(Link 4; = 0.001) on Finally, there is a Fast p Accuracy for Increasing Risk Factors. direct, negative effect of on (p = Prepopulation Accuracy for Increasing Risk Factors 0.029). [Insert Figure 2 about here] Consistent with Griffith et al. [2015], we use the bias-corrected bootstrapping method (Preacher and Hayes [2008]) to formally test for mediating effects because simulation tests indicate that it is the most accurate method for sample sizes less than 500 (Hayes and Scharkow [2013]). The findings are consistent with those from the structural equation model. Specifically, the 90% bias-corrected confidence interval for Stick with is (-2.65, -1.57), showing the negative effect of prepopulation on accuracy Last Year through the stick-with-last year response. The confidence interval for is (-0.43, Work Fast -0.04), supporting the negative effect of prepopulation on accuracy through the work fast response. The direct negative effect of prepopulation on accuracy for increasing risks suggests there are other mediators (e.g., Rucker et al. [2011]). For example, prepopulation may lead auditors to be more concerned about the increased substantive testing (i.e., reduced efficiency) ramifications of increasing risk ratings. 4.1.2 for Decreasing Risk Factors Figure 3 illustrates the model for decreasing risk factors. Again, the model fits the data well (χ2(1) = 0.67, = 0.413; CFI = 1.00; RMSEA = 0.00). As expected, there are p positive effects of on (Link 1; < 0.001) and Prepopulation Stick with Last Year p Work (Link 2; = 0.032). Also as expected, there are negative effects of Fast p Stick with Last (Link 3; < 0.001) and (Link 4; = 0.001) on Year p Work Fast p Accuracy for Decreasing
Fast (Link 4; p = 0.001) on Accuracy for Increasing Risk Factors. Finally, there is a direct, negative effect of Prepopulation on Accuracy for Increasing Risk Factors (p = 0.029). [Insert Figure 2 about here] Consistent with Griffith et al. [2015], we use the bias-corrected bootstrapping method (Preacher and Hayes [2008]) to formally test for mediating effects because simulation tests indicate that it is the most accurate method for sample sizes less than 500 (Hayes and Scharkow [2013]). The findings are consistent with those from the structural equation model. Specifically, the 90% bias-corrected confidence interval for Stick with Last Year is (-2.65, -1.57), showing the negative effect of prepopulation on accuracy through the stick-with-last year response. The confidence interval for Work Fast is (-0.43, -0.04), supporting the negative effect of prepopulation on accuracy through the work fast response. The direct negative effect of prepopulation on accuracy for increasing risks suggests there are other mediators (e.g., Rucker et al. [2011]). For example, prepopulation may lead auditors to be more concerned about the increased substantive testing (i.e., reduced efficiency) ramifications of increasing risk ratings. 4.1.2 for Decreasing Risk Factors Figure 3 illustrates the model for decreasing risk factors. Again, the model fits the data well (χ2(1) = 0.67, p = 0.413; CFI = 1.00; RMSEA = 0.00). As expected, there are positive effects of Prepopulation on Stick with Last Year (Link 1; p < 0.001) and Work Fast (Link 2; p = 0.032). Also as expected, there are negative effects of Stick with Last Year (Link 3; p < 0.001) and Work Fast (Link 4; p = 0.001) on Accuracy for Decreasing
BMR_JARforPublication_FINAL-1.docx