Creating DVs and condition variables
#trust
m$trust<-(m$ct1+(8-m$ct2)+m$ct3+(8-m$ct4)+m$overall.trust + m$overall.effective)/6
alpha(m[,c(18:23)], check.keys = TRUE)
## Warning in alpha(m[, c(18:23)], check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
##
## Reliability analysis
## Call: alpha(x = m[, c(18:23)], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.86 0.85 0.5 6 0.01 3.3 1.1 0.53
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## ct1 0.83 0.84 0.82 0.50 5.1 0.0120 0.0248
## ct2- 0.83 0.83 0.82 0.50 5.0 0.0120 0.0281
## ct3- 0.88 0.88 0.86 0.59 7.2 0.0088 0.0053
## ct4- 0.82 0.82 0.80 0.48 4.6 0.0129 0.0229
## overall.trust 0.81 0.81 0.79 0.46 4.3 0.0137 0.0203
## overall.effective 0.81 0.81 0.79 0.46 4.3 0.0136 0.0207
## med.r
## ct1 0.55
## ct2- 0.56
## ct3- 0.60
## ct4- 0.49
## overall.trust 0.49
## overall.effective 0.50
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## ct1 500 0.76 0.75 0.69 0.63 3.0 1.5
## ct2- 500 0.76 0.76 0.69 0.64 3.4 1.5
## ct3- 500 0.57 0.56 0.41 0.39 3.7 1.5
## ct4- 500 0.80 0.81 0.77 0.71 3.1 1.3
## overall.trust 500 0.85 0.85 0.83 0.77 3.3 1.4
## overall.effective 500 0.84 0.84 0.82 0.76 3.4 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## ct1 0.15 0.27 0.27 0.10 0.14 0.06 0.02 0
## ct2 0.01 0.08 0.15 0.19 0.28 0.20 0.09 0
## ct3 0.03 0.07 0.23 0.15 0.28 0.18 0.05 0
## ct4 0.01 0.05 0.10 0.17 0.36 0.22 0.10 0
## overall.trust 0.10 0.22 0.31 0.15 0.17 0.04 0.02 0
## overall.effective 0.11 0.18 0.26 0.20 0.19 0.05 0.01 0
mean(m$trust)
## [1] 3.424333
#use
m$use<-(m$used_committee + m$purchase + m$use_personally)/3
alpha(m[,c(24:26)], check.keys = TRUE)
##
## Reliability analysis
## Call: alpha(x = m[, c(24:26)], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.93 0.86 18 0.0041 3 1.6 0.85
##
## lower alpha upper 95% confidence boundaries
## 0.94 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## used_committee 0.91 0.91 0.83 0.83 9.6 0.0085 NA 0.83
## purchase 0.92 0.92 0.85 0.85 11.0 0.0074 NA 0.85
## use_personally 0.95 0.95 0.90 0.90 18.9 0.0045 NA 0.90
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## used_committee 500 0.96 0.96 0.94 0.92 2.9 1.6
## purchase 500 0.96 0.96 0.93 0.90 2.8 1.6
## use_personally 500 0.94 0.94 0.88 0.86 3.2 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## used_committee 0.23 0.25 0.18 0.10 0.17 0.05 0.01 0
## purchase 0.27 0.25 0.16 0.13 0.12 0.06 0.01 0
## use_personally 0.20 0.21 0.18 0.12 0.19 0.08 0.02 0
mean(m$use)
## [1] 2.976
#conditions
m$sci[m$subjSCI==1 | m$ObjSCI==1]<-"Science"
m$sci[m$subjREG==1 | m$ObjREG==1]<-"Control"
m$SO[m$subjSCI==1 | m$subjREG==1]<-"Subjective"
m$SO[m$ObjSCI==1 | m$ObjREG==1]<-"Objective"
table(m$sci, m$SO)
##
## Objective Subjective
## Control 127 118
## Science 128 127
ANOVAs, regressions
#excluding people with no hiring experience
table(m$hiring_experience)
##
## 1 2 3
## 227 236 37
m<-subset(m, hiring_experience!=3)
mod <- lm(use ~ sci*SO + hiring_experience_2, data=m, contrasts=list(sci=contr.sum, SO=contr.sum))
Anova(mod, type="III")
## Anova Table (Type III tests)
##
## Response: use
## Sum Sq Df F value Pr(>F)
## (Intercept) 44.22 1 18.1264 2.509e-05 ***
## sci 2.07 1 0.8479 0.35763
## SO 0.48 1 0.1953 0.65874
## hiring_experience_2 8.86 1 3.6296 0.05739 .
## sci:SO 0.02 1 0.0089 0.92483
## Residuals 1117.43 458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(lm(use~sci+SO , m))
##
## Call:
## lm(formula = use ~ sci + SO, data = m)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0656 -1.0656 -0.2006 1.3316 4.0689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9311 0.1252 23.418 <2e-16 ***
## sciScience 0.1345 0.1454 0.925 0.356
## SOSubjective -0.0638 0.1455 -0.439 0.661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.565 on 460 degrees of freedom
## Multiple R-squared: 0.002267, Adjusted R-squared: -0.002071
## F-statistic: 0.5227 on 2 and 460 DF, p-value: 0.5933
mod <- lm(trust ~ sci*SO + hiring_experience_2, data=m, contrasts=list(sci=contr.sum, SO=contr.sum))
Anova(mod, type="III")
## Anova Table (Type III tests)
##
## Response: trust
## Sum Sq Df F value Pr(>F)
## (Intercept) 33.17 1 40.6267 4.501e-10 ***
## sci 1.32 1 1.6228 0.20335
## SO 0.88 1 1.0763 0.30007
## hiring_experience_2 2.31 1 2.8338 0.09298 .
## sci:SO 0.05 1 0.0636 0.80106
## Residuals 373.93 458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(lm(trust~sci+SO, m))
##
## Call:
## lm(formula = trust ~ sci + SO, data = m)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5160 -0.5458 0.0112 0.5708 3.5910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.40897 0.07235 47.119 <2e-16 ***
## sciScience 0.10707 0.08407 1.273 0.203
## SOSubjective -0.08687 0.08410 -1.033 0.302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9045 on 460 degrees of freedom
## Multiple R-squared: 0.005798, Adjusted R-squared: 0.001476
## F-statistic: 1.341 on 2 and 460 DF, p-value: 0.2625
APPLICANTS: Creating DVs and condition variables
#trust
a$trust<-(a$ct1+(8-a$ct2)+a$ct3+(8-a$ct4)+a$overall.trust + a$overall.effective)/6
alpha(a[,c(18:23)], check.keys = TRUE)
## Warning in alpha(a[, c(18:23)], check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
##
## Reliability analysis
## Call: alpha(x = a[, c(18:23)], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.85 0.84 0.48 5.6 0.011 3.3 1.1 0.51
##
## lower alpha upper 95% confidence boundaries
## 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## ct1 0.82 0.82 0.81 0.48 4.6 0.0133 0.032
## ct2- 0.82 0.82 0.82 0.48 4.7 0.0130 0.038
## ct3- 0.87 0.88 0.86 0.59 7.1 0.0088 0.007
## ct4- 0.81 0.81 0.80 0.46 4.3 0.0137 0.032
## overall.trust 0.79 0.79 0.77 0.43 3.8 0.0152 0.023
## overall.effective 0.79 0.80 0.78 0.44 4.0 0.0149 0.025
## med.r
## ct1 0.53
## ct2- 0.56
## ct3- 0.58
## ct4- 0.49
## overall.trust 0.49
## overall.effective 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## ct1 502 0.76 0.76 0.69 0.63 3.1 1.5
## ct2- 502 0.75 0.75 0.67 0.62 3.3 1.4
## ct3- 502 0.54 0.52 0.36 0.34 3.6 1.5
## ct4- 502 0.78 0.79 0.74 0.68 3.0 1.2
## overall.trust 502 0.86 0.86 0.86 0.78 3.2 1.4
## overall.effective 502 0.84 0.84 0.83 0.74 3.5 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## ct1 0.13 0.27 0.27 0.14 0.13 0.05 0.01 0
## ct2 0.01 0.07 0.14 0.14 0.30 0.26 0.07 0
## ct3 0.04 0.08 0.18 0.14 0.29 0.21 0.05 0
## ct4 0.00 0.03 0.11 0.12 0.37 0.27 0.09 0
## overall.trust 0.09 0.24 0.31 0.15 0.17 0.05 0.00 0
## overall.effective 0.10 0.18 0.25 0.18 0.21 0.07 0.01 0
#use
a$dv<-(a$like + a$apply + a$happy)/3
alpha(a[,c(24:26)], check.keys = TRUE)
## Warning in alpha(a[, c(24:26)], check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
##
## Reliability analysis
## Call: alpha(x = a[, c(24:26)], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.75 0.61 4.6 0.014 3.2 1.3 0.61
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.82 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## like 0.76 0.76 0.61 0.61 3.1 0.022 NA 0.61
## apply- 0.76 0.76 0.62 0.62 3.2 0.021 NA 0.62
## happy 0.74 0.74 0.59 0.59 2.9 0.023 NA 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## like 502 0.85 0.86 0.74 0.67 2.6 1.5
## apply- 502 0.86 0.85 0.74 0.67 3.2 1.6
## happy 502 0.86 0.86 0.76 0.69 3.9 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## like 0.30 0.26 0.20 0.12 0.07 0.05 0.00 0
## apply 0.03 0.09 0.10 0.20 0.20 0.24 0.14 0
## happy 0.09 0.11 0.16 0.27 0.22 0.12 0.02 0
#conditions
a$sci[a$subjSCI==1 | a$ObjSCI==1]<-"Science"
a$sci[a$subjREG==1 | a$ObjREG==1]<-"Control"
a$SO[a$subjSCI==1 | a$subjREG==1]<-"Subjective"
a$SO[a$ObjSCI==1 | a$ObjREG==1]<-"Objective"
table(a$sci, a$SO)
##
## Objective Subjective
## Control 122 147
## Science 126 107
APPLICANTS - ANOVAs, regressions
#excluding non-students
table(a$student)
##
## 1 2
## 491 11
a<-subset(a, student==1)
mod <- lm(dv ~ sci*SO, data=a, contrasts=list(sci=contr.sum, SO=contr.sum))
Anova(mod, type="III")
## Anova Table (Type III tests)
##
## Response: dv
## Sum Sq Df F value Pr(>F)
## (Intercept) 6762.8 1 14688.6648 <2e-16 ***
## sci 0.0 1 0.0981 0.7543
## SO 0.1 1 0.1327 0.7158
## sci:SO 0.6 1 1.2960 0.2555
## Residuals 224.2 487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(lm(dv~sci+SO , a))
##
## Call:
## lm(formula = dv ~ sci + SO, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.73912 -0.40578 -0.07017 0.27982 2.59422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.72018 0.05360 69.406 <2e-16 ***
## sciScience 0.01893 0.06176 0.307 0.759
## SOSubjective 0.01665 0.06154 0.271 0.787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6787 on 488 degrees of freedom
## Multiple R-squared: 0.000313, Adjusted R-squared: -0.003784
## F-statistic: 0.0764 on 2 and 488 DF, p-value: 0.9265
mod <- lm(trust ~ sci*SO, data=a, contrasts=list(sci=contr.sum, SO=contr.sum))
Anova(mod, type="III")
## Anova Table (Type III tests)
##
## Response: trust
## Sum Sq Df F value Pr(>F)
## (Intercept) 5623.6 1 7015.9341 <2e-16 ***
## sci 0.0 1 0.0229 0.8798
## SO 0.0 1 0.0164 0.8980
## sci:SO 0.4 1 0.5572 0.4557
## Residuals 390.4 487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(lm(trust~sci+SO, a))
##
## Call:
## lm(formula = trust ~ sci + SO, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.41785 -0.57184 -0.06644 0.58755 2.42816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.412452 0.070670 48.287 <2e-16 ***
## sciScience -0.012676 0.081425 -0.156 0.876
## SOSubjective 0.005396 0.081144 0.066 0.947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8949 on 488 degrees of freedom
## Multiple R-squared: 6.333e-05, Adjusted R-squared: -0.004035
## F-statistic: 0.01545 on 2 and 488 DF, p-value: 0.9847
Comparing managers and applicants
#overall trust
t.test(a$trust, m$trust)
##
## Welch Two Sample t-test
##
## data: a$trust and m$trust
## t = -0.19015, df = 947.06, p-value = 0.8492
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1254096 0.1032541
## sample estimates:
## mean of x mean of y
## 3.409369 3.420446
#just the science / subjective conditions
a2<-subset(a, sci=="Science" & SO=="Subjective")
m2<-subset(m, sci=="Science" & SO=="Subjective")
t.test(a2$trust, m2$trust)
##
## Welch Two Sample t-test
##
## data: a2$trust and m2$trust
## t = -0.02661, df = 210.78, p-value = 0.9788
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2453579 0.2388220
## sample estimates:
## mean of x mean of y
## 3.441176 3.444444
#main effects of science
summary(lm(trust ~ sci, m))
##
## Call:
## lm(formula = trust ~ sci, data = m)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4735 -0.5333 -0.0333 0.5265 3.6333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.36667 0.05965 56.444 <2e-16 ***
## sciScience 0.10687 0.08408 1.271 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9046 on 461 degrees of freedom
## Multiple R-squared: 0.003492, Adjusted R-squared: 0.00133
## F-statistic: 1.615 on 1 and 461 DF, p-value: 0.2044
summary(lm(trust ~ sci, a))
##
## Call:
## lm(formula = trust ~ sci, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.41541 -0.56889 -0.06889 0.58459 2.43111
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.41541 0.05481 62.310 <2e-16 ***
## sciScience -0.01319 0.08097 -0.163 0.871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.894 on 489 degrees of freedom
## Multiple R-squared: 5.427e-05, Adjusted R-squared: -0.001991
## F-statistic: 0.02654 on 1 and 489 DF, p-value: 0.8707
#just the subjective conditions
a3<-subset(a, SO=="Subjective")
m3<-subset(m, SO=="Subjective")
summary(lm(trust ~ sci, m3))
##
## Call:
## lm(formula = trust ~ sci, data = m3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.44444 -0.61111 0.05556 0.52679 2.55556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.30655 0.08064 41.006 <2e-16 ***
## sciScience 0.13790 0.11353 1.215 0.226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8534 on 224 degrees of freedom
## Multiple R-squared: 0.006543, Adjusted R-squared: 0.002108
## F-statistic: 1.475 on 1 and 224 DF, p-value: 0.2258
summary(lm(trust ~ sci, a3))
##
## Call:
## lm(formula = trust ~ sci, data = a3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.44118 -0.60784 -0.05936 0.60731 2.39216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.39269 0.07533 45.039 <2e-16 ***
## sciScience 0.04848 0.11746 0.413 0.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9102 on 246 degrees of freedom
## Multiple R-squared: 0.0006921, Adjusted R-squared: -0.00337
## F-statistic: 0.1704 on 1 and 246 DF, p-value: 0.6801