accessed February 21, 2007.
  3. McPherson M, Arango P, Fox H, et al. A new definition of children with special health care needs. Pediatrics. 1998;102:137–140
  4. Stein REK, Jessop DJ. What Diagnosis does not tell–the case for a noncategorical approach to chronic illness in childhood. Soc Sci Med. 1989;29:769–778
  5. Bethell C, Read D, Stein R, et al. Identifying children with special health care needs: development and evaluation of a short screening tool. Ambul Pediatr. 2002;2:38–48
  6. Bethell C, Read D, Neff J, et al. Comparison of the children with special health care needs screener to the questionnaire to identify children with chronic conditions–revised. Ambul Pediatr. 2002;2:49–57
  7. U.S. Department of Health and Human Services . Health Resources and Services Administration, Maternal and Child Health Bureau. The National Survey of Children with Special Health Care Needs Chartbook 2005–2006. Rockville, Md. 2007;U.S. Department of Health and Human Services
  8. McPherson M, Honberg L. Identification of children with special health care needs: a cornerstone to achieving healthy people 2010. Ambul Pediatr. 2002;2:22–23
  9. McPherson M, Weissman G, Strickland BB, et al. Implementing community-based systems of services for children and youths with special health care needs: how well are we doing?. Pediatrics. 2004;113:1538–1544
  10. Read D, Bethell C, Blumberg SJ, et al. An evaluation of the linguistic and cultural validity of the Spanish language version of the children with special health care needs screener [serial online]. Matern Child Health J. 2007;11:568–585
  11. Bethell C, Read D, Blumberg S, Newacheck P. Is the prevalence of children and youth with special health care needs increasing? Toward an understanding of variations in findings across three national surveys. Matern Child Health J. 2008;12:1–14
  12. Child and Adolescent Health Measurement Initiative . Approaches to Identifying Children and Adults with Special Health Care Needs: A Resource Manual for State Medicaid Agencies and Managed Care Organizations. Portland, Ore: Oregon Health & Science University; 2002;
  13. Stein RE, Westbrook LE, Bauman LJ. Questionnaire for identifying children with chronic conditions (QuICCC): a measure based on a noncategorical approach. Pediatrics. 1997;99:513–521
  14. Westbrook LE, Silver EJ, Stein RE. Implications for estimates of disability in children: a comparison of definitional components [serial online]. Pediatrics. 1998;101:1025–1030
  15. Newacheck PW, Halfon N. Prevalence and impact of disabling chronic conditions in childhood [serial online]. Am J Public Health. 1998;88:610–617
  16. Bramlett M, Read D, Bethell C, Blumberg S. Differentiating subgroups of children with special health care needs by health status and complexity of health care needs. Matern Child Health J. 2009;13:151–163
  17. Blumberg SJ, Welch EM, Chowdhury SR, et al. Design and operation of the National Survey of Children With Special Health Care Needs, 2005-2006. Vital Health Stat. 2008;45:1–132
  18. McDonald RP. Test Theory: A Unified Treatment. Mahwah, NJ: Erlbaum; 1999;
  19. Nunnally JC, Berstein I. Psychometric Theory. 2nd ed. New York, NY: McGraw-Hill; 1994;701
  20. Henson RK. Understanding internal consistency reliability estimates: a conceptual primer on coefficient alpha. Meas Eval Couns Dev. 2001;34:177–189
  21. Carle AC. Interpreting the results of studies using latent variable models to assess data quality: an empirical example using confirmatory factor analysis. Qual Quant. In press.
  22. Embretson S, Reise SP. Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates; 2000.
  23. Chang C, Reeve BB. Item response theory and its applications to patient-reported outcomes measurement. Eval Health Prof. 2005;28:264–282
  24. Embretson S, Hershberger S. The new rules of measurement: what every psychologist and educator should know. Mahwah, NJ: Lawrence Erlbaum Associates; 1999.
  25. Flannery WP, Reise SP, Widaman KF. An item response theory analysis of the general and academic scales of the Self-Description Questionnaire II. J Res Pers. 1995;29:168–188
  26. Hambleton RK. Item Response Theory. Boston, Mass: Kluwer; 1985;
  27. Gregorich SE. Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework. Med Care. 2006;44:S78–S94
  28. McHorney CA, Fleishman JA. Assessing and understanding measurement equivalence in health outcome measures. Issues for further quantitative and qualitative inquiry. Med Care. 2006;44:S205–S210
  29. Samejima F. Estimation of latent ability using a response pattern of graded scores. Psychometrika Monogr Suppl. 1969;34(4 pt 2):100
  30. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6:1–55
  31. Hu L, Bentler PM. Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychol Methods. 1998;3:424–453
  32. Millsap RE, Yun-Tein J. Assessing factorial invariance in ordered-categorical measures. J Multivariate Behav Res. 2004;39:479–515
  33. Stone CA. Monte Carlo based null distribution for an alternative goodness-of-fit test statistic in IRT models. J Educ Meas. 2000;37:58–75
  34. Stone CA. Empirical power and type I error rates for an IRT fit statistic that considers the precision of ability estimates. Educ Psychol Meas. 2003;63:566–583
  35. Stone CA, Zhang B. Assessing goodness of fit of item response theory models: a comparison of traditional and alternative procedures. J Educ Meas. 2003;40:331–352
  36. Bjorner JB, Chang C, Thissen D, Reeve BB. Developing tailored instruments: item banking and computerized adaptive assessment. Qual Life Res. 2007;16(supp11):95–108
  37. Muthen LK, Muthen BO. Mplus User’s Guide. Fourth Edition. Los Angeles, Calif: Muthén & Muthén; 1998–2007.
  38. In:  Skinner CJ,  Holt D,  Smith TMF editor. Analysis of Complex Surveys. Chichester, England: Wiley; 1989;
  39. Asparouhov T, Muthen B. Multivariate statistical modeling with survey data. Proceedings of the Federal Committee on Statistical Methodology (FCSM) Research Conference. November 14–16, 2005, Sheraton Crystal City Hotel, Arlington, Va. Federal Committee on Statistical Methodology, Washington, DC; 2005.

PII: S1876-2859(09)00315-5

doi: 10.1016/j.acap.2009.11.006

Academic Pediatrics
Volume 11, Issue 2 , Pages 128-135 , March 2011