The predictive validity of charge nurse personality on objective and subjective performance of subordinates

J Nurs Manag. 2019 Mar;27(2):388-395. doi: 10.1111/jonm.12696. Epub 2018 Aug 31.

Abstract

Aim: This study examines the degree to which the Hogan Personality Inventory (HPI) predicts leadership effectiveness for charge nurses in Danish hospitals.

Background: Personality tests are implemented in health care management in an effort to improve evidence-based personnel selection and recruit more efficient leaders. However, relatively few studies of the predictive validity of personality have been conducted in hospital management.

Methods: Charge nurses (n = 177) from three Danish hospitals completed a five-factor, model-based personality inventory. These were coupled with data from 3,497 subordinates. Cluster-robust regression analysis was used to investigate relationships between personality and short-term sickness absence and satisfaction and leadership ratings for the subordinates.

Results: Low subordinate sickness absence was related to leader extraversion and conscientiousness. Employee satisfaction was related to leader emotional stability, extraversion, and conscientiousness. Leadership ratings were associated with emotional stability.

Conclusions: Personality predicted both objective and subjective measures of performance, although the effects were stronger for objective than subjective measures.

Implications for nursing management: The results lend support to the use of validated personality measures in recruiting and promoting nurses in the health care sector. The use of personality tests should support rather than replace other talent-management measures.

Keywords: charge nurse performance; job satisfaction; leadership, personality traits; quantitative methods.

MeSH terms

  • Adult
  • Denmark
  • Female
  • Humans
  • Job Satisfaction
  • Male
  • Middle Aged
  • Nurse Administrators / psychology*
  • Nurse Administrators / statistics & numerical data
  • Nurses / standards*
  • Personality
  • Personality Inventory / statistics & numerical data*
  • Regression Analysis
  • Surveys and Questionnaires
  • Work Performance / standards*