analyses
nickmodel1 <- lm(fear ~ cond*DPS, data = nickdiss)
summary(nickmodel1)
##
## Call:
## lm(formula = fear ~ cond * DPS, data = nickdiss)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1788 -1.4237 -0.1334 1.0039 4.6787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.40887 0.66466 2.120 0.034472 *
## cond2 -2.15359 0.99092 -2.173 0.030175 *
## cond3 -1.36162 0.97699 -1.394 0.163966
## cond4 -3.53475 1.00418 -3.520 0.000467 ***
## DPS 0.08457 0.03670 2.305 0.021555 *
## cond2:DPS 0.12617 0.05618 2.246 0.025109 *
## cond3:DPS 0.07786 0.05538 1.406 0.160320
## cond4:DPS 0.20761 0.05727 3.625 0.000315 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.675 on 560 degrees of freedom
## Multiple R-squared: 0.1383, Adjusted R-squared: 0.1275
## F-statistic: 12.83 on 7 and 560 DF, p-value: 2.43e-15
sim_slopes(nickmodel1, pred = DPS, modx = cond, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of DPS when cond = 4:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.29 0.04 6.64 0.00
##
## Slope of DPS when cond = 3:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.16 0.04 3.92 0.00
##
## Slope of DPS when cond = 2:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.21 0.04 4.95 0.00
##
## Slope of DPS when cond = 1:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.08 0.04 2.30 0.02
nickmodel2 <- lm(fear ~ No_vs_P*DPS, data = nickdiss)
summary(nickmodel2)
##
## Call:
## lm(formula = fear ~ No_vs_P * DPS, data = nickdiss)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5797 -1.3872 -0.1679 1.0514 4.8321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.40887 0.66506 2.118 0.03458 *
## No_vs_P2 -2.31050 0.78873 -2.929 0.00353 **
## DPS 0.08457 0.03672 2.303 0.02163 *
## No_vs_P2:DPS 0.13468 0.04421 3.046 0.00242 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.676 on 564 degrees of freedom
## Multiple R-squared: 0.131, Adjusted R-squared: 0.1264
## F-statistic: 28.35 on 3 and 564 DF, p-value: < 2.2e-16
sim_slopes(nickmodel2, pred = DPS, modx = No_vs_P, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of DPS when No_vs_P = 2:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.22 0.02 8.91 0.00
##
## Slope of DPS when No_vs_P = 1:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.08 0.04 2.30 0.02
nickmodel3 <- lm(disgust ~ cond*DPS, data = nickdiss)
summary(nickmodel3)
##
## Call:
## lm(formula = disgust ~ cond * DPS, data = nickdiss)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9313 -1.6687 0.1655 1.3313 4.1385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.77492 0.73123 2.427 0.0155 *
## cond2 -0.33052 1.09017 -0.303 0.7619
## cond3 -0.44745 1.07484 -0.416 0.6774
## cond4 -1.38737 1.10476 -1.256 0.2097
## DPS 0.10521 0.04037 2.606 0.0094 **
## cond2:DPS 0.01288 0.06181 0.208 0.8350
## cond3:DPS 0.04067 0.06093 0.667 0.5047
## cond4:DPS 0.11970 0.06301 1.900 0.0580 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.843 on 560 degrees of freedom
## Multiple R-squared: 0.09115, Adjusted R-squared: 0.07978
## F-statistic: 8.023 on 7 and 560 DF, p-value: 2.628e-09
sim_slopes(nickmodel3, pred = DPS, modx = cond, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of DPS when cond = 4:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.22 0.05 4.65 0.00
##
## Slope of DPS when cond = 3:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.15 0.05 3.20 0.00
##
## Slope of DPS when cond = 2:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.12 0.05 2.52 0.01
##
## Slope of DPS when cond = 1:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.11 0.04 2.61 0.01