Abstract
Date Presented 04/22/2023
It is critical to have measures of participation that tap a common underlying construct to assess progress of rehabilitation goals. This factor analysis of three measures found that a hierarchical model had the best theoretical and statistical fit.
Primary Author and Speaker: Samantha Randolph
Contributing Authors: Chih-Hung Chang, Lisa Connor
PURPOSE: Participation is an important outcome of rehabilitation services (Eyssen et al., 2011) and essential for client well-being (Egan et al., 2014). It is critical, therefore, to have measures for participation-related outcomes that tap a common underlying construct. This study analyzes the underlying factor structure of participation measures commonly used with stroke survivors: the Activity Card Sort (ACS), the Reintegration to Normal Living index (RNL), and the Stroke Impact Scale Participation score (SIS Participation).
DESIGN: This analysis is from a cross-sectional study of community-dwelling stroke survivors. Participants (N = 132) met the following inclusion criteria: ≥18 years old, ≥6 months post-stroke, able to tolerate 6 hours of testing over 2 sessions, and able to travel to testing site; and exclusion criteria: history of prior strokes, traumatic brain injury, pre-stroke disability or neurological conditions, and self-reported severe medical or psychiatric illness.
METHOD: Three measures examined participation in this study: the ACS, RNL, and SIS Participation. Data analyses used the ‘stats’ and ‘lavaan’ (Rosseel, 2012) packages in R (R Core Team, 2021). An exploratory factor analysis was conducted to gain a preliminary understanding of factor structure, and a confirmatory factor analysis was performed to further refine the model.
RESULTS: A hierarchical model with participation as the higher-order factor was identified as having the best theoretical and statistical fit (X2 (7) = 9.67, p =.21, RMSEA = .05, CFI = .99, TLI = .99). Factor loadings for each of the measures ranged from .74 to .92.
CONCLUSION: The results of this analysis demonstrate participation to be a complex construct with contributing characteristics.
IMPACT STATEMENT: researchers and clinicians should consider multiple dimensions of participation when selecting outcome measures for stroke survivors and understand how they contribute to a higher-order participation construct.
References
Eyssen, I., Steuljens, M., Dekker, J., & Terwee, C. (2011). A systematic review of instruments assessing participation: Challenges in defining participation. Archives of Physical Medicine & Rehabilitation, 92, 983-992.
Egan, M., Davis, C. G., Dubouloz, C. J., Kessler, D., & Kubina, L. A. (2014). Participation and well-being poststroke: evidence of reciprocal effects. Archives of physical medicine and rehabilitation, 95(2), 262-268.
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
