Russel (2014) uses a bivariate correlation analysis to examine the relationship between stress

Russel (2014) uses a bivariate correlation analysis to examine the relationship between stress, burnout, and transformational leadership (TFL) in high-risk occupations. Correlational designs measure the existence and strength of a relationship between variables (Warner, 2012). Russell (2014) utilized a bivariate correlational analysis because he/she is investigating the relationship between one independent variable and one dependent variable.

A perfect correlation is indicated with a value of 1.0, and positive/negative correlations indicate the direction of the relationship between IV and DV (Warner, 2012). For example, a positive/direct correlation suggests as the IV increases, so does the DV, or as the IV decreases so does the DV. In contrast a negative/inverse relationship suggests as the IV increases/decreases the DV does the opposite.

Russel (2014) use a Pearson correlation coefficient to answer two research questions (RQ). One RQ is, “Will stress (IV) and burnout (DV) among high-risk occupations demonstrate a relationship? While the null hypothesis states, “There is no relationship between burnout (IV) and stress (DV) among high-risk occupations.” Another RQ can be, “Will transformational leadership (IV) and burnout (DV) among high-risk occupations demonstrate a relationship?” While the null hypothesis states, “There is no relationship between transformational leadership (IV) and burnout (DV) among high-risk occupations.”

A review of a table containing significant correlations suggests the null hypothesis can be rejected (Russel, 2014). Specifically, stress is significant positively correlated with burnout (p<.01), transformational leadership (TFL) is significantly negatively correlated with the DV (p<.01) (Russel, 2014). Overall, results suggest as stress increases so does burnout among people in high-risk occupations; and, the more leadership is characterized as (TFL) the less likely burnout is reported among high-risk occupations. Since results are significant, both of the null hypothesis can be rejected; and the data stands alone.


Lisa M. Russell. (2014). An empirical investigation of high-risk occupations : Leader influence on employee stress and burnout among police. Management Research Review, 37(4), 367–384.

Warner, R. M. (2012). Applied statistics from bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.

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