Stimuli check
I compared these responses to the midpoint (4)
Did participants find the posts offensive?
##
## One Sample t-test
##
## data: exp1_5cond2clean$check_1
## t = 14, df = 228, p-value <0.0000000000000002
## alternative hypothesis: true mean is not equal to 4
## 95 percent confidence interval:
## 5.2 5.6
## sample estimates:
## mean of x
## 5.4Did participants find the posts a violation of Webster Springs’ rules?
##
## One Sample t-test
##
## data: exp1_5cond2clean$check_3
## t = 28, df = 228, p-value <0.0000000000000002
## alternative hypothesis: true mean is not equal to 4
## 95 percent confidence interval:
## 6.0 6.3
## sample estimates:
## mean of x
## 6.2##
## 6
## 317## manip_check
## cond_label -2 -1 0 1 2
## 1.HighD 2 3 6 10 38
## 2.RelD-In 3 10 3 46 0
## 3.RelD-Out 4 3 41 15 0
## 4.LowD-In 4 57 2 3 1
## 5.LowD-Out 50 7 3 5 1row <- 1
responseper <- vector("list")
for(row in 1:nrow(mctable)){
thisname <- rownames(mctable)[row]
rowinfo <- mctable[row,]
sumtotal <- sum(mctable[row,])
response <- t(as.data.frame(rowinfo/sumtotal))
rownames(response) <- thisname
responseper[[row]] <- response
}
responseper## [[1]]
## -2 -1 0 1 2
## 1.HighD 0.034 0.051 0.1 0.17 0.64
##
## [[2]]
## -2 -1 0 1 2
## 2.RelD-In 0.048 0.16 0.048 0.74 0
##
## [[3]]
## -2 -1 0 1 2
## 3.RelD-Out 0.063 0.048 0.65 0.24 0
##
## [[4]]
## -2 -1 0 1 2
## 4.LowD-In 0.06 0.85 0.03 0.045 0.015
##
## [[5]]
## -2 -1 0 1 2
## 5.LowD-Out 0.76 0.11 0.045 0.076 0.015All 5 conditions
Main effects and scale information
Post hoc tests
## $Tightness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.989 0.41 1.57 0.00
## 3.RelD-Out-1.HighD 0.846 0.25 1.45 0.00
## 4.LowD-In-1.HighD 1.200 0.64 1.76 0.00
## 5.LowD-Out-1.HighD 1.163 0.59 1.73 0.00
## 3.RelD-Out-2.RelD-In -0.143 -0.71 0.43 0.96
## 4.LowD-In-2.RelD-In 0.211 -0.31 0.73 0.80
## 5.LowD-Out-2.RelD-In 0.174 -0.36 0.71 0.90
## 4.LowD-In-3.RelD-Out 0.354 -0.19 0.90 0.38
## 5.LowD-Out-3.RelD-Out 0.317 -0.24 0.88 0.52
## 5.LowD-Out-4.LowD-In -0.037 -0.55 0.47 1.00
##
##
## $Status
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.026 -0.86 0.91 1.00
## 3.RelD-Out-1.HighD -0.118 -1.03 0.80 1.00
## 4.LowD-In-1.HighD 0.214 -0.64 1.07 0.96
## 5.LowD-Out-1.HighD -0.054 -0.93 0.82 1.00
## 3.RelD-Out-2.RelD-In -0.144 -1.01 0.72 0.99
## 4.LowD-In-2.RelD-In 0.189 -0.61 0.99 0.97
## 5.LowD-Out-2.RelD-In -0.080 -0.90 0.74 1.00
## 4.LowD-In-3.RelD-Out 0.333 -0.50 1.16 0.81
## 5.LowD-Out-3.RelD-Out 0.064 -0.79 0.92 1.00
## 5.LowD-Out-4.LowD-In -0.269 -1.05 0.51 0.88
##
##
## $Domin.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.096 -0.73 0.92 1.00
## 3.RelD-Out-1.HighD -0.180 -1.03 0.67 0.98
## 4.LowD-In-1.HighD 0.014 -0.78 0.80 1.00
## 5.LowD-Out-1.HighD 0.050 -0.76 0.86 1.00
## 3.RelD-Out-2.RelD-In -0.276 -1.08 0.53 0.88
## 4.LowD-In-2.RelD-In -0.082 -0.82 0.66 1.00
## 5.LowD-Out-2.RelD-In -0.046 -0.81 0.72 1.00
## 4.LowD-In-3.RelD-Out 0.194 -0.58 0.97 0.96
## 5.LowD-Out-3.RelD-Out 0.230 -0.56 1.02 0.93
## 5.LowD-Out-4.LowD-In 0.036 -0.69 0.76 1.00
##
##
## $Monitor
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.730 -0.20 1.66 0.20
## 3.RelD-Out-1.HighD 0.717 -0.24 1.67 0.24
## 4.LowD-In-1.HighD 0.498 -0.39 1.39 0.54
## 5.LowD-Out-1.HighD 0.570 -0.34 1.48 0.42
## 3.RelD-Out-2.RelD-In -0.013 -0.92 0.89 1.00
## 4.LowD-In-2.RelD-In -0.232 -1.07 0.60 0.94
## 5.LowD-Out-2.RelD-In -0.160 -1.02 0.70 0.99
## 4.LowD-In-3.RelD-Out -0.220 -1.09 0.65 0.96
## 5.LowD-Out-3.RelD-Out -0.147 -1.04 0.74 0.99
## 5.LowD-Out-4.LowD-In 0.072 -0.74 0.89 1.00
##
##
## $SOP
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.1823 -0.57 0.93 0.96
## 3.RelD-Out-1.HighD 0.0092 -0.77 0.78 1.00
## 4.LowD-In-1.HighD -0.0265 -0.75 0.69 1.00
## 5.LowD-Out-1.HighD -0.1387 -0.88 0.60 0.99
## 3.RelD-Out-2.RelD-In -0.1731 -0.91 0.56 0.97
## 4.LowD-In-2.RelD-In -0.2089 -0.89 0.47 0.91
## 5.LowD-Out-2.RelD-In -0.3210 -1.02 0.37 0.71
## 4.LowD-In-3.RelD-Out -0.0357 -0.74 0.67 1.00
## 5.LowD-Out-3.RelD-Out -0.1479 -0.87 0.57 0.98
## 5.LowD-Out-4.LowD-In -0.1121 -0.77 0.55 0.99
##
##
## $Affil.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.234 -0.54 1.01 0.92
## 3.RelD-Out-1.HighD -0.029 -0.83 0.77 1.00
## 4.LowD-In-1.HighD -0.037 -0.78 0.71 1.00
## 5.LowD-Out-1.HighD 0.220 -0.54 0.98 0.93
## 3.RelD-Out-2.RelD-In -0.263 -1.02 0.49 0.87
## 4.LowD-In-2.RelD-In -0.271 -0.97 0.43 0.82
## 5.LowD-Out-2.RelD-In -0.015 -0.73 0.70 1.00
## 4.LowD-In-3.RelD-Out -0.008 -0.73 0.72 1.00
## 5.LowD-Out-3.RelD-Out 0.249 -0.50 0.99 0.89
## 5.LowD-Out-4.LowD-In 0.257 -0.43 0.94 0.84
##
##
## $Block
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.221 -0.029 0.47 0.11
## 3.RelD-Out-1.HighD 0.126 -0.131 0.38 0.66
## 4.LowD-In-1.HighD 0.101 -0.138 0.34 0.77
## 5.LowD-Out-1.HighD 0.191 -0.054 0.44 0.20
## 3.RelD-Out-2.RelD-In -0.095 -0.339 0.15 0.82
## 4.LowD-In-2.RelD-In -0.120 -0.345 0.11 0.59
## 5.LowD-Out-2.RelD-In -0.030 -0.261 0.20 1.00
## 4.LowD-In-3.RelD-Out -0.025 -0.259 0.21 1.00
## 5.LowD-Out-3.RelD-Out 0.065 -0.175 0.30 0.95
## 5.LowD-Out-4.LowD-In 0.090 -0.130 0.31 0.79
##
##
## $Admin
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.251 -0.36 0.87 0.79
## 3.RelD-Out-1.HighD 0.371 -0.26 1.01 0.49
## 4.LowD-In-1.HighD -0.079 -0.67 0.51 1.00
## 5.LowD-Out-1.HighD 0.080 -0.52 0.68 1.00
## 3.RelD-Out-2.RelD-In 0.120 -0.48 0.72 0.98
## 4.LowD-In-2.RelD-In -0.330 -0.88 0.22 0.47
## 5.LowD-Out-2.RelD-In -0.171 -0.74 0.40 0.92
## 4.LowD-In-3.RelD-Out -0.451 -1.03 0.13 0.20
## 5.LowD-Out-3.RelD-Out -0.291 -0.88 0.30 0.66
## 5.LowD-Out-4.LowD-In 0.160 -0.38 0.70 0.93
##
##
## $`Admin-Block`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.459 -0.59 1.51 0.75
## 3.RelD-Out-1.HighD 0.784 -0.30 1.87 0.27
## 4.LowD-In-1.HighD -0.041 -1.05 0.96 1.00
## 5.LowD-Out-1.HighD 0.439 -0.59 1.47 0.77
## 3.RelD-Out-2.RelD-In 0.325 -0.70 1.35 0.91
## 4.LowD-In-2.RelD-In -0.500 -1.44 0.44 0.59
## 5.LowD-Out-2.RelD-In -0.020 -0.99 0.95 1.00
## 4.LowD-In-3.RelD-Out -0.825 -1.81 0.16 0.15
## 5.LowD-Out-3.RelD-Out -0.345 -1.35 0.66 0.88
## 5.LowD-Out-4.LowD-In 0.480 -0.44 1.40 0.61
##
##
## $`Admin-Kick`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.177 -0.86 1.22 0.99
## 3.RelD-Out-1.HighD 0.451 -0.62 1.52 0.78
## 4.LowD-In-1.HighD -0.381 -1.38 0.62 0.83
## 5.LowD-Out-1.HighD 0.111 -0.91 1.13 1.00
## 3.RelD-Out-2.RelD-In 0.274 -0.74 1.29 0.95
## 4.LowD-In-2.RelD-In -0.558 -1.49 0.38 0.47
## 5.LowD-Out-2.RelD-In -0.066 -1.03 0.89 1.00
## 4.LowD-In-3.RelD-Out -0.832 -1.81 0.14 0.13
## 5.LowD-Out-3.RelD-Out -0.340 -1.34 0.66 0.88
## 5.LowD-Out-4.LowD-In 0.492 -0.42 1.41 0.58
##
##
## $`Admin-Report`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD -0.523 -1.60 0.55 0.67
## 3.RelD-Out-1.HighD 0.316 -0.79 1.43 0.94
## 4.LowD-In-1.HighD -0.248 -1.28 0.78 0.96
## 5.LowD-Out-1.HighD -0.044 -1.10 1.01 1.00
## 3.RelD-Out-2.RelD-In 0.839 -0.21 1.89 0.19
## 4.LowD-In-2.RelD-In 0.275 -0.69 1.24 0.94
## 5.LowD-Out-2.RelD-In 0.479 -0.52 1.47 0.68
## 4.LowD-In-3.RelD-Out -0.564 -1.57 0.44 0.54
## 5.LowD-Out-3.RelD-Out -0.360 -1.39 0.67 0.87
## 5.LowD-Out-4.LowD-In 0.204 -0.74 1.15 0.98
##
##
## $`Admin-Pin`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.560 -0.29 1.408 0.37
## 3.RelD-Out-1.HighD -0.127 -1.00 0.748 0.99
## 4.LowD-In-1.HighD 0.062 -0.75 0.875 1.00
## 5.LowD-Out-1.HighD -0.167 -1.00 0.665 0.98
## 3.RelD-Out-2.RelD-In -0.687 -1.52 0.143 0.16
## 4.LowD-In-2.RelD-In -0.498 -1.26 0.266 0.38
## 5.LowD-Out-2.RelD-In -0.727 -1.51 0.057 0.08
## 4.LowD-In-3.RelD-Out 0.189 -0.61 0.984 0.97
## 5.LowD-Out-3.RelD-Out -0.040 -0.85 0.774 1.00
## 5.LowD-Out-4.LowD-In -0.229 -0.98 0.517 0.92
##
##
## $`Admin-Upvote`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.652 -0.076 1.380 0.10
## 3.RelD-Out-1.HighD 0.400 -0.352 1.152 0.59
## 4.LowD-In-1.HighD 0.125 -0.574 0.824 0.99
## 5.LowD-Out-1.HighD 0.040 -0.675 0.755 1.00
## 3.RelD-Out-2.RelD-In -0.252 -0.965 0.461 0.87
## 4.LowD-In-2.RelD-In -0.527 -1.183 0.129 0.18
## 5.LowD-Out-2.RelD-In -0.612 -1.286 0.062 0.09
## 4.LowD-In-3.RelD-Out -0.275 -0.958 0.408 0.80
## 5.LowD-Out-3.RelD-Out -0.360 -1.060 0.340 0.62
## 5.LowD-Out-4.LowD-In -0.085 -0.727 0.557 1.00
##
##
## $`Admin-Monitor`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.1810 -0.68 1.05 0.98
## 3.RelD-Out-1.HighD 0.4027 -0.49 1.30 0.73
## 4.LowD-In-1.HighD 0.0063 -0.82 0.84 1.00
## 5.LowD-Out-1.HighD 0.1027 -0.75 0.95 1.00
## 3.RelD-Out-2.RelD-In 0.2217 -0.62 1.07 0.95
## 4.LowD-In-2.RelD-In -0.1747 -0.95 0.60 0.97
## 5.LowD-Out-2.RelD-In -0.0783 -0.88 0.72 1.00
## 4.LowD-In-3.RelD-Out -0.3964 -1.21 0.41 0.66
## 5.LowD-Out-3.RelD-Out -0.3000 -1.13 0.53 0.86
## 5.LowD-Out-4.LowD-In 0.0964 -0.66 0.86 1.00
##
##
## $`Admin-EncourageOthersReport`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5cond2clean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.028 -1.07 1.12 1.00
## 3.RelD-Out-1.HighD 0.344 -0.78 1.47 0.92
## 4.LowD-In-1.HighD 0.080 -0.97 1.13 1.00
## 5.LowD-Out-1.HighD 0.699 -0.37 1.77 0.38
## 3.RelD-Out-2.RelD-In 0.316 -0.75 1.39 0.93
## 4.LowD-In-2.RelD-In 0.052 -0.93 1.04 1.00
## 5.LowD-Out-2.RelD-In 0.671 -0.34 1.68 0.36
## 4.LowD-In-3.RelD-Out -0.264 -1.29 0.76 0.95
## 5.LowD-Out-3.RelD-Out 0.355 -0.69 1.40 0.88
## 5.LowD-Out-4.LowD-In 0.619 -0.34 1.58 0.39Logistic regression
Block
##
## Call:
## glm(formula = block ~ cond_label, family = "binomial", data = exp1_5cond2clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.613 0.344 1.78 0.075 .
## cond_label2.RelD-In 1.284 0.557 2.31 0.021 *
## cond_label3.RelD-Out 0.624 0.512 1.22 0.223
## cond_label4.LowD-In 0.486 0.462 1.05 0.294
## cond_label5.LowD-Out 1.045 0.517 2.02 0.043 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 240.36 on 228 degrees of freedom
## Residual deviance: 233.20 on 224 degrees of freedom
## AIC: 243.2
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_label2.RelD-In cond_label3.RelD-Out cond_label4.LowD-In cond_label5.LowD-Out
## 1.8 3.6 1.9 1.6 2.8With controls
summary(glm(block~cond_label+gender+morality+Age+check_1+check_3, exp1_5cond2clean, family = "binomial"))##
## Call:
## glm(formula = block ~ cond_label + gender + morality + Age +
## check_1 + check_3, family = "binomial", data = exp1_5cond2clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.92857 1.57205 -1.86 0.062 .
## cond_label2.RelD-In 1.30957 0.64688 2.02 0.043 *
## cond_label3.RelD-Out 0.36973 0.61226 0.60 0.546
## cond_label4.LowD-In 0.41406 0.56714 0.73 0.465
## cond_label5.LowD-Out 1.16855 0.60150 1.94 0.052 .
## gender -0.08637 0.33284 -0.26 0.795
## morality"Benevolence" -0.98148 1.32254 -0.74 0.458
## morality"Hedonism" -2.36198 1.77278 -1.33 0.183
## morality"Power" -1.97303 1.76385 -1.12 0.263
## morality"Security" -1.15052 1.18202 -0.97 0.330
## morality"Self-direction" -1.84852 1.20425 -1.53 0.125
## morality"Stimulation" -2.37729 1.37661 -1.73 0.084 .
## morality"Tradition" -0.01195 1.58358 -0.01 0.994
## morality"Universalism" -1.88761 1.21202 -1.56 0.119
## Age -0.00676 0.01510 -0.45 0.654
## check_1 0.06987 0.15555 0.45 0.653
## check_3 0.84807 0.20060 4.23 0.000024 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 240.36 on 228 degrees of freedom
## Residual deviance: 189.79 on 212 degrees of freedom
## AIC: 223.8
##
## Number of Fisher Scoring iterations: 5Graphs
Full scale measures
Controls
Full list
## $Tightness
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.029 -0.589 0.000 0.582 2.993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.49549 0.47370 7.38 0.0000000000041 ***
## cond_label2.RelD-In 0.98939 0.21034 4.70 0.0000047302165 ***
## cond_label3.RelD-Out 0.74532 0.22245 3.35 0.00096 ***
## cond_label4.LowD-In 1.23759 0.20578 6.01 0.0000000083522 ***
## cond_label5.LowD-Out 1.21558 0.20851 5.83 0.0000000216889 ***
## gender -0.21013 0.11878 -1.77 0.07840 .
## Age -0.00944 0.00543 -1.74 0.08363 .
## Race1,2 -0.32194 0.56202 -0.57 0.56740
## Race1,2,5 0.98011 0.95940 1.02 0.30820
## Race1,3 0.76922 0.98683 0.78 0.43660
## Race1,6 0.01967 0.67837 0.03 0.97690
## Race2 0.05744 0.19765 0.29 0.77164
## Race2,7 -0.29647 0.95258 -0.31 0.75595
## Race3 0.12722 0.23773 0.54 0.59313
## Race5 0.50262 0.96847 0.52 0.60434
## Race6 0.79201 0.95621 0.83 0.40849
## Race7 0.23058 0.48240 0.48 0.63318
## morality"Benevolence" -0.57745 0.32775 -1.76 0.07961 .
## morality"Hedonism" -0.48090 0.60929 -0.79 0.43087
## morality"Power" 1.01691 0.60053 1.69 0.09193 .
## morality"Security" -0.17580 0.27683 -0.64 0.52611
## morality"Self-direction" -0.24559 0.29277 -0.84 0.40256
## morality"Stimulation" -0.60095 0.39772 -1.51 0.13235
## morality"Tradition" 0.11882 0.39018 0.30 0.76104
## morality"Universalism" -0.30988 0.30520 -1.02 0.31116
## check_1 -0.00410 0.05408 -0.08 0.93962
## check_3 0.16992 0.06895 2.46 0.01456 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.93 on 202 degrees of freedom
## Multiple R-squared: 0.283, Adjusted R-squared: 0.19
## F-statistic: 3.06 on 26 and 202 DF, p-value: 0.00000487
##
##
## $Status
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.348 -0.905 -0.205 0.734 4.344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.41987 0.67448 8.04 0.000000000000077 ***
## cond_label2.RelD-In 0.03169 0.29950 0.11 0.91585
## cond_label3.RelD-Out 0.20655 0.31673 0.65 0.51506
## cond_label4.LowD-In 0.21188 0.29300 0.72 0.47042
## cond_label5.LowD-Out 0.00531 0.29688 0.02 0.98575
## gender 0.06269 0.16913 0.37 0.71129
## Age -0.00523 0.00773 -0.68 0.49953
## Race1,2 -1.05815 0.80024 -1.32 0.18756
## Race1,2,5 3.71234 1.36605 2.72 0.00715 **
## Race1,3 -1.21979 1.40510 -0.87 0.38636
## Race1,6 -0.32354 0.96589 -0.33 0.73800
## Race2 1.05457 0.28143 3.75 0.00023 ***
## Race2,7 -0.31992 1.35633 -0.24 0.81377
## Race3 0.09278 0.33849 0.27 0.78429
## Race5 1.99991 1.37896 1.45 0.14853
## Race6 -1.26016 1.36150 -0.93 0.35577
## Race7 0.65679 0.68686 0.96 0.34011
## morality"Benevolence" -0.60513 0.46667 -1.30 0.19621
## morality"Hedonism" -1.42731 0.86754 -1.65 0.10147
## morality"Power" -0.40109 0.85506 -0.47 0.63952
## morality"Security" -0.47921 0.39417 -1.22 0.22550
## morality"Self-direction" -0.47586 0.41686 -1.14 0.25501
## morality"Stimulation" -0.19957 0.56629 -0.35 0.72489
## morality"Tradition" 0.47627 0.55556 0.86 0.39231
## morality"Universalism" -0.61898 0.43456 -1.42 0.15588
## check_1 -0.09904 0.07700 -1.29 0.19983
## check_3 -0.32203 0.09817 -3.28 0.00122 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 202 degrees of freedom
## Multiple R-squared: 0.262, Adjusted R-squared: 0.168
## F-statistic: 2.76 on 26 and 202 DF, p-value: 0.0000343
##
##
## $Domin.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.633 -0.949 0.000 0.927 3.369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.63563 0.68083 5.34 0.00000025 ***
## cond_label2.RelD-In 0.15489 0.30231 0.51 0.609
## cond_label3.RelD-Out -0.31727 0.31971 -0.99 0.322
## cond_label4.LowD-In 0.15534 0.29576 0.53 0.600
## cond_label5.LowD-Out 0.11886 0.29967 0.40 0.692
## gender 0.02776 0.17072 0.16 0.871
## Age -0.00933 0.00780 -1.20 0.233
## Race1,2 -0.85174 0.80776 -1.05 0.293
## Race1,2,5 -2.13589 1.37889 -1.55 0.123
## Race1,3 1.64776 1.41831 1.16 0.247
## Race1,6 1.18837 0.97497 1.22 0.224
## Race2 -0.05322 0.28408 -0.19 0.852
## Race2,7 0.26679 1.36908 0.19 0.846
## Race3 -0.01329 0.34167 -0.04 0.969
## Race5 -0.53456 1.39193 -0.38 0.701
## Race6 -0.69302 1.37430 -0.50 0.615
## Race7 -1.14964 0.69332 -1.66 0.099 .
## morality"Benevolence" -0.32665 0.47105 -0.69 0.489
## morality"Hedonism" 0.88538 0.87570 1.01 0.313
## morality"Power" 1.35500 0.86310 1.57 0.118
## morality"Security" -0.30673 0.39788 -0.77 0.442
## morality"Self-direction" -0.75516 0.42078 -1.79 0.074 .
## morality"Stimulation" -0.75260 0.57162 -1.32 0.189
## morality"Tradition" -0.66833 0.56078 -1.19 0.235
## morality"Universalism" -0.53814 0.43864 -1.23 0.221
## check_1 0.04032 0.07773 0.52 0.604
## check_3 -0.00609 0.09909 -0.06 0.951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 202 degrees of freedom
## Multiple R-squared: 0.125, Adjusted R-squared: 0.0126
## F-statistic: 1.11 on 26 and 202 DF, p-value: 0.33
##
##
## $Monitor
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.416 -0.888 0.026 1.031 3.079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.54149 0.77960 4.54 0.0000095 ***
## cond_label2.RelD-In 0.69020 0.34617 1.99 0.048 *
## cond_label3.RelD-Out 0.60629 0.36609 1.66 0.099 .
## cond_label4.LowD-In 0.49585 0.33866 1.46 0.145
## cond_label5.LowD-Out 0.58065 0.34315 1.69 0.092 .
## gender -0.12346 0.19549 -0.63 0.528
## Age -0.00862 0.00894 -0.96 0.336
## Race1,2 -0.58330 0.92495 -0.63 0.529
## Race1,2,5 -1.10403 1.57894 -0.70 0.485
## Race1,3 0.68145 1.62408 0.42 0.675
## Race1,6 0.62649 1.11642 0.56 0.575
## Race2 0.36937 0.32529 1.14 0.258
## Race2,7 -0.94817 1.56771 -0.60 0.546
## Race3 -0.10478 0.39124 -0.27 0.789
## Race5 -0.43324 1.59387 -0.27 0.786
## Race6 1.83837 1.57368 1.17 0.244
## Race7 0.65950 0.79391 0.83 0.407
## morality"Benevolence" -0.25831 0.53939 -0.48 0.633
## morality"Hedonism" 0.52281 1.00274 0.52 0.603
## morality"Power" -0.62837 0.98832 -0.64 0.526
## morality"Security" -0.48824 0.45560 -1.07 0.285
## morality"Self-direction" -0.34162 0.48183 -0.71 0.479
## morality"Stimulation" -1.25783 0.65455 -1.92 0.056 .
## morality"Tradition" 0.08756 0.64214 0.14 0.892
## morality"Universalism" -0.36432 0.50228 -0.73 0.469
## check_1 0.07443 0.08900 0.84 0.404
## check_3 0.11967 0.11347 1.05 0.293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 202 degrees of freedom
## Multiple R-squared: 0.112, Adjusted R-squared: -0.00276
## F-statistic: 0.976 on 26 and 202 DF, p-value: 0.503
##
##
## $SOP
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.236 -0.682 0.015 0.769 2.859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.29948 0.63493 6.77 0.00000000014 ***
## cond_label2.RelD-In 0.15763 0.28193 0.56 0.577
## cond_label3.RelD-Out 0.05681 0.29816 0.19 0.849
## cond_label4.LowD-In -0.10860 0.27582 -0.39 0.694
## cond_label5.LowD-Out -0.21324 0.27947 -0.76 0.446
## gender 0.31093 0.15921 1.95 0.052 .
## Age -0.01010 0.00728 -1.39 0.167
## Race1,2 -0.84962 0.75331 -1.13 0.261
## Race1,2,5 2.68769 1.28594 2.09 0.038 *
## Race1,3 0.47617 1.32270 0.36 0.719
## Race1,6 0.83935 0.90925 0.92 0.357
## Race2 -0.23127 0.26493 -0.87 0.384
## Race2,7 0.75791 1.27679 0.59 0.553
## Race3 -0.08942 0.31864 -0.28 0.779
## Race5 0.50434 1.29810 0.39 0.698
## Race6 0.59324 1.28166 0.46 0.644
## Race7 -0.25131 0.64658 -0.39 0.698
## morality"Benevolence" 0.11070 0.43930 0.25 0.801
## morality"Hedonism" -0.35524 0.81666 -0.43 0.664
## morality"Power" -0.03371 0.80492 -0.04 0.967
## morality"Security" 0.21889 0.37105 0.59 0.556
## morality"Self-direction" 0.17739 0.39242 0.45 0.652
## morality"Stimulation" 0.49933 0.53308 0.94 0.350
## morality"Tradition" 0.71823 0.52298 1.37 0.171
## morality"Universalism" -0.01713 0.40907 -0.04 0.967
## check_1 0.00398 0.07249 0.05 0.956
## check_3 -0.04537 0.09241 -0.49 0.624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 202 degrees of freedom
## Multiple R-squared: 0.0872, Adjusted R-squared: -0.0303
## F-statistic: 0.742 on 26 and 202 DF, p-value: 0.814
##
##
## $Affil.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.281 -0.667 -0.159 0.589 3.616
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.4343590 0.5276813 12.19 < 0.0000000000000002 ***
## cond_label2.RelD-In 0.2411577 0.2343109 1.03 0.30461
## cond_label3.RelD-Out 0.2612614 0.2477950 1.05 0.29298
## cond_label4.LowD-In -0.0000819 0.2292288 0.00 0.99972
## cond_label5.LowD-Out 0.3126411 0.2322658 1.35 0.17980
## gender -0.2014307 0.1323181 -1.52 0.12949
## Age 0.0016126 0.0060479 0.27 0.79001
## Race1,2 -0.5915194 0.6260621 -0.94 0.34588
## Race1,2,5 1.4653796 1.0687223 1.37 0.17185
## Race1,3 -0.3119000 1.0992743 -0.28 0.77691
## Race1,6 -0.4771922 0.7556629 -0.63 0.52844
## Race2 0.6390465 0.2201755 2.90 0.00411 **
## Race2,7 -0.6146408 1.0611196 -0.58 0.56307
## Race3 -0.2740485 0.2648179 -1.03 0.30197
## Race5 1.8258863 1.0788290 1.69 0.09210 .
## Race6 -0.2993241 1.0651653 -0.28 0.77899
## Race7 0.2304283 0.5373652 0.43 0.66852
## morality"Benevolence" -0.9366757 0.3650944 -2.57 0.01103 *
## morality"Hedonism" -1.5014148 0.6787172 -2.21 0.02808 *
## morality"Power" -1.2604621 0.6689554 -1.88 0.06097 .
## morality"Security" -0.9960367 0.3083774 -3.23 0.00145 **
## morality"Self-direction" -0.7694735 0.3261320 -2.36 0.01926 *
## morality"Stimulation" -0.3282620 0.4430364 -0.74 0.45959
## morality"Tradition" -0.1687087 0.4346423 -0.39 0.69831
## morality"Universalism" -0.6001475 0.3399735 -1.77 0.07903 .
## check_1 -0.2605296 0.0602429 -4.32 0.000024 ***
## check_3 -0.2850774 0.0768033 -3.71 0.00027 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 202 degrees of freedom
## Multiple R-squared: 0.409, Adjusted R-squared: 0.333
## F-statistic: 5.38 on 26 and 202 DF, p-value: 0.00000000000109
##
##
## $Block
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8987 -0.0595 0.0864 0.2198 0.8648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04336 0.19235 -0.23 0.822
## cond_label2.RelD-In 0.18230 0.08541 2.13 0.034 *
## cond_label3.RelD-Out 0.06322 0.09033 0.70 0.485
## cond_label4.LowD-In 0.08968 0.08356 1.07 0.284
## cond_label5.LowD-Out 0.20589 0.08467 2.43 0.016 *
## gender -0.02045 0.04823 -0.42 0.672
## Age -0.00194 0.00220 -0.88 0.380
## Race1,2 0.26876 0.22821 1.18 0.240
## Race1,2,5 0.10064 0.38957 0.26 0.796
## Race1,3 0.13302 0.40071 0.33 0.740
## Race1,6 0.09939 0.27545 0.36 0.719
## Race2 -0.04716 0.08026 -0.59 0.557
## Race2,7 0.36062 0.38680 0.93 0.352
## Race3 -0.22593 0.09653 -2.34 0.020 *
## Race5 0.20445 0.39325 0.52 0.604
## Race6 0.08039 0.38827 0.21 0.836
## Race7 0.04365 0.19588 0.22 0.824
## morality"Benevolence" -0.06220 0.13308 -0.47 0.641
## morality"Hedonism" -0.19423 0.24741 -0.79 0.433
## morality"Power" -0.21959 0.24385 -0.90 0.369
## morality"Security" -0.07987 0.11241 -0.71 0.478
## morality"Self-direction" -0.18657 0.11888 -1.57 0.118
## morality"Stimulation" -0.25610 0.16150 -1.59 0.114
## morality"Tradition" 0.04073 0.15844 0.26 0.797
## morality"Universalism" -0.17853 0.12393 -1.44 0.151
## check_1 0.01373 0.02196 0.63 0.533
## check_3 0.14302 0.02800 5.11 0.00000075 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.38 on 202 degrees of freedom
## Multiple R-squared: 0.261, Adjusted R-squared: 0.166
## F-statistic: 2.75 on 26 and 202 DF, p-value: 0.0000389
##
##
## $Admin
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1358 -0.4002 0.0116 0.5529 2.1407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.13831 0.40980 5.22 0.00000044723 ***
## cond_label2.RelD-In 0.12798 0.18197 0.70 0.4827
## cond_label3.RelD-Out 0.09973 0.19244 0.52 0.6049
## cond_label4.LowD-In -0.16087 0.17802 -0.90 0.3673
## cond_label5.LowD-Out 0.06255 0.18038 0.35 0.7291
## gender -0.14385 0.10276 -1.40 0.1631
## Age 0.00488 0.00470 1.04 0.2997
## Race1,2 1.00065 0.48621 2.06 0.0409 *
## Race1,2,5 -0.33932 0.82998 -0.41 0.6831
## Race1,3 1.25231 0.85371 1.47 0.1440
## Race1,6 0.11751 0.58686 0.20 0.8415
## Race2 0.13415 0.17099 0.78 0.4336
## Race2,7 0.21510 0.82408 0.26 0.7943
## Race3 -0.22036 0.20566 -1.07 0.2852
## Race5 -0.64033 0.83783 -0.76 0.4456
## Race6 0.74042 0.82722 0.90 0.3718
## Race7 0.26830 0.41732 0.64 0.5210
## morality"Benevolence" 0.05737 0.28354 0.20 0.8398
## morality"Hedonism" 0.02367 0.52710 0.04 0.9642
## morality"Power" -0.85507 0.51952 -1.65 0.1013
## morality"Security" -0.05221 0.23949 -0.22 0.8276
## morality"Self-direction" -0.20873 0.25328 -0.82 0.4108
## morality"Stimulation" -0.07618 0.34407 -0.22 0.8250
## morality"Tradition" -0.30831 0.33755 -0.91 0.3621
## morality"Universalism" -0.29142 0.26403 -1.10 0.2710
## check_1 0.13059 0.04679 2.79 0.0058 **
## check_3 0.39668 0.05965 6.65 0.00000000027 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.81 on 202 degrees of freedom
## Multiple R-squared: 0.444, Adjusted R-squared: 0.373
## F-statistic: 6.22 on 26 and 202 DF, p-value: 0.00000000000000562
##
##
## $`Admin-Block`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.404 -0.907 0.190 1.036 2.788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.40594 0.77198 1.82 0.0701 .
## cond_label2.RelD-In 0.37451 0.34279 1.09 0.2759
## cond_label3.RelD-Out 0.42471 0.36252 1.17 0.2428
## cond_label4.LowD-In -0.13377 0.33536 -0.40 0.6904
## cond_label5.LowD-Out 0.54033 0.33980 1.59 0.1134
## gender -0.25781 0.19358 -1.33 0.1844
## Age 0.00551 0.00885 0.62 0.5338
## Race1,2 1.88047 0.91591 2.05 0.0414 *
## Race1,2,5 2.12660 1.56351 1.36 0.1753
## Race1,3 2.11988 1.60821 1.32 0.1889
## Race1,6 0.57482 1.10552 0.52 0.6037
## Race2 0.35451 0.32211 1.10 0.2724
## Race2,7 0.19484 1.55239 0.13 0.9002
## Race3 -0.32699 0.38742 -0.84 0.3997
## Race5 -0.68801 1.57830 -0.44 0.6634
## Race6 1.50022 1.55831 0.96 0.3368
## Race7 0.57563 0.78615 0.73 0.4649
## morality"Benevolence" 0.01846 0.53412 0.03 0.9725
## morality"Hedonism" 0.35102 0.99295 0.35 0.7241
## morality"Power" -1.18941 0.97866 -1.22 0.2257
## morality"Security" 0.05704 0.45115 0.13 0.8995
## morality"Self-direction" -0.67572 0.47712 -1.42 0.1582
## morality"Stimulation" -0.32803 0.64815 -0.51 0.6133
## morality"Tradition" -1.06486 0.63587 -1.67 0.0956 .
## morality"Universalism" -0.62616 0.49737 -1.26 0.2095
## check_1 0.28412 0.08813 3.22 0.0015 **
## check_3 0.33904 0.11236 3.02 0.0029 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 202 degrees of freedom
## Multiple R-squared: 0.324, Adjusted R-squared: 0.237
## F-statistic: 3.73 on 26 and 202 DF, p-value: 0.0000000572
##
##
## $`Admin-Kick`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.138 -1.034 0.209 1.051 2.963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.55641 0.77921 2.00 0.047 *
## cond_label2.RelD-In 0.06620 0.34600 0.19 0.848
## cond_label3.RelD-Out 0.18306 0.36591 0.50 0.617
## cond_label4.LowD-In -0.30996 0.33849 -0.92 0.361
## cond_label5.LowD-Out 0.21296 0.34298 0.62 0.535
## gender -0.07938 0.19539 -0.41 0.685
## Age -0.01289 0.00893 -1.44 0.150
## Race1,2 0.61726 0.92448 0.67 0.505
## Race1,2,5 -1.73233 1.57814 -1.10 0.274
## Race1,3 0.56377 1.62326 0.35 0.729
## Race1,6 1.02920 1.11586 0.92 0.357
## Race2 0.24176 0.32513 0.74 0.458
## Race2,7 1.15145 1.56692 0.73 0.463
## Race3 -0.86677 0.39105 -2.22 0.028 *
## Race5 -1.03772 1.59307 -0.65 0.516
## Race6 -0.92366 1.57289 -0.59 0.558
## Race7 -0.36839 0.79351 -0.46 0.643
## morality"Benevolence" -0.28439 0.53912 -0.53 0.598
## morality"Hedonism" -0.42155 1.00224 -0.42 0.674
## morality"Power" -1.50570 0.98782 -1.52 0.129
## morality"Security" -0.26734 0.45537 -0.59 0.558
## morality"Self-direction" -0.69777 0.48159 -1.45 0.149
## morality"Stimulation" -0.73668 0.65422 -1.13 0.261
## morality"Tradition" -0.56401 0.64182 -0.88 0.381
## morality"Universalism" -0.59678 0.50203 -1.19 0.236
## check_1 0.15440 0.08896 1.74 0.084 .
## check_3 0.55689 0.11341 4.91 0.0000019 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 202 degrees of freedom
## Multiple R-squared: 0.295, Adjusted R-squared: 0.205
## F-statistic: 3.26 on 26 and 202 DF, p-value: 0.00000131
##
##
## $`Admin-Report`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.514 -0.967 0.000 1.031 4.979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.58913 0.78926 2.01 0.04539 *
## cond_label2.RelD-In -0.70921 0.35046 -2.02 0.04432 *
## cond_label3.RelD-Out -0.14493 0.37063 -0.39 0.69619
## cond_label4.LowD-In -0.31727 0.34286 -0.93 0.35589
## cond_label5.LowD-Out 0.00276 0.34740 0.01 0.99368
## gender -0.48470 0.19791 -2.45 0.01517 *
## Age 0.00615 0.00905 0.68 0.49758
## Race1,2 2.30342 0.93641 2.46 0.01474 *
## Race1,2,5 -2.65546 1.59850 -1.66 0.09822 .
## Race1,3 1.38815 1.64419 0.84 0.39951
## Race1,6 -0.12708 1.13025 -0.11 0.91059
## Race2 1.04755 0.32932 3.18 0.00170 **
## Race2,7 -0.60690 1.58713 -0.38 0.70257
## Race3 0.12821 0.39609 0.32 0.74651
## Race5 0.91794 1.61361 0.57 0.57008
## Race6 0.95832 1.59318 0.60 0.54817
## Race7 -0.46044 0.80374 -0.57 0.56737
## morality"Benevolence" -0.43364 0.54607 -0.79 0.42806
## morality"Hedonism" 1.37736 1.01516 1.36 0.17636
## morality"Power" -1.53011 1.00056 -1.53 0.12777
## morality"Security" -0.32674 0.46124 -0.71 0.47952
## morality"Self-direction" -0.65292 0.48780 -1.34 0.18224
## morality"Stimulation" -0.67254 0.66265 -1.01 0.31136
## morality"Tradition" -0.29887 0.65010 -0.46 0.64621
## morality"Universalism" -1.00487 0.50850 -1.98 0.04950 *
## check_1 0.35302 0.09011 3.92 0.00012 ***
## check_3 0.20817 0.11488 1.81 0.07145 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 202 degrees of freedom
## Multiple R-squared: 0.324, Adjusted R-squared: 0.237
## F-statistic: 3.72 on 26 and 202 DF, p-value: 0.0000000589
##
##
## $`Admin-Pin`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.147 -0.337 0.241 0.717 2.443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.88331 0.64351 4.48 0.000012 ***
## cond_label2.RelD-In 0.48753 0.28574 1.71 0.090 .
## cond_label3.RelD-Out -0.29532 0.30219 -0.98 0.330
## cond_label4.LowD-In -0.10834 0.27955 -0.39 0.699
## cond_label5.LowD-Out -0.36936 0.28325 -1.30 0.194
## gender 0.03965 0.16136 0.25 0.806
## Age 0.01472 0.00738 2.00 0.047 *
## Race1,2 0.55039 0.76349 0.72 0.472
## Race1,2,5 0.49889 1.30332 0.38 0.702
## Race1,3 1.90356 1.34057 1.42 0.157
## Race1,6 -0.31432 0.92154 -0.34 0.733
## Race2 -0.76200 0.26851 -2.84 0.005 **
## Race2,7 0.63923 1.29404 0.49 0.622
## Race3 0.31827 0.32295 0.99 0.326
## Race5 -0.58185 1.31564 -0.44 0.659
## Race6 0.76840 1.29898 0.59 0.555
## Race7 0.62923 0.65532 0.96 0.338
## morality"Benevolence" 0.74208 0.44524 1.67 0.097 .
## morality"Hedonism" -1.06755 0.82770 -1.29 0.199
## morality"Power" 0.05964 0.81580 0.07 0.942
## morality"Security" 0.28912 0.37607 0.77 0.443
## morality"Self-direction" 0.73549 0.39772 1.85 0.066 .
## morality"Stimulation" 0.94629 0.54029 1.75 0.081 .
## morality"Tradition" 0.07109 0.53005 0.13 0.893
## morality"Universalism" 0.71878 0.41460 1.73 0.085 .
## check_1 -0.06906 0.07347 -0.94 0.348
## check_3 0.41333 0.09366 4.41 0.000017 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 202 degrees of freedom
## Multiple R-squared: 0.285, Adjusted R-squared: 0.193
## F-statistic: 3.09 on 26 and 202 DF, p-value: 0.00000394
##
##
## $`Admin-Upvote`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.522 -0.377 0.180 0.548 1.991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.69973 0.55243 4.89 0.00000208 ***
## cond_label2.RelD-In 0.53330 0.24530 2.17 0.0309 *
## cond_label3.RelD-Out 0.17696 0.25942 0.68 0.4959
## cond_label4.LowD-In -0.00760 0.23998 -0.03 0.9748
## cond_label5.LowD-Out -0.13118 0.24316 -0.54 0.5902
## gender 0.07852 0.13852 0.57 0.5714
## Age 0.01763 0.00633 2.78 0.0059 **
## Race1,2 0.48807 0.65543 0.74 0.4573
## Race1,2,5 -0.63327 1.11885 -0.57 0.5720
## Race1,3 1.49522 1.15084 1.30 0.1953
## Race1,6 -0.07227 0.79111 -0.09 0.9273
## Race2 -0.57951 0.23050 -2.51 0.0127 *
## Race2,7 0.53084 1.11090 0.48 0.6333
## Race3 -0.14807 0.27724 -0.53 0.5939
## Race5 -0.94166 1.12944 -0.83 0.4054
## Race6 0.65842 1.11513 0.59 0.5556
## Race7 0.54961 0.56257 0.98 0.3298
## morality"Benevolence" 0.60545 0.38222 1.58 0.1147
## morality"Hedonism" 0.73683 0.71056 1.04 0.3010
## morality"Power" 0.83696 0.70034 1.20 0.2335
## morality"Security" 0.33202 0.32284 1.03 0.3050
## morality"Self-direction" 0.67215 0.34143 1.97 0.0504 .
## morality"Stimulation" 0.78183 0.46382 1.69 0.0934 .
## morality"Tradition" 0.37173 0.45503 0.82 0.4149
## morality"Universalism" 0.64978 0.35592 1.83 0.0694 .
## check_1 -0.06802 0.06307 -1.08 0.2821
## check_3 0.40977 0.08041 5.10 0.00000079 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 202 degrees of freedom
## Multiple R-squared: 0.291, Adjusted R-squared: 0.199
## F-statistic: 3.18 on 26 and 202 DF, p-value: 0.00000216
##
##
## $`Admin-Monitor`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.072 -0.641 0.202 0.918 3.375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.69537 0.63180 4.27 0.0000305 ***
## cond_label2.RelD-In 0.01555 0.28054 0.06 0.956
## cond_label3.RelD-Out 0.25389 0.29669 0.86 0.393
## cond_label4.LowD-In -0.08829 0.27446 -0.32 0.748
## cond_label5.LowD-Out 0.11982 0.27809 0.43 0.667
## gender -0.15940 0.15843 -1.01 0.316
## Age -0.00182 0.00724 -0.25 0.802
## Race1,2 0.16429 0.74959 0.22 0.827
## Race1,2,5 0.35964 1.27959 0.28 0.779
## Race1,3 0.04326 1.31617 0.03 0.974
## Race1,6 -0.38531 0.90476 -0.43 0.671
## Race2 0.50260 0.26362 1.91 0.058 .
## Race2,7 -0.61885 1.27048 -0.49 0.627
## Race3 -0.42681 0.31707 -1.35 0.180
## Race5 -1.51070 1.29169 -1.17 0.244
## Race6 1.48081 1.27533 1.16 0.247
## Race7 0.68414 0.64339 1.06 0.289
## morality"Benevolence" -0.30371 0.43713 -0.69 0.488
## morality"Hedonism" -0.83407 0.81263 -1.03 0.306
## morality"Power" -1.80180 0.80094 -2.25 0.026 *
## morality"Security" -0.39738 0.36922 -1.08 0.283
## morality"Self-direction" -0.63362 0.39048 -1.62 0.106
## morality"Stimulation" -0.44797 0.53045 -0.84 0.399
## morality"Tradition" -0.36491 0.52040 -0.70 0.484
## morality"Universalism" -0.88924 0.40705 -2.18 0.030 *
## check_1 0.12905 0.07213 1.79 0.075 .
## check_3 0.45290 0.09196 4.93 0.0000018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 202 degrees of freedom
## Multiple R-squared: 0.32, Adjusted R-squared: 0.232
## F-statistic: 3.65 on 26 and 202 DF, p-value: 0.0000000931
##
##
## $`Admin-EncourageOthersReport`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.934 -0.928 0.000 1.217 3.805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4353921 0.8486082 1.69 0.09229 .
## cond_label2.RelD-In -0.1245346 0.3768148 -0.33 0.74137
## cond_label3.RelD-Out 0.0384090 0.3984998 0.10 0.92331
## cond_label4.LowD-In 0.0154098 0.3686418 0.04 0.96670
## cond_label5.LowD-Out 0.7921512 0.3735259 2.12 0.03516 *
## gender 0.0119555 0.2127917 0.06 0.95525
## Age 0.0000414 0.0097262 0.00 0.99661
## Race1,2 1.0304555 1.0068226 1.02 0.30731
## Race1,2,5 1.5168194 1.7187013 0.88 0.37853
## Race1,3 1.2475743 1.7678346 0.71 0.48118
## Race1,6 0.2979684 1.2152444 0.25 0.80656
## Race2 0.5995858 0.3540826 1.69 0.09193 .
## Race2,7 1.3541576 1.7064748 0.79 0.42839
## Race3 -0.4278518 0.4258758 -1.00 0.31627
## Race5 1.0649399 1.7349547 0.61 0.54003
## Race6 -0.4942672 1.7129810 -0.29 0.77323
## Race7 -0.3464565 0.8641817 -0.40 0.68891
## morality"Benevolence" -0.3856497 0.5871388 -0.66 0.51204
## morality"Hedonism" -0.0388809 1.0915016 -0.04 0.97162
## morality"Power" -2.2302341 1.0758028 -2.07 0.03943 *
## morality"Security" -0.5180094 0.4959274 -1.04 0.29749
## morality"Self-direction" -1.1709197 0.5244800 -2.23 0.02668 *
## morality"Stimulation" -0.8876682 0.7124837 -1.25 0.21425
## morality"Tradition" -0.9866842 0.6989844 -1.41 0.15961
## morality"Universalism" -1.0886739 0.5467396 -1.99 0.04781 *
## check_1 0.1417968 0.0968815 1.46 0.14485
## check_3 0.4241503 0.1235138 3.43 0.00072 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.7 on 202 degrees of freedom
## Multiple R-squared: 0.242, Adjusted R-squared: 0.145
## F-statistic: 2.48 on 26 and 202 DF, p-value: 0.000212Moderations
Just significant moderations
Broad
For these analyses, I grouped conditions as just “Low”, “Relative”, and “High” diversity.
Main effects and scale information
Post hoc tests
## $Tightness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.92 0.480 1.36 0.00
## 3. Low-1. High 1.18 0.753 1.61 0.00
## 3. Low-2. Relative 0.26 -0.067 0.59 0.15
##
##
## $Status
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High -0.041 -0.72 0.64 0.99
## 3. Low-1. High 0.088 -0.57 0.74 0.95
## 3. Low-2. Relative 0.129 -0.37 0.63 0.82
##
##
## $Domin.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High -0.032 -0.66 0.60 0.99
## 3. Low-1. High 0.031 -0.58 0.64 0.99
## 3. Low-2. Relative 0.063 -0.40 0.53 0.94
##
##
## $Monitor
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.72 0.02 1.43 0.04
## 3. Low-1. High 0.53 -0.15 1.22 0.16
## 3. Low-2. Relative -0.19 -0.71 0.33 0.66
##
##
## $SOP
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.102 -0.47 0.67 0.91
## 3. Low-1. High -0.079 -0.63 0.48 0.94
## 3. Low-2. Relative -0.181 -0.60 0.24 0.57
##
##
## $Affil.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.112 -0.48 0.70 0.90
## 3. Low-1. High 0.084 -0.49 0.66 0.94
## 3. Low-2. Relative -0.028 -0.46 0.41 0.99
##
##
## $Block
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.177 -0.014 0.37 0.08
## 3. Low-1. High 0.144 -0.042 0.33 0.16
## 3. Low-2. Relative -0.033 -0.174 0.11 0.84
##
##
## $Admin
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.307 -0.16 0.776 0.27
## 3. Low-1. High -0.004 -0.46 0.451 1.00
## 3. Low-2. Relative -0.311 -0.66 0.035 0.09
##
##
## $`Admin-Block`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.61 -0.19 1.41 0.17
## 3. Low-1. High 0.19 -0.59 0.96 0.84
## 3. Low-2. Relative -0.42 -1.02 0.17 0.21
##
##
## $`Admin-Kick`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.30 -0.49 1.10 0.64
## 3. Low-1. High -0.15 -0.92 0.62 0.89
## 3. Low-2. Relative -0.45 -1.04 0.13 0.16
##
##
## $`Admin-Report`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High -0.133 -0.96 0.69 0.92
## 3. Low-1. High -0.152 -0.95 0.65 0.90
## 3. Low-2. Relative -0.019 -0.63 0.59 1.00
##
##
## $`Admin-Pin`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.240 -0.41 0.89 0.66
## 3. Low-1. High -0.046 -0.68 0.59 0.98
## 3. Low-2. Relative -0.286 -0.77 0.20 0.34
##
##
## $`Admin-Upvote`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.535 -0.02 1.09 0.06
## 3. Low-1. High 0.085 -0.45 0.62 0.93
## 3. Low-2. Relative -0.450 -0.86 -0.04 0.03
##
##
## $`Admin-Monitor`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.284 -0.37 0.94 0.57
## 3. Low-1. High 0.052 -0.59 0.69 0.98
## 3. Low-2. Relative -0.232 -0.72 0.25 0.50
##
##
## $`Admin-EncourageOthersReport`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5cond2clean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.17 -0.66 1.01 0.88
## 3. Low-1. High 0.37 -0.44 1.19 0.53
## 3. Low-2. Relative 0.20 -0.42 0.82 0.73Logistic regression
Block
##
## Call:
## glm(formula = block ~ cond_broad, family = "binomial", data = exp1_5cond2clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.613 0.344 1.78 0.075 .
## cond_broad2. Relative 0.942 0.446 2.11 0.035 *
## cond_broad3. Low 0.727 0.419 1.73 0.083 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 240.36 on 228 degrees of freedom
## Residual deviance: 235.84 on 226 degrees of freedom
## AIC: 241.8
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_broad2. Relative cond_broad3. Low
## 1.8 2.6 2.1Graphs
Full scale measures
Single Item measures
Controls
Full list
## $Tightness
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.324 -0.562 -0.003 0.506 3.166
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.32455 0.38448 11.25 < 0.0000000000000002 ***
## cond_broad2. Relative 0.94118 0.18988 4.96 0.00000149429 ***
## cond_broad3. Low 1.23739 0.18760 6.60 0.00000000035 ***
## gender -0.13974 0.11795 -1.18 0.238
## Age -0.00743 0.00523 -1.42 0.157
## Race1,2 -0.28888 0.56936 -0.51 0.612
## Race1,2,5 1.18867 0.95876 1.24 0.216
## Race1,3 0.56241 0.99318 0.57 0.572
## Race1,6 0.11504 0.67949 0.17 0.866
## Race2 0.11934 0.19430 0.61 0.540
## Race2,7 -0.44998 0.95967 -0.47 0.640
## Race3 0.15704 0.24045 0.65 0.514
## Race5 0.56974 0.96630 0.59 0.556
## Race6 0.97232 0.95832 1.01 0.311
## Race7 0.41210 0.48498 0.85 0.396
## morality"Benevolence" -0.61036 0.33074 -1.85 0.066 .
## morality"Hedonism" -0.60111 0.61513 -0.98 0.330
## morality"Power" 0.94829 0.60664 1.56 0.120
## morality"Security" -0.21693 0.28003 -0.77 0.439
## morality"Self-direction" -0.28266 0.29557 -0.96 0.340
## morality"Stimulation" -0.58076 0.40209 -1.44 0.150
## morality"Tradition" -0.05535 0.38571 -0.14 0.886
## morality"Universalism" -0.38354 0.30839 -1.24 0.215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.94 on 206 degrees of freedom
## Multiple R-squared: 0.248, Adjusted R-squared: 0.167
## F-statistic: 3.08 on 22 and 206 DF, p-value: 0.0000138
##
##
## $Status
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.426 -1.161 -0.230 0.749 4.760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.47338 0.56812 6.11 0.0000000048 ***
## cond_broad2. Relative -0.02726 0.28057 -0.10 0.9227
## cond_broad3. Low 0.07333 0.27720 0.26 0.7916
## gender -0.10279 0.17429 -0.59 0.5560
## Age -0.01317 0.00772 -1.71 0.0897 .
## Race1,2 -1.11192 0.84130 -1.32 0.1877
## Race1,2,5 3.60635 1.41669 2.55 0.0116 *
## Race1,3 -0.97713 1.46755 -0.67 0.5063
## Race1,6 -0.83806 1.00403 -0.83 0.4049
## Race2 0.85520 0.28710 2.98 0.0032 **
## Race2,7 0.19305 1.41802 0.14 0.8918
## Race3 0.00458 0.35529 0.01 0.9897
## Race5 1.85195 1.42783 1.30 0.1961
## Race6 -1.36744 1.41603 -0.97 0.3353
## Race7 0.33024 0.71661 0.46 0.6454
## morality"Benevolence" -0.45274 0.48870 -0.93 0.3553
## morality"Hedonism" -1.28730 0.90893 -1.42 0.1582
## morality"Power" -0.37415 0.89638 -0.42 0.6768
## morality"Security" -0.35224 0.41377 -0.85 0.3956
## morality"Self-direction" -0.35210 0.43673 -0.81 0.4211
## morality"Stimulation" -0.24472 0.59414 -0.41 0.6809
## morality"Tradition" 1.00286 0.56994 1.76 0.0800 .
## morality"Universalism" -0.44212 0.45569 -0.97 0.3331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 206 degrees of freedom
## Multiple R-squared: 0.167, Adjusted R-squared: 0.0782
## F-statistic: 1.88 on 22 and 206 DF, p-value: 0.0125
##
##
## $Domin.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.641 -0.959 0.000 0.878 3.462
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.73001 0.54309 6.87 0.000000000075 ***
## cond_broad2. Relative -0.04876 0.26821 -0.18 0.856
## cond_broad3. Low 0.13909 0.26499 0.52 0.600
## gender 0.04996 0.16661 0.30 0.765
## Age -0.00843 0.00738 -1.14 0.255
## Race1,2 -0.87428 0.80423 -1.09 0.278
## Race1,2,5 -2.16693 1.35427 -1.60 0.111
## Race1,3 1.38752 1.40289 0.99 0.324
## Race1,6 1.13271 0.95979 1.18 0.239
## Race2 0.01252 0.27445 0.05 0.964
## Race2,7 0.24736 1.35555 0.18 0.855
## Race3 -0.03050 0.33964 -0.09 0.929
## Race5 -0.83082 1.36492 -0.61 0.543
## Race6 -0.72734 1.35365 -0.54 0.592
## Race7 -1.08799 0.68504 -1.59 0.114
## morality"Benevolence" -0.30184 0.46717 -0.65 0.519
## morality"Hedonism" 0.79713 0.86888 0.92 0.360
## morality"Power" 1.26884 0.85689 1.48 0.140
## morality"Security" -0.30512 0.39554 -0.77 0.441
## morality"Self-direction" -0.77006 0.41749 -1.84 0.067 .
## morality"Stimulation" -0.69288 0.56797 -1.22 0.224
## morality"Tradition" -0.61707 0.54482 -1.13 0.259
## morality"Universalism" -0.54615 0.43561 -1.25 0.211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 206 degrees of freedom
## Multiple R-squared: 0.114, Adjusted R-squared: 0.0194
## F-statistic: 1.2 on 22 and 206 DF, p-value: 0.247
##
##
## $Monitor
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.351 -1.089 0.072 0.912 3.102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.39519 0.62439 7.04 0.000000000028 ***
## cond_broad2. Relative 0.71134 0.30836 2.31 0.022 *
## cond_broad3. Low 0.55682 0.30465 1.83 0.069 .
## gender -0.05018 0.19155 -0.26 0.794
## Age -0.00427 0.00849 -0.50 0.616
## Race1,2 -0.56725 0.92462 -0.61 0.540
## Race1,2,5 -1.12680 1.55701 -0.72 0.470
## Race1,3 0.57920 1.61290 0.36 0.720
## Race1,6 0.88588 1.10347 0.80 0.423
## Race2 0.48169 0.31554 1.53 0.128
## Race2,7 -1.17907 1.55847 -0.76 0.450
## Race3 -0.06746 0.39048 -0.17 0.863
## Race5 -0.43463 1.56925 -0.28 0.782
## Race6 1.83373 1.55628 1.18 0.240
## Race7 0.78893 0.78759 1.00 0.318
## morality"Benevolence" -0.33517 0.53711 -0.62 0.533
## morality"Hedonism" 0.47670 0.99895 0.48 0.634
## morality"Power" -0.63818 0.98516 -0.65 0.518
## morality"Security" -0.54878 0.45475 -1.21 0.229
## morality"Self-direction" -0.40861 0.47999 -0.85 0.396
## morality"Stimulation" -1.22991 0.65299 -1.88 0.061 .
## morality"Tradition" -0.15332 0.62638 -0.24 0.807
## morality"Universalism" -0.44243 0.50082 -0.88 0.378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 206 degrees of freedom
## Multiple R-squared: 0.0929, Adjusted R-squared: -0.00393
## F-statistic: 0.959 on 22 and 206 DF, p-value: 0.517
##
##
## $SOP
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1138 -0.6702 0.0233 0.7485 2.9100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.08575 0.50405 8.11 0.000000000000046 ***
## cond_broad2. Relative 0.09695 0.24893 0.39 0.697
## cond_broad3. Low -0.16300 0.24594 -0.66 0.508
## gender 0.29362 0.15463 1.90 0.059 .
## Age -0.01060 0.00685 -1.55 0.123
## Race1,2 -0.85971 0.74642 -1.15 0.251
## Race1,2,5 2.68267 1.25693 2.13 0.034 *
## Race1,3 0.44206 1.30205 0.34 0.735
## Race1,6 0.75095 0.89080 0.84 0.400
## Race2 -0.23202 0.25473 -0.91 0.363
## Race2,7 0.84892 1.25811 0.67 0.501
## Race3 -0.10614 0.31522 -0.34 0.737
## Race5 0.39489 1.26681 0.31 0.756
## Race6 0.59501 1.25635 0.47 0.636
## Race7 -0.27972 0.63580 -0.44 0.660
## morality"Benevolence" 0.13362 0.43359 0.31 0.758
## morality"Hedonism" -0.35688 0.80642 -0.44 0.659
## morality"Power" -0.06526 0.79530 -0.08 0.935
## morality"Security" 0.23421 0.36711 0.64 0.524
## morality"Self-direction" 0.18258 0.38748 0.47 0.638
## morality"Stimulation" 0.50658 0.52714 0.96 0.338
## morality"Tradition" 0.79410 0.50566 1.57 0.118
## morality"Universalism" 0.00258 0.40430 0.01 0.995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 206 degrees of freedom
## Multiple R-squared: 0.0844, Adjusted R-squared: -0.0134
## F-statistic: 0.863 on 22 and 206 DF, p-value: 0.644
##
##
## $Affil.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.349 -0.970 -0.127 0.779 3.486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0871 0.4928 8.29 0.000000000000014 ***
## cond_broad2. Relative 0.0966 0.2434 0.40 0.692
## cond_broad3. Low 0.0888 0.2404 0.37 0.712
## gender -0.3744 0.1512 -2.48 0.014 *
## Age -0.0128 0.0067 -1.91 0.058 .
## Race1,2 -0.6595 0.7297 -0.90 0.367
## Race1,2,5 1.4427 1.2288 1.17 0.242
## Race1,3 -0.1793 1.2729 -0.14 0.888
## Race1,6 -1.1686 0.8709 -1.34 0.181
## Race2 0.2815 0.2490 1.13 0.260
## Race2,7 -0.2398 1.2300 -0.19 0.846
## Race3 -0.3736 0.3082 -1.21 0.227
## Race5 1.8634 1.2385 1.50 0.134
## Race6 -0.4514 1.2283 -0.37 0.714
## Race7 -0.0565 0.6216 -0.09 0.928
## morality"Benevolence" -0.6843 0.4239 -1.61 0.108
## morality"Hedonism" -1.4588 0.7884 -1.85 0.066 .
## morality"Power" -1.1740 0.7775 -1.51 0.133
## morality"Security" -0.8167 0.3589 -2.28 0.024 *
## morality"Self-direction" -0.5293 0.3788 -1.40 0.164
## morality"Stimulation" -0.3636 0.5153 -0.71 0.481
## morality"Tradition" 0.5528 0.4944 1.12 0.265
## morality"Universalism" -0.3858 0.3953 -0.98 0.330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 206 degrees of freedom
## Multiple R-squared: 0.18, Adjusted R-squared: 0.0922
## F-statistic: 2.05 on 22 and 206 DF, p-value: 0.00507
##
##
## $Block
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.881 0.000 0.142 0.246 0.599
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.713222 0.169631 4.20 0.000039 ***
## cond_broad2. Relative 0.184109 0.083773 2.20 0.029 *
## cond_broad3. Low 0.158001 0.082768 1.91 0.058 .
## gender 0.046496 0.052040 0.89 0.373
## Age 0.000352 0.002306 0.15 0.879
## Race1,2 0.291697 0.251198 1.16 0.247
## Race1,2,5 0.187467 0.423002 0.44 0.658
## Race1,3 0.002638 0.438187 0.01 0.995
## Race1,6 0.266748 0.299788 0.89 0.375
## Race2 0.012754 0.085725 0.15 0.882
## Race2,7 0.149420 0.423400 0.35 0.725
## Race3 -0.192364 0.106084 -1.81 0.071 .
## Race5 0.288209 0.426328 0.68 0.500
## Race6 0.148364 0.422806 0.35 0.726
## Race7 0.190193 0.213970 0.89 0.375
## morality"Benevolence" -0.107854 0.145919 -0.74 0.461
## morality"Hedonism" -0.273759 0.271391 -1.01 0.314
## morality"Power" -0.235638 0.267646 -0.88 0.380
## morality"Security" -0.123842 0.123546 -1.00 0.317
## morality"Self-direction" -0.221821 0.130402 -1.70 0.090 .
## morality"Stimulation" -0.236901 0.177402 -1.34 0.183
## morality"Tradition" -0.145129 0.170174 -0.85 0.395
## morality"Universalism" -0.246820 0.136061 -1.81 0.071 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.42 on 206 degrees of freedom
## Multiple R-squared: 0.0853, Adjusted R-squared: -0.0124
## F-statistic: 0.873 on 22 and 206 DF, p-value: 0.63
##
##
## $Admin
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8799 -0.5773 0.0096 0.6545 2.1239
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.57371 0.40393 11.32 <0.0000000000000002 ***
## cond_broad2. Relative 0.28520 0.19948 1.43 0.1543
## cond_broad3. Low -0.00214 0.19709 -0.01 0.9914
## gender 0.05558 0.12392 0.45 0.6543
## Age 0.01527 0.00549 2.78 0.0059 **
## Race1,2 1.07595 0.59816 1.80 0.0735 .
## Race1,2,5 -0.21873 1.00726 -0.22 0.8283
## Race1,3 1.05795 1.04342 1.01 0.3118
## Race1,6 0.80595 0.71386 1.13 0.2602
## Race2 0.37891 0.20413 1.86 0.0648 .
## Race2,7 -0.40792 1.00821 -0.40 0.6862
## Race3 -0.10284 0.25261 -0.41 0.6844
## Race5 -0.37587 1.01518 -0.37 0.7116
## Race6 0.87962 1.00679 0.87 0.3833
## Race7 0.64665 0.50951 1.27 0.2058
## morality"Benevolence" -0.15401 0.34746 -0.44 0.6581
## morality"Hedonism" -0.10574 0.64624 -0.16 0.8702
## morality"Power" -0.86123 0.63732 -1.35 0.1781
## morality"Security" -0.21753 0.29419 -0.74 0.4605
## morality"Self-direction" -0.37147 0.31052 -1.20 0.2330
## morality"Stimulation" -0.04124 0.42243 -0.10 0.9223
## morality"Tradition" -1.00631 0.40522 -2.48 0.0138 *
## morality"Universalism" -0.51313 0.32399 -1.58 0.1148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.99 on 206 degrees of freedom
## Multiple R-squared: 0.141, Adjusted R-squared: 0.0492
## F-statistic: 1.54 on 22 and 206 DF, p-value: 0.0649
##
##
## $`Admin-Block`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.282 -0.978 0.101 1.206 2.993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0768 0.6840 5.96 0.000000011 ***
## cond_broad2. Relative 0.5789 0.3378 1.71 0.0881 .
## cond_broad3. Low 0.2623 0.3337 0.79 0.4328
## gender -0.0205 0.2098 -0.10 0.9222
## Age 0.0205 0.0093 2.20 0.0288 *
## Race1,2 1.9093 1.0129 1.89 0.0608 .
## Race1,2,5 1.7475 1.7057 1.02 0.3068
## Race1,3 1.9757 1.7669 1.12 0.2648
## Race1,6 1.6263 1.2088 1.35 0.1800
## Race2 0.7152 0.3457 2.07 0.0398 *
## Race2,7 -0.7405 1.7073 -0.43 0.6649
## Race3 -0.1911 0.4278 -0.45 0.6556
## Race5 -0.6216 1.7191 -0.36 0.7180
## Race6 1.1981 1.7049 0.70 0.4830
## Race7 0.9236 0.8628 1.07 0.2857
## morality"Benevolence" -0.2686 0.5884 -0.46 0.6485
## morality"Hedonism" 0.2970 1.0943 0.27 0.7863
## morality"Power" -1.0802 1.0792 -1.00 0.3180
## morality"Security" -0.1515 0.4982 -0.30 0.7613
## morality"Self-direction" -0.8891 0.5258 -1.69 0.0924 .
## morality"Stimulation" -0.2389 0.7153 -0.33 0.7388
## morality"Tradition" -1.8991 0.6862 -2.77 0.0062 **
## morality"Universalism" -0.8737 0.5486 -1.59 0.1128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.7 on 206 degrees of freedom
## Multiple R-squared: 0.155, Adjusted R-squared: 0.0653
## F-statistic: 1.72 on 22 and 206 DF, p-value: 0.0271
##
##
## $`Admin-Kick`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.930 -1.137 0.188 1.283 3.105
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.873351 0.700650 6.96 0.000000000046 ***
## cond_broad2. Relative 0.351453 0.346020 1.02 0.311
## cond_broad3. Low 0.010916 0.341866 0.03 0.975
## gender 0.195748 0.214949 0.91 0.364
## Age 0.000525 0.009526 0.06 0.956
## Race1,2 0.720300 1.037556 0.69 0.488
## Race1,2,5 -1.613740 1.747182 -0.92 0.357
## Race1,3 0.373207 1.809901 0.21 0.837
## Race1,6 2.031717 1.238255 1.64 0.102
## Race2 0.540928 0.354079 1.53 0.128
## Race2,7 0.203104 1.748825 0.12 0.908
## Race3 -0.695385 0.438172 -1.59 0.114
## Race5 -0.543134 1.760921 -0.31 0.758
## Race6 -0.798470 1.746371 -0.46 0.648
## Race7 0.142080 0.883788 0.16 0.872
## morality"Benevolence" -0.578524 0.602709 -0.96 0.338
## morality"Hedonism" -0.579680 1.120962 -0.52 0.606
## morality"Power" -1.448564 1.105492 -1.31 0.192
## morality"Security" -0.494082 0.510297 -0.97 0.334
## morality"Self-direction" -0.900695 0.538618 -1.67 0.096 .
## morality"Stimulation" -0.699360 0.732747 -0.95 0.341
## morality"Tradition" -1.536882 0.702892 -2.19 0.030 *
## morality"Universalism" -0.899454 0.561991 -1.60 0.111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.7 on 206 degrees of freedom
## Multiple R-squared: 0.0932, Adjusted R-squared: -0.00361
## F-statistic: 0.963 on 22 and 206 DF, p-value: 0.513
##
##
## $`Admin-Report`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.87 -1.13 -0.05 1.12 3.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.87457 0.69260 5.59 0.00000007 ***
## cond_broad2. Relative -0.31144 0.34205 -0.91 0.364
## cond_broad3. Low -0.08706 0.33794 -0.26 0.797
## gender -0.30709 0.21248 -1.45 0.150
## Age 0.02294 0.00942 2.44 0.016 *
## Race1,2 2.33627 1.02564 2.28 0.024 *
## Race1,2,5 -3.15890 1.72712 -1.83 0.069 .
## Race1,3 1.59232 1.78912 0.89 0.375
## Race1,6 0.96325 1.22404 0.79 0.432
## Race2 1.42021 0.35001 4.06 0.00007036 ***
## Race2,7 -1.30133 1.72874 -0.75 0.452
## Race3 0.25830 0.43314 0.60 0.552
## Race5 0.99967 1.74070 0.57 0.566
## Race6 0.62986 1.72632 0.36 0.716
## Race7 -0.29269 0.87364 -0.34 0.738
## morality"Benevolence" -0.76815 0.59579 -1.29 0.199
## morality"Hedonism" 1.50834 1.10809 1.36 0.175
## morality"Power" -1.37911 1.09280 -1.26 0.208
## morality"Security" -0.53717 0.50444 -1.06 0.288
## morality"Self-direction" -0.90296 0.53243 -1.70 0.091 .
## morality"Stimulation" -0.64192 0.72433 -0.89 0.377
## morality"Tradition" -1.15285 0.69482 -1.66 0.099 .
## morality"Universalism" -1.21905 0.55554 -2.19 0.029 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.7 on 206 degrees of freedom
## Multiple R-squared: 0.171, Adjusted R-squared: 0.0827
## F-statistic: 1.93 on 22 and 206 DF, p-value: 0.00938
##
##
## $`Admin-Pin`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.487 -0.321 0.284 0.765 2.058
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.69361 0.54811 8.56 0.0000000000000026 ***
## cond_broad2. Relative 0.27347 0.27069 1.01 0.314
## cond_broad3. Low -0.21980 0.26744 -0.82 0.412
## gender 0.19127 0.16815 1.14 0.257
## Age 0.01736 0.00745 2.33 0.021 *
## Race1,2 0.63829 0.81166 0.79 0.433
## Race1,2,5 1.19747 1.36679 0.88 0.382
## Race1,3 1.30315 1.41586 0.92 0.358
## Race1,6 -0.29173 0.96867 -0.30 0.764
## Race2 -0.65283 0.27699 -2.36 0.019 *
## Race2,7 0.42287 1.36808 0.31 0.758
## Race3 0.37076 0.34277 1.08 0.281
## Race5 -0.44794 1.37754 -0.33 0.745
## Race6 1.37079 1.36616 1.00 0.317
## Race7 1.08301 0.69137 1.57 0.119
## morality"Benevolence" 0.71441 0.47149 1.52 0.131
## morality"Hedonism" -1.41418 0.87691 -1.61 0.108
## morality"Power" -0.18296 0.86481 -0.21 0.833
## morality"Security" 0.21729 0.39920 0.54 0.587
## morality"Self-direction" 0.67151 0.42135 1.59 0.113
## morality"Stimulation" 0.99083 0.57322 1.73 0.085 .
## morality"Tradition" -0.23819 0.54986 -0.43 0.665
## morality"Universalism" 0.56041 0.43964 1.27 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 206 degrees of freedom
## Multiple R-squared: 0.174, Adjusted R-squared: 0.0858
## F-statistic: 1.97 on 22 and 206 DF, p-value: 0.00768
##
##
## $`Admin-Upvote`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.178 -0.387 0.201 0.697 2.055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.50672 0.47235 9.54 <0.0000000000000002 ***
## cond_broad2. Relative 0.50798 0.23327 2.18 0.0306 *
## cond_broad3. Low -0.04889 0.23047 -0.21 0.8322
## gender 0.22161 0.14491 1.53 0.1277
## Age 0.02063 0.00642 3.21 0.0015 **
## Race1,2 0.58571 0.69949 0.84 0.4034
## Race1,2,5 -0.01997 1.17789 -0.02 0.9865
## Race1,3 1.12954 1.22017 0.93 0.3557
## Race1,6 0.08805 0.83479 0.11 0.9161
## Race2 -0.49868 0.23871 -2.09 0.0379 *
## Race2,7 0.25347 1.17900 0.21 0.8300
## Race3 -0.07526 0.29540 -0.25 0.7991
## Race5 -0.59285 1.18715 -0.50 0.6180
## Race6 1.19159 1.17734 1.01 0.3127
## Race7 0.94710 0.59582 1.59 0.1135
## morality"Benevolence" 0.53703 0.40633 1.32 0.1877
## morality"Hedonism" 0.48230 0.75571 0.64 0.5241
## morality"Power" 0.69532 0.74529 0.93 0.3519
## morality"Security" 0.24731 0.34403 0.72 0.4730
## morality"Self-direction" 0.60806 0.36312 1.67 0.0955 .
## morality"Stimulation" 0.78733 0.49399 1.59 0.1125
## morality"Tradition" -0.01996 0.47387 -0.04 0.9664
## morality"Universalism" 0.49021 0.37888 1.29 0.1972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 206 degrees of freedom
## Multiple R-squared: 0.174, Adjusted R-squared: 0.0863
## F-statistic: 1.98 on 22 and 206 DF, p-value: 0.00744
##
##
## $`Admin-Monitor`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.542 -0.865 0.280 0.957 2.255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.41722 0.56809 9.54 <0.0000000000000002 ***
## cond_broad2. Relative 0.31082 0.28055 1.11 0.2692
## cond_broad3. Low 0.06968 0.27719 0.25 0.8018
## gender 0.05245 0.17428 0.30 0.7638
## Age 0.00967 0.00772 1.25 0.2121
## Race1,2 0.26579 0.84126 0.32 0.7524
## Race1,2,5 0.53530 1.41663 0.38 0.7059
## Race1,3 -0.02627 1.46748 -0.02 0.9857
## Race1,6 0.41807 1.00398 0.42 0.6775
## Race2 0.74862 0.28709 2.61 0.0098 **
## Race2,7 -1.28514 1.41796 -0.91 0.3658
## Race3 -0.28435 0.35527 -0.80 0.4244
## Race5 -1.04932 1.42776 -0.73 0.4632
## Race6 1.68586 1.41597 1.19 0.2352
## Race7 1.07682 0.71658 1.50 0.1344
## morality"Benevolence" -0.56020 0.48868 -1.15 0.2530
## morality"Hedonism" -0.92825 0.90888 -1.02 0.3083
## morality"Power" -1.77186 0.89634 -1.98 0.0494 *
## morality"Security" -0.58699 0.41375 -1.42 0.1575
## morality"Self-direction" -0.81560 0.43671 -1.87 0.0632 .
## morality"Stimulation" -0.44549 0.59412 -0.75 0.4542
## morality"Tradition" -1.19086 0.56991 -2.09 0.0379 *
## morality"Universalism" -1.13718 0.45567 -2.50 0.0134 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 206 degrees of freedom
## Multiple R-squared: 0.125, Adjusted R-squared: 0.0312
## F-statistic: 1.33 on 22 and 206 DF, p-value: 0.152
##
##
## $`Admin-EncourageOthersReport`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.81 -1.12 0.00 1.28 3.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.03670 0.72857 5.54 0.000000091 ***
## cond_broad2. Relative 0.13213 0.35981 0.37 0.714
## cond_broad3. Low 0.44693 0.35549 1.26 0.210
## gender 0.24125 0.22351 1.08 0.282
## Age 0.01082 0.00991 1.09 0.276
## Race1,2 1.08973 1.07890 1.01 0.314
## Race1,2,5 1.40465 1.81681 0.77 0.440
## Race1,3 1.13233 1.88203 0.60 0.548
## Race1,6 1.21921 1.28760 0.95 0.345
## Race2 0.83484 0.36819 2.27 0.024 *
## Race2,7 0.42303 1.81852 0.23 0.816
## Race3 -0.28427 0.45563 -0.62 0.533
## Race5 1.46350 1.83110 0.80 0.425
## Race6 -0.60943 1.81597 -0.34 0.738
## Race7 0.04344 0.91901 0.05 0.962
## morality"Benevolence" -0.62930 0.62673 -1.00 0.317
## morality"Hedonism" -0.14134 1.16563 -0.12 0.904
## morality"Power" -2.10010 1.14955 -1.83 0.069 .
## morality"Security" -0.70285 0.53063 -1.32 0.187
## morality"Self-direction" -1.31966 0.56008 -2.36 0.019 *
## morality"Stimulation" -0.83979 0.76195 -1.10 0.272
## morality"Tradition" -1.77230 0.73090 -2.42 0.016 *
## morality"Universalism" -1.32792 0.58439 -2.27 0.024 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.8 on 206 degrees of freedom
## Multiple R-squared: 0.111, Adjusted R-squared: 0.0159
## F-statistic: 1.17 on 22 and 206 DF, p-value: 0.28