Abstract
Purpose
The patient activation measure short form (PAM-13) assesses patients’ self-reported health management skills, knowledge, confidence, and motivation. We used item response theory to evaluate the psychometric properties of the PAM-13 utilized in rural settings.
Methods
A Rasch partial credit model analysis was conducted on the PAM-13 instrument using a sample of 812 rural patients recruited by providers and our research staff. Specially, we examined dimensionality, item fit, and quality of measures, category response curves, and item differential functioning. Convergent and divergent validities were also examined.
Findings
The PAM-13 instrument has excellent convergent and divergent validities. It is fairly unidimensional, and all items fit the Rasch model well. It has relatively high person and item reliability indices. Majority of the items were free of item differential functioning. There were, however, some issues with ceiling effects. Additionally, there was a lack of responses for category one across all items.
Conclusions
Patient activation measure short form (PAM-13) performs well in some areas, but not all. In general, more items need to be added to cover the upper end of the trait. The four response categories of PAM-13 should be collapsed into three.
This is a preview of subscription content, log in via an institution to check access.
References
Hibbard, J. H., Stockard, J., Mahoney, E. R., & Tusler, M. (2004). Development of the patient activation measure (PAM): Conceptualizing and measuring activation in patients and consumers. Health Service Research, 39(4 Pt 1), 1005–1026.
Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. Chicago: The University of Chicago Press.
Virani, S., Burke, L., Remick, S. C., & Abraham, J. (2011). Barriers to recruitment of rural patients in cancer clinical trials. Journal of Oncology Practice, 7(3), 172–177.
Buzza, C., Ono, S. S., Turvey, C., Wittrock, S., Noble, M., Reddy, G., et al. (2011). Distance is relative: Unpacking a principal barrier in rural healthcare. Journal of General Internal Medicine, 26(Suppl 2), 648–654.
Tollefson, J., Usher, K., & Foster, K. (2011). Relationship in pain: The experience of relationships to people living with chronic pain in rural areas. International Journal of Nursing Practice, 17, 478–485.
Hartley, D. (2004). Rural health disparities, population health, and rural culture. American Journal of Public Health, 94(10), 1675–1678.
Phillips, C. D., Hawes, C., & Leyk Williams, M. (2003). Nursing homes in rural and urban areas, 2000. College Station, TX, USA: The Texas A&M University System Health Science Center, School of Rural Public Health, Southwest Rural Health Research Center.
Eberhardt, M., Ingram, D., & Makuc, D. (2001). Health, United States 2001: Urban and rural health chart book. Hyattsville, MD, USA: National Center for Health Statistics.
Coward, R. T., McLaughlin, D., & Duncan, R. P. (1994). An overview of health and aging in rural America. In R. T. Coward, G. Brill, & G. Kukulaka (Eds.), Health services for rural elders. New York, NY, USA: Springer.
Norton, C. H., & McManus, M. A. (1989). Background tables on demographic characteristics, health status, and health services utilization. Health Services Research, 23(6), 725–756.
Schlenker, R. E., Powell, M. C., & Goodrich, G. K. (2002). Rural-urban home health care differences before the balanced budget act of 1997. Journal of Rural Health, 18(2), 359–372.
Gamm, L., Hutchison, L., & Dabney, B. J. (2003). Rural healthy people 2010: A companion document to healthy people 2010. College Station, TX, USA: The Texas A&M University System Health Science Center, School of Rural Public Health, Southwest Rural Health Research Center.
Darby, C., Crofton, C., & Clancy, C. M. (2006). Consumer assessment of health providers and systems (CAHPS): Evolving to meet stakeholder needs. American Journal of Medical Quality, 21(2), 144–147.
Bond, T. G., & Fox, C. M. (2001). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ, USA: Lawrence Erlbaum.
Hung, M., Clegg, D. O., Greene, T., & Saltzman, C. L. (2011). Evaluation of the PROMIS physical function item bank in orthopaedic patients. Journal of Orthopaedic Research, 29(6), 947–953.
Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149–174.
Masters, G. N. (1988). The analysis of partial credit scoring. Applied Measurement in Education, 1(4), 279–297.
Joyce, T. B., & Yates, S. M. (2007). A Rasch analysis of the academic self-concept questionnaire. International Education Journal, 8(2), 470–484.
Linacre, J. M. (2011). A user’s guide to WINSTEPS/MINISTEPS rasch model computer programs. USA: Elsevier.
Teresi, J., Ocepek-Welikson, K., Kleinman, M., Eimicke, J. P., Crane, P. K., Jones, R. N., et al. (2009). Analysis of differential item functioning in the depression item bank from the patient reported outcome measurement information system (PROMIS): An item response theory approach. Psychology Science Quarterly, 51(2), 148–180.
Thissen, D., Steinberg, L., & Gerrard, M. (1986). Beyond group-mean differences: The concept of item bias. Psychological Bullentin, 99, 118–128.
Wang, W. C., & Yeh, Y. L. (2003). Effects of anchor item methods on differential item functioning detection with likelihood ratio test. Applied Psychological Measurement, 27, 479–498.
Acknowledgments
This investigation was supported by the Agency for Healthcare Research and Quality research grant number R18-HS-017308.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hung, M., Carter, M., Hayden, C. et al. Psychometric assessment of the patient activation measure short form (PAM-13) in rural settings. Qual Life Res 22, 521–529 (2013). https://doi.org/10.1007/s11136-012-0168-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11136-012-0168-9