The Political Competence Scale for Nurses (PCS-N): Instrument Development and Psychometric Evaluation

Abstract

Background: Few tools are available to measure nurses' political competence, and the existing ones have limitations in reflecting the multidimensional factors of competence. This study developed a multidimensional political competence measurement tool-the Political Competence Scale for Nurses (PCS-N)-and assessed its validity and reliability in measuring nurses' political competence levels. Methods: This methodological study was based on the tool development and tool-test stages suggested by DeVellis. Results: The validity and reliability of the PCS-N were established. The PCS-N was tested for construct validity through content validity, item-total correlations of preliminary items, and exploratory factor analysis. The PCS-N comprises 35 items across four factors: political knowledge, political efficacy, political interaction, and political activity. The suitability of this measurement tool was established through construct validity and confirmatory factor analysis. Concurrent validity was verified and was significantly correlated with existing political science measurement tools, political efficacy, and political interest (r = 0.511, p < 0.001). Internal consistency reliability (Cronbach's α = 0.951) and test-retest reliability were also established, confirming the stability of the PCS-N. Conclusions: The PCS-N can be used to evaluate nurses' political competence and provide a basis for constructing education and training programs to strengthen political competence and evaluate their effectiveness.

Keywords: competence; measurement; nurses; politics; validation study.

Figure 1

The tool development process based on DeVellis' scale development phase [33]. Abbreviations: CFI, comparative fit index; CVI, content validity index; GFI, goodness-of-fit index; KMO, Kaiser–Mayer–Olkin; PGFI, parsimonious goodness-of-fit index; PNFI, parsimonious normed fit index; RMR, root mean residual; RMSEA, root mean square error of approximation.

Figure 2

The proposed models of the tool by confirmatory factor analysis. (a) Model fit. (b) Modified model fit.