setwd("~/Dropbox/Research/Johannes/")
p<-read.csv ("Surviving automation pilot .csv", header=T, sep=",")
p$exp<-(p$e1+p$e2+p$e3+p$e4)/4
p$ag<-(p$a1+p$a2+p$a3+p$a4)/4
p$inst<-(p$instr1 + p$instr2 + p$instr3)/3
p$lonely<-(p$Q22)
p$like_being_w_people<-p$Q41
p$tech_affinity<-(p$Q38+ p$Q40)/2
p$cond[p$human==1]<-"human"
p$cond[p$robo==1]<-"robot"
p$cond[p$ss==1]<-"self serve"
table(p$cond)
##
## human robot self serve
## 68 60 54
summary(lm(exp~ cond, p))
##
## Call:
## lm(formula = exp ~ cond, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.90278 -0.44485 0.05515 0.48333 0.84722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.19485 0.07877 53.257 < 2e-16 ***
## condrobot 0.32181 0.11505 2.797 0.00572 **
## condself serve -0.04208 0.11839 -0.355 0.72272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6495 on 179 degrees of freedom
## Multiple R-squared: 0.05876, Adjusted R-squared: 0.04825
## F-statistic: 5.588 on 2 and 179 DF, p-value: 0.004426
summary(lm(ag ~ cond, p))
##
## Call:
## lm(formula = ag ~ cond, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.98897 -0.48897 0.01103 0.56526 1.08333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.98897 0.08538 46.718 <2e-16 ***
## condrobot 0.25686 0.12471 2.060 0.0409 *
## condself serve -0.07230 0.12834 -0.563 0.5739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7041 on 179 degrees of freedom
## Multiple R-squared: 0.03808, Adjusted R-squared: 0.02734
## F-statistic: 3.543 on 2 and 179 DF, p-value: 0.03096
summary(lm(inst ~ cond, p))
##
## Call:
## lm(formula = inst ~ cond, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.68137 -0.51852 0.03333 0.48148 1.65196
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.34804 0.08479 39.486 <2e-16 ***
## condrobot 0.28529 0.12385 2.304 0.0224 *
## condself serve 0.17048 0.12745 1.338 0.1827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6992 on 179 degrees of freedom
## Multiple R-squared: 0.02935, Adjusted R-squared: 0.0185
## F-statistic: 2.706 on 2 and 179 DF, p-value: 0.06955
summary(lm(donate ~ cond, p))
##
## Call:
## lm(formula = donate ~ cond, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.07407 -0.89706 -0.07407 0.10294 3.10294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.89706 0.12717 93.556 <2e-16 ***
## condrobot 0.05294 0.18574 0.285 0.776
## condself serve 0.17702 0.19114 0.926 0.356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.049 on 179 degrees of freedom
## Multiple R-squared: 0.004899, Adjusted R-squared: -0.006219
## F-statistic: 0.4406 on 2 and 179 DF, p-value: 0.6443
nohuman<-subset(p, cond!="human")
summary(lm(exp~cond, nohuman))
##
## Call:
## lm(formula = exp ~ cond, data = nohuman)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.90278 -0.40278 0.09722 0.48333 0.84722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.51667 0.08049 56.115 < 2e-16 ***
## condself serve -0.36389 0.11695 -3.112 0.00236 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6235 on 112 degrees of freedom
## Multiple R-squared: 0.07957, Adjusted R-squared: 0.07135
## F-statistic: 9.682 on 1 and 112 DF, p-value: 0.002362
summary(lm(ag~cond, nohuman))
##
## Call:
## lm(formula = ag ~ cond, data = nohuman)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.91667 -0.47604 0.08333 0.58333 1.08333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.24583 0.09315 45.580 <2e-16 ***
## condself serve -0.32917 0.13535 -2.432 0.0166 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7216 on 112 degrees of freedom
## Multiple R-squared: 0.05016, Adjusted R-squared: 0.04168
## F-statistic: 5.915 on 1 and 112 DF, p-value: 0.0166
summary(lm(inst~cond, nohuman))
##
## Call:
## lm(formula = inst ~ cond, data = nohuman)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.63333 -0.51852 0.03333 0.48148 1.48148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.63333 0.08652 41.994 <2e-16 ***
## condself serve -0.11481 0.12571 -0.913 0.363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6702 on 112 degrees of freedom
## Multiple R-squared: 0.007393, Adjusted R-squared: -0.00147
## F-statistic: 0.8342 on 1 and 112 DF, p-value: 0.363
summary(lm(donate ~ cond, nohuman))
##
## Call:
## lm(formula = donate ~ cond, data = nohuman)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.07407 -0.95000 -0.07407 0.05000 3.05000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.9500 0.1350 88.489 <2e-16 ***
## condself serve 0.1241 0.1962 0.632 0.528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.046 on 112 degrees of freedom
## Multiple R-squared: 0.003557, Adjusted R-squared: -0.005339
## F-statistic: 0.3998 on 1 and 112 DF, p-value: 0.5285
norobo<-subset(p, cond!="robot")
summary(lm(exp~cond, norobo))
##
## Call:
## lm(formula = exp ~ cond, data = norobo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.90278 -0.44485 0.05515 0.59722 0.84722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.19485 0.08314 50.454 <2e-16 ***
## condself serve -0.04208 0.12497 -0.337 0.737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6856 on 120 degrees of freedom
## Multiple R-squared: 0.0009437, Adjusted R-squared: -0.007382
## F-statistic: 0.1134 on 1 and 120 DF, p-value: 0.7369
summary(lm(ag~cond, norobo))
##
## Call:
## lm(formula = ag ~ cond, data = norobo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.98897 -0.47089 0.01103 0.51103 1.08333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9890 0.0872 45.743 <2e-16 ***
## condself serve -0.0723 0.1311 -0.552 0.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7191 on 120 degrees of freedom
## Multiple R-squared: 0.002529, Adjusted R-squared: -0.005783
## F-statistic: 0.3043 on 1 and 120 DF, p-value: 0.5822
summary(lm(inst~cond, norobo))
##
## Call:
## lm(formula = inst ~ cond, data = norobo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.68137 -0.51852 0.06672 0.48148 1.65196
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3480 0.0864 38.751 <2e-16 ***
## condself serve 0.1705 0.1299 1.313 0.192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7125 on 120 degrees of freedom
## Multiple R-squared: 0.01416, Adjusted R-squared: 0.005942
## F-statistic: 1.723 on 1 and 120 DF, p-value: 0.1918
summary(lm(donate ~ cond, norobo))
##
## Call:
## lm(formula = donate ~ cond, data = norobo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.07407 -0.89706 -0.07407 0.10294 3.10294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.8971 0.1319 90.192 <2e-16 ***
## condself serve 0.1770 0.1983 0.893 0.374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.088 on 120 degrees of freedom
## Multiple R-squared: 0.006599, Adjusted R-squared: -0.00168
## F-statistic: 0.7971 on 1 and 120 DF, p-value: 0.3738
#moderators
summary(lm(exp ~ cond * tech_affinity, p))
##
## Call:
## lm(formula = exp ~ cond * tech_affinity, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8444 -0.4328 0.0593 0.5485 0.9056
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.35222 0.32306 13.472 <2e-16 ***
## condrobot 0.59701 0.44036 1.356 0.177
## condself serve 0.08847 0.54096 0.164 0.870
## tech_affinity -0.04038 0.08040 -0.502 0.616
## condrobot:tech_affinity -0.07296 0.11013 -0.662 0.509
## condself serve:tech_affinity -0.02887 0.13002 -0.222 0.825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6496 on 176 degrees of freedom
## Multiple R-squared: 0.07443, Adjusted R-squared: 0.04814
## F-statistic: 2.831 on 5 and 176 DF, p-value: 0.01743
summary(lm(exp ~ cond * lonely, p))
##
## Call:
## lm(formula = exp ~ cond * lonely, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0721 -0.4648 0.0441 0.5188 1.0511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.25064 0.20510 20.724 <2e-16 ***
## condrobot 0.17066 0.33383 0.511 0.610
## condself serve -0.39497 0.31423 -1.257 0.210
## lonely -0.01789 0.06072 -0.295 0.769
## condrobot:lonely 0.04785 0.09917 0.483 0.630
## condself serve:lonely 0.11117 0.09219 1.206 0.229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6513 on 176 degrees of freedom
## Multiple R-squared: 0.06955, Adjusted R-squared: 0.04312
## F-statistic: 2.631 on 5 and 176 DF, p-value: 0.02538
summary(lm(exp ~ cond * like_being_w_people, p))
##
## Call:
## lm(formula = exp ~ cond * like_being_w_people, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.90059 -0.44304 0.05696 0.55696 0.96769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.31619 0.37840 11.406 <2e-16
## condrobot 0.66709 0.52130 1.280 0.202
## condself serve 0.30751 0.56031 0.549 0.584
## like_being_w_people -0.03079 0.09391 -0.328 0.743
## condrobot:like_being_w_people -0.08443 0.12740 -0.663 0.508
## condself serve:like_being_w_people -0.08749 0.13820 -0.633 0.528
##
## (Intercept) ***
## condrobot
## condself serve
## like_being_w_people
## condrobot:like_being_w_people
## condself serve:like_being_w_people
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6491 on 176 degrees of freedom
## Multiple R-squared: 0.07588, Adjusted R-squared: 0.04963
## F-statistic: 2.89 on 5 and 176 DF, p-value: 0.01557
summary(lm(ag ~ cond * tech_affinity, p))
##
## Call:
## lm(formula = ag ~ cond * tech_affinity, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.88629 -0.42238 0.02089 0.56651 1.21148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.351768 0.348290 12.495 <2e-16 ***
## condrobot 0.242296 0.474755 0.510 0.6104
## condself serve -1.163803 0.583212 -1.996 0.0475 *
## tech_affinity -0.093095 0.086675 -1.074 0.2843
## condrobot:tech_affinity 0.001856 0.118730 0.016 0.9875
## condself serve:tech_affinity 0.268373 0.140171 1.915 0.0572 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7003 on 176 degrees of freedom
## Multiple R-squared: 0.0644, Adjusted R-squared: 0.03782
## F-statistic: 2.423 on 5 and 176 DF, p-value: 0.03743
summary(lm(ag ~ cond * lonely, p))
##
## Call:
## lm(formula = ag ~ cond * lonely, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.94286 -0.42497 0.05714 0.54968 1.15357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.810455 0.222965 17.090 <2e-16 ***
## condrobot 0.525556 0.362902 1.448 0.149
## condself serve 0.003831 0.341592 0.011 0.991
## lonely 0.057260 0.066002 0.868 0.387
## condrobot:lonely -0.085588 0.107807 -0.794 0.428
## condself serve:lonely -0.025117 0.100213 -0.251 0.802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.708 on 176 degrees of freedom
## Multiple R-squared: 0.04376, Adjusted R-squared: 0.01659
## F-statistic: 1.611 on 5 and 176 DF, p-value: 0.1595
summary(lm(ag ~ cond * like_being_w_people, p))
##
## Call:
## lm(formula = ag ~ cond * like_being_w_people, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.98707 -0.43748 0.01293 0.51293 1.30897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.11638 0.41138 10.006 <2e-16
## condrobot 0.52038 0.56673 0.918 0.360
## condself serve -0.65309 0.60914 -1.072 0.285
## like_being_w_people -0.03233 0.10210 -0.317 0.752
## condrobot:like_being_w_people -0.06420 0.13850 -0.464 0.644
## condself serve:like_being_w_people 0.14620 0.15024 0.973 0.332
##
## (Intercept) ***
## condrobot
## condself serve
## like_being_w_people
## condrobot:like_being_w_people
## condself serve:like_being_w_people
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7056 on 176 degrees of freedom
## Multiple R-squared: 0.05013, Adjusted R-squared: 0.02314
## F-statistic: 1.858 on 5 and 176 DF, p-value: 0.1041
summary(lm(inst ~ cond * tech_affinity, p))
##
## Call:
## lm(formula = inst ~ cond * tech_affinity, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6676 -0.4586 0.0333 0.3993 1.7995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.86923 0.34138 11.334 < 2e-16 ***
## condrobot -0.23419 0.46533 -0.503 0.61541
## condself serve -1.56045 0.57164 -2.730 0.00698 **
## tech_affinity -0.13374 0.08496 -1.574 0.11723
## condrobot:tech_affinity 0.13329 0.11637 1.145 0.25361
## condself serve:tech_affinity 0.42472 0.13739 3.091 0.00232 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6864 on 176 degrees of freedom
## Multiple R-squared: 0.08025, Adjusted R-squared: 0.05412
## F-statistic: 3.071 on 5 and 176 DF, p-value: 0.01104
summary(lm(inst ~ cond * lonely, p))
##
## Call:
## lm(formula = inst ~ cond * lonely, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.72449 -0.40490 0.03257 0.49039 1.66820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.324131 0.220528 15.073 <2e-16 ***
## condrobot -0.046125 0.358936 -0.129 0.898
## condself serve 0.393796 0.337859 1.166 0.245
## lonely 0.007669 0.065281 0.117 0.907
## condrobot:lonely 0.103952 0.106629 0.975 0.331
## condself serve:lonely -0.070274 0.099118 -0.709 0.479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7002 on 176 degrees of freedom
## Multiple R-squared: 0.04279, Adjusted R-squared: 0.01559
## F-statistic: 1.573 on 5 and 176 DF, p-value: 0.1699
summary(lm(inst ~ cond * like_being_w_people, p))
##
## Call:
## lm(formula = inst ~ cond * like_being_w_people, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.70484 -0.42539 0.03841 0.44782 1.71716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.26067 0.40580 8.035 1.3e-13
## condrobot -0.03816 0.55905 -0.068 0.946
## condself serve -0.53737 0.60087 -0.894 0.372
## like_being_w_people 0.02217 0.10071 0.220 0.826
## condrobot:like_being_w_people 0.07927 0.13662 0.580 0.563
## condself serve:like_being_w_people 0.17756 0.14821 1.198 0.232
##
## (Intercept) ***
## condrobot
## condself serve
## like_being_w_people
## condrobot:like_being_w_people
## condself serve:like_being_w_people
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.696 on 176 degrees of freedom
## Multiple R-squared: 0.05423, Adjusted R-squared: 0.02736
## F-statistic: 2.018 on 5 and 176 DF, p-value: 0.07825
summary(lm(donate ~ cond * tech_affinity, p))
##
## Call:
## lm(formula = donate ~ cond * tech_affinity, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1026 -0.9102 -0.0433 0.1041 3.1375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.850529 0.525846 22.536 <2e-16 ***
## condrobot 0.025170 0.716781 0.035 0.972
## condself serve 0.082567 0.880530 0.094 0.925
## tech_affinity 0.011940 0.130862 0.091 0.927
## condrobot:tech_affinity 0.007528 0.179258 0.042 0.967
## condself serve:tech_affinity 0.021970 0.211630 0.104 0.917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.057 on 176 degrees of freedom
## Multiple R-squared: 0.005324, Adjusted R-squared: -0.02293
## F-statistic: 0.1884 on 5 and 176 DF, p-value: 0.9667
summary(lm(donate ~ cond * lonely, p))
##
## Call:
## lm(formula = donate ~ cond * lonely, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4294 -0.8420 -0.0606 0.3538 3.3390
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.54959 0.32844 35.165 <2e-16 ***
## condrobot 0.69805 0.53458 1.306 0.193
## condself serve -0.09917 0.50318 -0.197 0.844
## lonely 0.11145 0.09723 1.146 0.253
## condrobot:lonely -0.20495 0.15881 -1.291 0.199
## condself serve:lonely 0.08435 0.14762 0.571 0.568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.043 on 176 degrees of freedom
## Multiple R-squared: 0.03226, Adjusted R-squared: 0.004765
## F-statistic: 1.173 on 5 and 176 DF, p-value: 0.3241
summary(lm(donate ~ cond * like_being_w_people, p))
##
## Call:
## lm(formula = donate ~ cond * like_being_w_people, data = p)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1753 -0.8916 -0.0759 0.1915 3.2007
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.26108 0.61500 19.937 <2e-16
## condrobot -0.53549 0.84725 -0.632 0.528
## condself serve -0.58282 0.91064 -0.640 0.523
## like_being_w_people -0.09236 0.15263 -0.605 0.546
## condrobot:like_being_w_people 0.14777 0.20705 0.714 0.476
## condself serve:like_being_w_people 0.19178 0.22461 0.854 0.394
##
## (Intercept) ***
## condrobot
## condself serve
## like_being_w_people
## condrobot:like_being_w_people
## condself serve:like_being_w_people
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.055 on 176 degrees of freedom
## Multiple R-squared: 0.009889, Adjusted R-squared: -0.01824
## F-statistic: 0.3516 on 5 and 176 DF, p-value: 0.8807
library(interactions)
## Warning: package 'interactions' was built under R version 3.4.4
#breaking down tech affinity interactions, finding JN points
p$selfserve[p$human==1]<-0
p$selfserve[p$ss==1]<-1
norobo<-subset(p, cond!="robot")
#instrumentality DV
m1 <- lm(inst ~ selfserve * tech_affinity, data = norobo)
sim_slopes(m1, pred = selfserve, modx = tech_affinity, jnplot = TRUE)
## JOHNSON-NEYMAN INTERVAL
##
## When tech_affinity is OUTSIDE the interval [2.52, 4.28], the slope of
## selfserve is p < .05.
##
## Note: The range of observed values of tech_affinity is [1.00, 5.00]
## SIMPLE SLOPES ANALYSIS
##
## Slope of selfserve when tech_affinity = 4.96 (+ 1 SD):
##
## Est. S.E. t val. p
## ----- ----- ------- -----
## 0.54 0.18 3.06 0.00
##
## Slope of selfserve when tech_affinity = 4.01 (Mean):
##
## Est. S.E. t val. p
## ----- ----- ------- -----
## 0.14 0.13 1.13 0.26
##
## Slope of selfserve when tech_affinity = 3.07 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ----- ------- -----
## -0.26 0.19 -1.38 0.17
#agency DV - interaction isn't quite significant so no JN apparently
m2 <- lm(ag ~ selfserve * tech_affinity, data = norobo)
sim_slopes(m2, pred = selfserve, modx = tech_affinity, jnplot = TRUE)
## JOHNSON-NEYMAN INTERVAL
##
## The Johnson-Neyman interval could not be found. Is the p value for
## your interaction term below the specified alpha?
## SIMPLE SLOPES ANALYSIS
##
## Slope of selfserve when tech_affinity = 4.96 (+ 1 SD):
##
## Est. S.E. t val. p
## ----- ----- ------- -----
## 0.17 0.18 0.90 0.37
##
## Slope of selfserve when tech_affinity = 4.01 (Mean):
##
## Est. S.E. t val. p
## ------ ----- ------- -----
## -0.09 0.13 -0.66 0.51
##
## Slope of selfserve when tech_affinity = 3.07 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ----- ------- -----
## -0.34 0.19 -1.76 0.08
#correlations
cor.test(p$exp, p$ag)
##
## Pearson's product-moment correlation
##
## data: p$exp and p$ag
## t = 13.22, df = 180, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6196902 0.7688427
## sample estimates:
## cor
## 0.7018801
cor.test(p$exp, p$inst)
##
## Pearson's product-moment correlation
##
## data: p$exp and p$inst
## t = 2.3848, df = 180, p-value = 0.01813
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03032844 0.31251203
## sample estimates:
## cor
## 0.1750119