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. 2013 Sep;94(9):1653-60.
doi: 10.1016/j.apmr.2013.03.011. Epub 2013 Mar 29.

Development of a self-report physical function instrument for disability assessment: item pool construction and factor analysis

Christine M McDonough  1 , Alan M Jette, Pengsheng Ni, Kara Bogusz, Elizabeth E Marfeo, Diane E Brandt, Leighton Chan, Mark Meterko, Stephen M Haley, Elizabeth K Rasch
Affiliations

Development of a self-report physical function instrument for disability assessment: item pool construction and factor analysis

Christine M McDonough et al. Arch Phys Med Rehabil. 2013 Sep.

Abstract

Objectives: To build a comprehensive item pool representing work-relevant physical functioning and to test the factor structure of the item pool. These developmental steps represent initial outcomes of a broader project to develop instruments for the assessment of function within the context of Social Security Administration (SSA) disability programs.

Design: Comprehensive literature review; gap analysis; item generation with expert panel input; stakeholder interviews; cognitive interviews; cross-sectional survey administration; and exploratory and confirmatory factor analyses to assess item pool structure.

Setting: In-person and semistructured interviews and Internet and telephone surveys.

Participants: Sample of SSA claimants (n=1017) and a normative sample of adults from the U.S. general population (n=999).

Interventions: Not applicable.

Main outcome measure: Model fit statistics.

Results: The final item pool consisted of 139 items. Within the claimant sample, 58.7% were white; 31.8% were black; 46.6% were women; and the mean age was 49.7 years. Initial factor analyses revealed a 4-factor solution, which included more items and allowed separate characterization of: (1) changing and maintaining body position, (2) whole body mobility, (3) upper body function, and (4) upper extremity fine motor. The final 4-factor model included 91 items. Confirmatory factor analyses for the 4-factor models for the claimant and the normative samples demonstrated very good fit. Fit statistics for claimant and normative samples, respectively, were: Comparative Fit Index=.93 and .98; Tucker-Lewis Index=.92 and .98; and root mean square error approximation=.05 and .04.

Conclusions: The factor structure of the physical function item pool closely resembled the hypothesized content model. The 4 scales relevant to work activities offer promise for providing reliable information about claimant physical functioning relevant to work disability.

Keywords: CAT; CFA; CFI; Disability evaluation; Disabled persons; EFA; Factor analysis, statistical; ICF; IRT; Insurance; International Classification of Functioning, Disability and Health; PROMIS; Patient-Reported Outcomes Measurement Information System; Questionnaires; RMSEA; Rehabilitation; SCI-FI; SSA; Social Security Administration; Spinal Cord Injury-Functional Index; TLI; Tucker-Lewis Index; comparative fit index; computer-adaptive testing; confirmatory factor analysis; exploratory factor analysis; item response theory; root mean square error of approximation.

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Conflict of interest statement

COMMERCIAL SUPPORT/CONFLICTS STATEMENT:

We certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated AND, we certify that all financial and material support for this research and work are clearly identified in the title page of the manuscript (Christine M. McDonough, Alan M. Jette, Pengsheng Ni, Kara Bogusz, Elizabeth E Marfeo, Diane E Brandt, Leighton Chan, Mark Meterko, Stephen M. Haley, Elizabeth K. Rasch).

Figures

Figure 1
Figure 1
Physical Function Item Pool Development Process
Figure 2
Figure 2
Physical Function Final Model
See this image and copyright information in PMC

References

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    1. IOM (Institute of Medicine) Improving the Social Security disability decision process. Washington, D.C: 2007.
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    1. US Department of Health and Human Services FDA. Guidance for industry patient-reported outcome measures: use in medical product development to support labeling claims. Silver Spring: US Department of Health and Human Services Food and Drug Administration; 2009.

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