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Published in: BMC Medical Informatics and Decision Making 1/2019

Open Access 01-12-2019 | Prostate Cancer | Research article

Item response theory analysis and properties of decisional conflict scales: findings from two multi-site trials of men with localized prostate cancer

Authors: Rachel A. Pozzar, Donna L. Berry, Fangxin Hong

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

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Abstract

Background

Decisional conflict is associated with decision quality and may affect decision outcomes. In the health sciences literature, the Decisional Conflict Scale is widely used to measure decisional conflict, yet limited research has described the psychometric properties of the Decisional Conflict Scale subscales and of the low literacy version of the scale. The purpose of this secondary data analysis was therefore to examine properties of the original (DCS-12) and low literacy (LL DCS-10) Decisional Conflict Scales using Classical Measurement Theory and Item Response Theory.

Methods

Data from two multi-site trials of men with prostate cancer were used to analyze the DCS-12, LL DCS-10, and an aggregated DCS-12 dataset in which five response options were aggregated into three. Internal consistency was estimated with Cronbach’s alphas. Subscale correlations were evaluated with Pearson’s correlation coefficient. Item difficulty, item discrimination, and test information were evaluated using Graded Response Modeling (GRM). The likelihood ratio test guided model selection.

Results

Cronbach’s alphas for the total scales and three of four subscales were ≥ 0.85. Alphas ranged from 0.34–0.57 for the support subscales. Subscale correlations ranged from 0.42–0.71 (P < 0.001). Items on the DCS-12 exhibited the widest range of difficulty. Two items on the support subscale had low to moderate discrimination and contributed little information. Only the DCS-12 was informative across the full range of decisional conflict values.

Conclusions

Lack of precision in the support subscale raises concerns about subscale validity. The DCS-12 is most capable of discriminating between respondents with high and low decisional conflict. Evaluation of interventions to reduce decisional conflict must consider the above findings.
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Literature
4.
go back to reference Eastwood JA, Doering L, Roper J, Hays RD. Uncertainty and health-related quality of life 1 year after coronary angiography. Am J Crit Care. 2008;17:232.PubMed Eastwood JA, Doering L, Roper J, Hays RD. Uncertainty and health-related quality of life 1 year after coronary angiography. Am J Crit Care. 2008;17:232.PubMed
9.
go back to reference Janis IL, Mann L. Coping with decisional conflict: an analysis of how stress affects decision-making suggests interventions to improve the process. Am Sci. 1976;64:657–67. Janis IL, Mann L. Coping with decisional conflict: an analysis of how stress affects decision-making suggests interventions to improve the process. Am Sci. 1976;64:657–67.
10.
go back to reference NANDA International. In: Kamitsuru THS, editor. Nursing diagnoses: definitions and classification 2015–2017. 10th ed. Hoboken: Wiley Blackwell; 2014. NANDA International. In: Kamitsuru THS, editor. Nursing diagnoses: definitions and classification 2015–2017. 10th ed. Hoboken: Wiley Blackwell; 2014.
15.
go back to reference Martinho MJCM, Da Silva MMMFP, Angelo M. Scale of conflict in health care decision-making: an instrument adapted and validated for the Portuguese language. Rev Esc Enferm USP. 2013;47:576.CrossRefPubMed Martinho MJCM, Da Silva MMMFP, Angelo M. Scale of conflict in health care decision-making: an instrument adapted and validated for the Portuguese language. Rev Esc Enferm USP. 2013;47:576.CrossRefPubMed
25.
go back to reference Waltz CF, Strickland O, Lenz E. Measurement in nursing and health research. New York: Springer; 2010. Waltz CF, Strickland O, Lenz E. Measurement in nursing and health research. New York: Springer; 2010.
26.
go back to reference DeVellis RF. In: Bickman L, Rog D, editors. Scale development: theory and applications. 2nd ed. Thousand Oaks: SAGE Publications; 2003. 171 p. DeVellis RF. In: Bickman L, Rog D, editors. Scale development: theory and applications. 2nd ed. Thousand Oaks: SAGE Publications; 2003. 171 p.
28.
go back to reference Cella D, Chang C, Heinemann A. Item response theory (IRT): applications in quality of life measurement, analysis, and interpretation. In: Mesbah M, Cole B, Lee M, editors. Statistical methods for quality of life studies. New York: Springer; 2002. p. 169–85.CrossRef Cella D, Chang C, Heinemann A. Item response theory (IRT): applications in quality of life measurement, analysis, and interpretation. In: Mesbah M, Cole B, Lee M, editors. Statistical methods for quality of life studies. New York: Springer; 2002. p. 169–85.CrossRef
30.
go back to reference Baker FB. In: Kim S-H, editor. The basics of item response theory using R. Cham: Springer; 2017.CrossRef Baker FB. In: Kim S-H, editor. The basics of item response theory using R. Cham: Springer; 2017.CrossRef
31.
go back to reference Samejima F. Graded response model. In: van der Linden WJ, Hambleton RK, editors. Handbook of modern item response theory. New York: Springer; 1997. Samejima F. Graded response model. In: van der Linden WJ, Hambleton RK, editors. Handbook of modern item response theory. New York: Springer; 1997.
Metadata
Title
Item response theory analysis and properties of decisional conflict scales: findings from two multi-site trials of men with localized prostate cancer
Authors
Rachel A. Pozzar
Donna L. Berry
Fangxin Hong
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-019-0853-5

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