Data Cleaning
- use job type “1=RN” and “2=Tech” only.
- missing questions and multiple answers to one question are marked as wrong.
All
## Test Summary.post Summary.pre P.Value
## 1 IQsum 0.4 (0.3) 0.3 (0.2) 0.0211
## 3 Qsum 0.7 (0.2) 0.6 (0.2) <.0001
## 5 Scoresum 0.6 (0.1) 0.5 (0.2) <.0001
Jobs Type
result.rn
## Test Summary.post Summary.pre P.Value
## 1 IQsum 0.4 (0.3) 0.3 (0.2) 0.1568
## 5 Qsum 0.7 (0.2) 0.6 (0.1) <.0001
## 9 Scoresum 0.6 (0.1) 0.5 (0.1) <.0001
result.tech
## Test Summary.post Summary.pre P.Value
## 2 IQsum 0.4 (0.3) 0.2 (0.2) 0.0978
## 6 Qsum 0.6 (0.2) 0.5 (0.2) 0.0074
## 10 Scoresum 0.5 (0.1) 0.4 (0.1) 0.0015
# interaction test
summary(lm(Score~Visit*Job, subset(dat3, Test=="IQsum")))
##
## Call:
## lm(formula = Score ~ Visit * Job, data = subset(dat3, Test ==
## "IQsum"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39130 -0.14130 -0.08553 0.16447 0.66447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.33553 0.02893 11.597 <2e-16 ***
## Visitpost 0.05578 0.03910 1.427 0.1551
## Job2 -0.10068 0.05258 -1.915 0.0568 .
## Visitpost:Job2 0.06165 0.07967 0.774 0.4399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2522 on 219 degrees of freedom
## Multiple R-squared: 0.04166, Adjusted R-squared: 0.02853
## F-statistic: 3.173 on 3 and 219 DF, p-value: 0.0251
summary(lm(Score~Visit*Job, subset(dat3, Test=="Qsum")))
##
## Call:
## lm(formula = Score ~ Visit * Job, data = subset(dat3, Test ==
## "Qsum"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.42391 -0.12391 -0.00658 0.09342 0.31818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.60658 0.01783 34.027 < 2e-16 ***
## Visitpost 0.11733 0.02409 4.871 2.13e-06 ***
## Job2 -0.15506 0.03240 -4.786 3.13e-06 ***
## Visitpost:Job2 0.01297 0.04909 0.264 0.792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1554 on 219 degrees of freedom
## Multiple R-squared: 0.2699, Adjusted R-squared: 0.2599
## F-statistic: 26.99 on 3 and 219 DF, p-value: 6.826e-15
summary(lm(Score~Visit*Job, subset(dat3, Test=="Scoresum")))
##
## Call:
## lm(formula = Score ~ Visit * Job, data = subset(dat3, Test ==
## "Scoresum"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.34317 -0.09411 0.01398 0.09790 0.29969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.52914 0.01543 34.288 < 2e-16 ***
## Visitpost 0.09975 0.02085 4.783 3.17e-06 ***
## Job2 -0.13952 0.02805 -4.975 1.32e-06 ***
## Visitpost:Job2 0.02688 0.04250 0.632 0.528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1345 on 219 degrees of freedom
## Multiple R-squared: 0.2717, Adjusted R-squared: 0.2618
## F-statistic: 27.24 on 3 and 219 DF, p-value: 5.207e-15
Experience
result.exp
## Test Visit < 1 1~5 5~10 > 10
## 1 Qsum pre 0.5 (0.2) 0.6 (0.2) 0.6 (0.2) 0.6 (0.2)
## 2 Qsum post 0.7 (0.3) 0.7 (0.2) 0.7 (0.2) 0.7 (0.1)
## 3 IQsum pre 0.3 (0.2) 0.4 (0.2) 0.3 (0.3) 0.3 (0.2)
## 4 IQsum post 0.4 (0.1) 0.4 (0.3) 0.4 (0.3) 0.4 (0.2)
## 5 Scoresum pre 0.5 (0.2) 0.5 (0.1) 0.5 (0.2) 0.5 (0.2)
## 6 Scoresum post 0.6 (0.2) 0.6 (0.1) 0.6 (0.1) 0.6 (0.1)
# interaction test
anova(lm(Score~Visit*Job.Years, subset(dat3, Test=="IQsum")))
## Analysis of Variance Table
##
## Response: Score
## Df Sum Sq Mean Sq F value Pr(>F)
## Visit 1 0.3611 0.36114 5.4572 0.02046 *
## Job.Years 3 0.1554 0.05180 0.7827 0.50482
## Visit:Job.Years 3 0.0149 0.00497 0.0751 0.97333
## Residuals 204 13.5001 0.06618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(lm(Score~Visit*Job.Years, subset(dat3, Test=="Qsum")))
## Analysis of Variance Table
##
## Response: Score
## Df Sum Sq Mean Sq F value Pr(>F)
## Visit 1 0.9511 0.95113 32.4516 4.227e-08 ***
## Job.Years 3 0.0375 0.01251 0.4270 0.7339
## Visit:Job.Years 3 0.0109 0.00364 0.1242 0.9457
## Residuals 204 5.9791 0.02931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(lm(Score~Visit*Job.Years, subset(dat3, Test=="Scoresum")))
## Analysis of Variance Table
##
## Response: Score
## Df Sum Sq Mean Sq F value Pr(>F)
## Visit 1 0.7540 0.75397 34.3780 1.805e-08 ***
## Job.Years 3 0.0540 0.01800 0.8209 0.4837
## Visit:Job.Years 3 0.0028 0.00092 0.0419 0.9886
## Residuals 204 4.4741 0.02193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1