Applying bifactor modelling to improve the clinical interpretive values of Functional Independence Measure in adults with acquired brain injury

References

• Stineman MG, Shea JA, Jette A, et al. The functional independence measure: tests of scaling assumptions, structure, and reliability across 20 diverse impairment categories. Arch Phys Med Rehabil. 1996;77:1101–1108.
   PubMed Web of Science ®Google Scholar
• Gallegos-Kearin V, Mix J, Knowlton S, et al. Outcome trends of adult cancer patients receiving in-patient rehabilitation: a 10-year review of the uniform data system for medical rehabilitation. Arch Phys Med Rehabil. 2016;97:e46–e46.
   Google Scholar
• Turner-Stokes L, Vanderstay R, Stevermuer T, et al. Comparison of rehabilitation outcomes for long term neurological conditions: a cohort analysis of the Australian rehabilitation outcomes centre dataset for adults of working age. PLoS One. 2015;10:e0132275.
   PubMed Web of Science ®Google Scholar
• Khan F, Turner-Stokes L, Stevermuer T, et al. Multiple sclerosis rehabilitation outcomes: analysis of a national casemix data set from Australia. Mult Scler. 2009;15:869–875.
   PubMed Web of Science ®Google Scholar
• Choo SX, Stratford P, Richardson J, et al. Comparison of the sensitivity to change of the Functional Independence Measure with the assessment of Motor and Process skills within different rehabilitation populations. Disabil Rehabil. 2017. DOI:10.1080/09638288.2017.1375033
   PubMed Web of Science ®Google Scholar
• Scaratti C, Leonardi M, Sattin D, et al. Work-related difficulties in patients with traumatic brain injury: a systematic review on predictors and associated factors. Disabil Rehabil. 2017;39:847–855.
   PubMed Web of Science ®Google Scholar
• Guerriero EN, Smith PM, Stergiou-Kita M, et al. Rehabilitation utilization following a work-related traumatic brain injury: a sex-based examination of workers' compensation claims in Victoria, Australia. PLoS One 2016;11:e0151462.
   PubMed Web of Science ®Google Scholar
• The Australian Government the Treasury. [Internet] 2015; Available from: https://treasury.gov.au/programs-initiatives-consumers-community/niis/agreed-minimum-benchmarks-for-motor-vehicle-accidents/.
   Google Scholar
• Granger CV, Deutsch A, Russell C, et al. Modifications of the FIM instrument under the inpatient rehabilitation facility prospective payment system. Am J Phys Med Rehabil. 2007;86:883–892.
   PubMed Web of Science ®Google Scholar
• Middleton A, Graham JE, Lin Y-L, et al. Motor and Cognitive functional status are associated with 30-day unplanned rehospitalization following post-acute care in medicare fee-for-service beneficiaries. J Gen Intern Med. 2016;31:1427–1434.
   PubMed Web of Science ®Google Scholar
• Dallmeijer AJ, Dekker J, Roorda LD, et al. Differential item functioning of the Functional Independence Measure in higher performing neurological patients. J Rehabil Med. 2005;37:346–352.
   PubMed Web of Science ®Google Scholar
• Granger CV, Markello SJ, Graham JE, et al. The uniform data system for medical rehabilitation: report of patients with lower limb joint replacement discharged from rehabilitation programs in 2000–2007. Am J Phys Med Rehabil. 2010;89:781–794.
   PubMed Web of Science ®Google Scholar
• Chen FF, Hayes A, Carver CS, et al. Modeling general and specific variance in multifaceted constructs: a comparison of the bifactor model to other approaches. J Pers. 2012;80:219–251.
   PubMed Web of Science ®Google Scholar
• Rodriguez A, Reise SP, Haviland MG. Evaluating bifactor models: calculating and interpreting statistical indices. Psychol Methods. 2016;21:137–150.
   PubMed Web of Science ®Google Scholar
• World Health Organization. Rehabilitation 2030: a call for action. 2017 [cited Dec 8]. Available from: http://www.who.int/disabilities/care/Rehab2030MeetigReport_plain_text_version.pdf
   Google Scholar
• Reise SP, Morizot J, Hays RD. The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Qual Life Res. 2007;16:19–31.
   PubMed Web of Science ®Google Scholar
• Canivez GL. Bifactor modeling in construct validation of multifactored tests: implications for multidimensionality and test interpretation. In: Schweizer K, DiStefano C, editors. Principles and methods of test construction: standards and recent advancements. Gottingen, Germany: Hogrefe; 2016. p. 247–271.
   Google Scholar
• Reise SP. The rediscovery of bifactor measurement models. Multivariate Behav Res. 2012;47:667–696.
   PubMed Web of Science ®Google Scholar
• Reise SP, Bonifay WE, Haviland MG. Scoring and modeling psychological measures in the presence of multidimensionality. J Pers Assess. 2013;95:129–140.
   PubMed Web of Science ®Google Scholar
• Rodriguez A, Reise SP, Haviland MG. Applying Bifactor statistical indices in the evaluation of psychological measures. J Pers Assess. 2016;98:223–237.
   PubMed Web of Science ®Google Scholar
• Reise SP, Scheines R, Widaman KF, et al. Multidimensionality and structural coefficient bias in structural equation modeling: a bifactor perspective. Educ Psychol Meas. 2013;73:5–26.
   Web of Science ®Google Scholar
• Streiner DL. Starting at the beginning: an introduction to coefficient alpha and internal consistency. J Pers Assess. 2003;80:99–103.
   PubMed Web of Science ®Google Scholar
• Miller MB. Coefficient alpha: a basic introduction from the perspectives of classical test theory and structural equation modeling. Struct Equation Model-Multidisciplinary J. 1995;2:255–273.
   Web of Science ®Google Scholar
• Bentler PM. Alpha, dimension-free, and model-based internal consistency reliability. Psychometrika. 2009;74:137–143.
   PubMed Web of Science ®Google Scholar
• McDonald RP. Test theory: a unified treatment. London, Mahwah, NJ: L. Erlbaum Associates; 1999.
   Google Scholar
• Zinbarg RE, Revelle W, Yovel I, et al. Cronbach's alpha, Revelle's beta, and McDonald's (omega H): their relations with each other and two alternative conceptualizations of reliability. Psychometrika. 2005;70:123–133.
   Web of Science ®Google Scholar
• DeVellis RF. Scale development: theory and applications. Thousand Oaks, CA: SAGE; 2012.
   Google Scholar
• Stineman MG, Jette A, Fiedler R, et al. Impairment-specific dimensions within the functional independence measure. Arch Phys Med Rehabil. 1997;78:636–643.
   PubMed Web of Science ®Google Scholar
• Rezaei S, Moghadam DA, Khodadadi N, et al. Functional independence measure in Iran: a confirmatory factor analysis and evaluation of ceiling and floor effects in traumatic brain injury patients. Arch Trauma Res. 2015;4:e25363.
   PubMed Web of Science ®Google Scholar
• Smith-Knapp KIP, Corrigan JD, Arnett JA. Predicting functional independence from neuropsychological tests following traumatic brain injury. Brain Inj. 1996;10:651–662.
   PubMed Web of Science ®Google Scholar
• Brouwer D, Meijer RR, Zevalkink DJ. On the factor structure of the Beck Depression Inventory-II: G is the key. Psychol Assess. 2013;25:136–145.
   PubMed Web of Science ®Google Scholar
• Turner-Stokes L, Williams H, Bill A, et al. Cost-efficiency of specialist inpatient rehabilitation for working-aged adults with complex neurological disabilities: a multicentre cohort analysis of a national clinical data set. BMJ Open. 2016;6:e010238.
   PubMed Web of Science ®Google Scholar
• Brown AW, Therneau TM, Schultz BA, et al. Measure of functional independence dominates discharge outcome prediction after inpatient rehabilitation for stroke. Stroke. 2015;46:1038–1044.
   PubMed Web of Science ®Google Scholar
• Australian Rehabilitation Outcomes Centre. AROC v4 data dictionary for analysts. Wollongong, Australia: AROC; 2017 [cited Dec 8]. Available from: https://apps.ahsri.uow.edu.au/confluence/display/AD/AROC+Data+Dictionaries
   Google Scholar
• IBM Corporation. IBM SPSS Statistics for Windows, Version 24.0. Armonk, (NY): IBM Corporation; 2016.
   Google Scholar
• Muthén LK, Muthén BO. Mplus statistical analysis with latent variables: user’s guide. Los Angeles, CA: Muthén & Muthén; 2012.
   Google Scholar
• Linacre JM. FIM levels as ordinal categories. J Outcome Meas. 2000;4:616–633.
   PubMedGoogle Scholar
• Muthén BO, du Toit SHC, Spisic D. Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. 1993.
   Google Scholar
• Muthén BO. Goodness of fit with categorical and other non-normal variables. In: Bollen KA, Long JS, editors. Testing structural equation models, SAGE focus editions. Newbury Park (CA): Sage; 1993. p. 205–34.
   Google Scholar
• Muthen B, Kaplan D. A comparison of some methodologies for the factor-analysis of non-normal Likert variables. Brit J Math Stat Psychol. 1985;38:171–189.
   Web of Science ®Google Scholar
• Hu L-t, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equation Model-Multidisciplinary J. 1999;6:1–55.
   Web of Science ®Google Scholar
• Yu C-Y. Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes: Los Angeles (CA): ProQuest Dissertations Publishing; 2002.
   Google Scholar
• Stucky BD, Edelen MO, Vaughan CA, et al. The psychometric development and initial validation of the DCI-A short form for adolescent therapeutic community treatment process. J Subst Abuse Treat. 2014;46:516–521.
   PubMed Web of Science ®Google Scholar
• Reise SP, Moore TM, Haviland MG. Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores. J Pers Assess. 2010;92:544–559.
   PubMed Web of Science ®Google Scholar
• Stucky BD, Thissen D, Edelen OM. Using logistic approximations of marginal trace lines to develop short assessments. Appl Psychol Meas. 2013;37:41–57.
   Web of Science ®Google Scholar
• Diamantopoulos A. Introducing Lisrel: a guide for the uninitiated. London: SAGE; 2000.
   Google Scholar
• Anderson JC, Gerbing DW. Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull. 1988;103:411–423.
   Web of Science ®Google Scholar
• Gignac GE, Bates TC, Jang KL. Implications relevant to CFA model misfit, reliability, and the five-factor model as measured by the NEO-FFI. Pers Individ Diff. 2007;43:1051–1062.
   Web of Science ®Google Scholar
• DeMars CE. A tutorial on interpreting bifactor model scores. Int J Test. 2013;13:354–378.
   Google Scholar
• Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale, NJ: L. Erlbaum Associates; 1988.
   Google Scholar
• Granger CV, Divan N, Fiedler RC. Functional assessment scales. a study of persons after traumatic brain injury. Am J Phys Med Rehabil. 1995;74:107–113.
   PubMed Web of Science ®Google Scholar
• Pretz CR, Kean J, Heinemann AW, et al. A Multidimensional Rasch analysis of the Functional Independence Measure Based on the National Institute on Disability, Independent Living, and Rehabilitation Research Traumatic Brain Injury Model Systems National Database. J Neurotrauma. 2016;33:1358–1362.
   PubMed Web of Science ®Google Scholar
• Sijtsma K. On the use, the misuse, and the very limited usefulness of Cronbach's alpha. Psychometrika. 2009;74:107–120.
   PubMed Web of Science ®Google Scholar
• Arango-Lasprilla JC, Ketchum JM, Cifu D, et al. Predictors of Extended rehabilitation length of stay after traumatic brain injury. Arch Phys Med Rehabil. 2010;91:1495–1504.
   PubMed Web of Science ®Google Scholar
• Oh H-J, Glutting JJ, Watkins MW, et al. Correct interpretation of latent versus observed abilities: implications from structural equation modeling applied to the WISC-III and WIAT linking sample. J Spec Educ. 2004;38:159–173.
   Web of Science ®Google Scholar
• Schneider WJ. What if we took our models seriously? Estimating latent scores in individuals. J Psychoeduc Assess. 2013;31:186–201.
   Web of Science ®Google Scholar
• Williamson MLC, Elliott TR, Berry JW, et al. Predictors of health-related quality-of-life following traumatic brain injury. Brain Injury. 2013;27:992.
   PubMed Web of Science ®Google Scholar
• Beauducel A, Herzberg PY. On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Struct Equation Model-Multidisciplinary J. 2006;13:186–203.
   Web of Science ®Google Scholar