Power analyses based on below results
How powered are we to detect our tightness effect?
##
## Balanced one-way analysis of variance power calculation
##
## k = 5
## n = 19
## f = 0.37
## sig.level = 0.05
## power = 0.81
##
## NOTE: n is number in each groupHow powered are we to detect our sense of power effect?
##
## Balanced one-way analysis of variance power calculation
##
## k = 5
## n = 19
## f = 0.23
## sig.level = 0.05
## power = 0.38
##
## NOTE: n is number in each groupHow powered are we to detect our “block” effect?
##
## Balanced one-way analysis of variance power calculation
##
## k = 5
## n = 19
## f = 0.2
## sig.level = 0.05
## power = 0.3
##
## NOTE: n is number in each groupStimuli check
I compared these responses to the midpoint (4)
Did participants find the posts offensive?
##
## One Sample t-test
##
## data: exp1_5condclean$check_1
## t = 7, df = 169, p-value = 0.00000000004
## alternative hypothesis: true mean is not equal to 4
## 95 percent confidence interval:
## 4.7 5.2
## sample estimates:
## mean of x
## 4.9Did participants find the posts a violation of Webster Springs’ rules?
##
## One Sample t-test
##
## data: exp1_5condclean$check_3
## t = 17, df = 169, p-value <0.0000000000000002
## alternative hypothesis: true mean is not equal to 4
## 95 percent confidence interval:
## 5.6 6.0
## sample estimates:
## mean of x
## 5.8All 5 conditions
## Some items ( looseness_4 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionMain 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_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.858 0.288 1.43 0.00
## 3.RelD-Out-1.HighD 0.672 0.067 1.28 0.02
## 4.LowD-In-1.HighD 0.772 0.022 1.52 0.04
## 5.LowD-Out-1.HighD 0.931 0.206 1.66 0.00
## 3.RelD-Out-2.RelD-In -0.186 -0.780 0.41 0.91
## 4.LowD-In-2.RelD-In -0.086 -0.827 0.65 1.00
## 5.LowD-Out-2.RelD-In 0.073 -0.642 0.79 1.00
## 4.LowD-In-3.RelD-Out 0.100 -0.668 0.87 1.00
## 5.LowD-Out-3.RelD-Out 0.259 -0.484 1.00 0.87
## 5.LowD-Out-4.LowD-In 0.159 -0.706 1.02 0.99
##
##
## $Status
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.010 -0.75 0.77 1.00
## 3.RelD-Out-1.HighD 0.175 -0.63 0.98 0.97
## 4.LowD-In-1.HighD 0.081 -0.91 1.08 1.00
## 5.LowD-Out-1.HighD -0.068 -1.03 0.89 1.00
## 3.RelD-Out-2.RelD-In 0.165 -0.62 0.95 0.98
## 4.LowD-In-2.RelD-In 0.070 -0.91 1.05 1.00
## 5.LowD-Out-2.RelD-In -0.078 -1.03 0.87 1.00
## 4.LowD-In-3.RelD-Out -0.095 -1.11 0.92 1.00
## 5.LowD-Out-3.RelD-Out -0.243 -1.23 0.74 0.96
## 5.LowD-Out-4.LowD-In -0.148 -1.30 1.00 1.00
##
##
## $Domin.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.188 -0.57 0.95 0.96
## 3.RelD-Out-1.HighD 0.139 -0.67 0.95 0.99
## 4.LowD-In-1.HighD -0.124 -1.13 0.88 1.00
## 5.LowD-Out-1.HighD 0.476 -0.49 1.44 0.66
## 3.RelD-Out-2.RelD-In -0.049 -0.84 0.74 1.00
## 4.LowD-In-2.RelD-In -0.312 -1.30 0.68 0.91
## 5.LowD-Out-2.RelD-In 0.288 -0.67 1.24 0.92
## 4.LowD-In-3.RelD-Out -0.263 -1.29 0.76 0.95
## 5.LowD-Out-3.RelD-Out 0.337 -0.66 1.33 0.88
## 5.LowD-Out-4.LowD-In 0.600 -0.56 1.76 0.61
##
##
## $Monitor
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.528 -0.36 1.42 0.47
## 3.RelD-Out-1.HighD 0.157 -0.78 1.10 0.99
## 4.LowD-In-1.HighD 0.394 -0.77 1.56 0.88
## 5.LowD-Out-1.HighD 0.603 -0.52 1.73 0.58
## 3.RelD-Out-2.RelD-In -0.372 -1.29 0.55 0.80
## 4.LowD-In-2.RelD-In -0.135 -1.29 1.02 1.00
## 5.LowD-Out-2.RelD-In 0.074 -1.04 1.19 1.00
## 4.LowD-In-3.RelD-Out 0.237 -0.96 1.43 0.98
## 5.LowD-Out-3.RelD-Out 0.446 -0.71 1.60 0.82
## 5.LowD-Out-4.LowD-In 0.209 -1.14 1.55 0.99
##
##
## $SOP
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD -0.155 -0.85 0.539 0.97
## 3.RelD-Out-1.HighD 0.183 -0.55 0.920 0.96
## 4.LowD-In-1.HighD -0.828 -1.74 0.085 0.10
## 5.LowD-Out-1.HighD -0.141 -1.02 0.741 0.99
## 3.RelD-Out-2.RelD-In 0.339 -0.38 1.061 0.70
## 4.LowD-In-2.RelD-In -0.673 -1.57 0.228 0.24
## 5.LowD-Out-2.RelD-In 0.014 -0.86 0.884 1.00
## 4.LowD-In-3.RelD-Out -1.011 -1.95 -0.077 0.03
## 5.LowD-Out-3.RelD-Out -0.324 -1.23 0.580 0.86
## 5.LowD-Out-4.LowD-In 0.687 -0.37 1.740 0.38
##
##
## $Moraliz.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD -0.027 -0.85 0.79 1.00
## 3.RelD-Out-1.HighD 0.108 -0.76 0.98 1.00
## 4.LowD-In-1.HighD 0.325 -0.76 1.41 0.92
## 5.LowD-Out-1.HighD 0.209 -0.84 1.25 0.98
## 3.RelD-Out-2.RelD-In 0.135 -0.72 0.99 0.99
## 4.LowD-In-2.RelD-In 0.353 -0.72 1.42 0.89
## 5.LowD-Out-2.RelD-In 0.237 -0.79 1.27 0.97
## 4.LowD-In-3.RelD-Out 0.217 -0.89 1.32 0.98
## 5.LowD-Out-3.RelD-Out 0.101 -0.97 1.17 1.00
## 5.LowD-Out-4.LowD-In -0.116 -1.36 1.13 1.00
##
##
## $Affil.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD -0.254 -0.98 0.47 0.87
## 3.RelD-Out-1.HighD 0.218 -0.55 0.99 0.94
## 4.LowD-In-1.HighD 0.086 -0.87 1.04 1.00
## 5.LowD-Out-1.HighD 0.050 -0.87 0.97 1.00
## 3.RelD-Out-2.RelD-In 0.471 -0.28 1.23 0.42
## 4.LowD-In-2.RelD-In 0.340 -0.60 1.28 0.86
## 5.LowD-Out-2.RelD-In 0.304 -0.60 1.21 0.89
## 4.LowD-In-3.RelD-Out -0.132 -1.11 0.84 1.00
## 5.LowD-Out-3.RelD-Out -0.168 -1.11 0.78 0.99
## 5.LowD-Out-4.LowD-In -0.036 -1.14 1.06 1.00
##
##
## $`Rep. Police`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.083 -0.18 0.35 0.91
## 3.RelD-Out-1.HighD 0.013 -0.27 0.29 1.00
## 4.LowD-In-1.HighD -0.039 -0.39 0.31 1.00
## 5.LowD-Out-1.HighD 0.179 -0.16 0.51 0.58
## 3.RelD-Out-2.RelD-In -0.070 -0.34 0.20 0.95
## 4.LowD-In-2.RelD-In -0.123 -0.47 0.22 0.86
## 5.LowD-Out-2.RelD-In 0.095 -0.24 0.43 0.93
## 4.LowD-In-3.RelD-Out -0.053 -0.41 0.30 0.99
## 5.LowD-Out-3.RelD-Out 0.165 -0.18 0.51 0.67
## 5.LowD-Out-4.LowD-In 0.218 -0.18 0.62 0.56
##
##
## $`Rep. Admin`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.0985 -0.11 0.31 0.69
## 3.RelD-Out-1.HighD 0.0239 -0.20 0.25 1.00
## 4.LowD-In-1.HighD -0.0287 -0.30 0.25 1.00
## 5.LowD-Out-1.HighD -0.0087 -0.27 0.26 1.00
## 3.RelD-Out-2.RelD-In -0.0746 -0.29 0.14 0.88
## 4.LowD-In-2.RelD-In -0.1272 -0.40 0.14 0.69
## 5.LowD-Out-2.RelD-In -0.1071 -0.37 0.15 0.79
## 4.LowD-In-3.RelD-Out -0.0526 -0.33 0.23 0.99
## 5.LowD-Out-3.RelD-Out -0.0326 -0.30 0.24 1.00
## 5.LowD-Out-4.LowD-In 0.0201 -0.30 0.34 1.00
##
##
## $Block
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_label, data = exp1_5condclean)
##
## $cond_label
## diff lwr upr p adj
## 2.RelD-In-1.HighD 0.172 -0.062 0.41 0.26
## 3.RelD-Out-1.HighD 0.081 -0.167 0.33 0.90
## 4.LowD-In-1.HighD 0.108 -0.201 0.42 0.87
## 5.LowD-Out-1.HighD 0.223 -0.075 0.52 0.24
## 3.RelD-Out-2.RelD-In -0.091 -0.335 0.15 0.84
## 4.LowD-In-2.RelD-In -0.065 -0.369 0.24 0.98
## 5.LowD-Out-2.RelD-In 0.051 -0.243 0.34 0.99
## 4.LowD-In-3.RelD-Out 0.026 -0.289 0.34 1.00
## 5.LowD-Out-3.RelD-Out 0.142 -0.164 0.45 0.70
## 5.LowD-Out-4.LowD-In 0.115 -0.240 0.47 0.90Logistic regression
Police
##
## Call:
## glm(formula = report_police ~ cond, family = "binomial", data = exp1_5condclean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.099 0.348 -3.16 0.0016 **
## condlow_in -0.223 0.662 -0.34 0.7360
## condlow_out 0.811 0.562 1.44 0.1489
## condrel_in 0.406 0.464 0.87 0.3818
## condrel_out 0.069 0.507 0.14 0.8917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 205.97 on 169 degrees of freedom
## Residual deviance: 202.63 on 165 degrees of freedom
## AIC: 212.6
##
## Number of Fisher Scoring iterations: 4## (Intercept) condlow_in condlow_out condrel_in condrel_out
## 0.33 0.80 2.25 1.50 1.07Admin
##
## Call:
## glm(formula = report_admin ~ cond, family = "binomial", data = exp1_5condclean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.5041 0.3909 3.85 0.00012 ***
## condlow_in -0.1823 0.6852 -0.27 0.79016
## condlow_out -0.0572 0.6794 -0.08 0.93295
## condrel_in 0.8938 0.6523 1.37 0.17061
## condrel_out 0.1699 0.5922 0.29 0.77419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 145.44 on 169 degrees of freedom
## Residual deviance: 142.42 on 165 degrees of freedom
## AIC: 152.4
##
## Number of Fisher Scoring iterations: 5## (Intercept) condlow_in condlow_out condrel_in condrel_out
## 4.50 0.83 0.94 2.44 1.19Block
##
## Call:
## glm(formula = block ~ cond, family = "binomial", data = exp1_5condclean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.762 0.324 2.35 0.019 *
## condlow_in 0.560 0.649 0.86 0.389
## condlow_out 1.489 0.811 1.84 0.066 .
## condrel_in 1.006 0.522 1.93 0.054 .
## condrel_out 0.408 0.500 0.82 0.415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.54 on 169 degrees of freedom
## Residual deviance: 169.29 on 165 degrees of freedom
## AIC: 179.3
##
## Number of Fisher Scoring iterations: 4## (Intercept) condlow_in condlow_out condrel_in condrel_out
## 2.1 1.7 4.4 2.7 1.5Graphs
Full scale measures
Single Item measures
Controls
Full list
## $Tightness
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7736 -0.6267 0.0637 0.5595 2.2550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.96899 1.13039 4.40 0.000021 ***
## cond_label2.RelD-In 0.88139 0.21939 4.02 0.000094 ***
## cond_label3.RelD-Out 0.63117 0.23164 2.72 0.0072 **
## cond_label4.LowD-In 0.80570 0.29040 2.77 0.0063 **
## cond_label5.LowD-Out 0.92031 0.28736 3.20 0.0017 **
## gender -0.23715 0.14597 -1.62 0.1064
## Age -0.00946 0.00661 -1.43 0.1548
## Race1,2 -0.07765 0.73307 -0.11 0.9158
## Race1,3 0.53938 0.59632 0.90 0.3672
## Race1,5 -0.38984 0.74189 -0.53 0.6001
## Race1,7 0.13786 0.73030 0.19 0.8505
## Race2 -0.08877 0.28777 -0.31 0.7582
## Race3 0.12948 0.36691 0.35 0.7247
## morality"Achievement" -1.09158 1.05054 -1.04 0.3005
## morality"Benevolence" -1.35804 1.03246 -1.32 0.1905
## morality"Conformity" -1.09041 1.15427 -0.94 0.3464
## morality"Hedonism" -2.28952 1.26917 -1.80 0.0733 .
## morality"Power" -0.43717 1.24801 -0.35 0.7266
## morality"Security" -1.34492 1.02500 -1.31 0.1916
## morality"Self-direction" -1.44916 1.03187 -1.40 0.1623
## morality"Stimulation" -0.81530 1.10102 -0.74 0.4602
## morality"Tradition" -0.93262 1.06175 -0.88 0.3812
## morality"Universalism" -1.19210 1.05083 -1.13 0.2585
## check_1 0.02412 0.05814 0.41 0.6788
## check_3 0.08828 0.07002 1.26 0.2094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.99 on 145 degrees of freedom
## Multiple R-squared: 0.225, Adjusted R-squared: 0.0971
## F-statistic: 1.76 on 24 and 145 DF, p-value: 0.0229
##
##
## $Status
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.682 -0.771 -0.105 0.389 3.175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.85706 1.36939 5.74 0.000000054 ***
## cond_label2.RelD-In 0.24188 0.26577 0.91 0.3643
## cond_label3.RelD-Out 0.30621 0.28061 1.09 0.2770
## cond_label4.LowD-In 0.36396 0.35180 1.03 0.3026
## cond_label5.LowD-Out -0.22289 0.34812 -0.64 0.5230
## gender -0.02424 0.17683 -0.14 0.8912
## Age 0.00154 0.00801 0.19 0.8476
## Race1,2 -1.21781 0.88807 -1.37 0.1724
## Race1,3 -1.09110 0.72240 -1.51 0.1331
## Race1,5 0.64467 0.89875 0.72 0.4743
## Race1,7 -0.74282 0.88471 -0.84 0.4025
## Race2 0.71312 0.34861 2.05 0.0426 *
## Race3 -0.21004 0.44449 -0.47 0.6373
## morality"Achievement" -3.37291 1.27266 -2.65 0.0089 **
## morality"Benevolence" -4.06633 1.25076 -3.25 0.0014 **
## morality"Conformity" -3.24637 1.39832 -2.32 0.0216 *
## morality"Hedonism" -4.11676 1.53752 -2.68 0.0083 **
## morality"Power" -2.84489 1.51188 -1.88 0.0619 .
## morality"Security" -3.67479 1.24171 -2.96 0.0036 **
## morality"Self-direction" -3.84314 1.25004 -3.07 0.0025 **
## morality"Stimulation" -4.13571 1.33382 -3.10 0.0023 **
## morality"Tradition" -3.49405 1.28624 -2.72 0.0074 **
## morality"Universalism" -3.93602 1.27301 -3.09 0.0024 **
## check_1 0.00486 0.07043 0.07 0.9451
## check_3 -0.34716 0.08483 -4.09 0.000070532 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 145 degrees of freedom
## Multiple R-squared: 0.269, Adjusted R-squared: 0.148
## F-statistic: 2.23 on 24 and 145 DF, p-value: 0.00202
##
##
## $Domin.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.967 -0.831 -0.016 0.821 3.686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.76545 1.42354 4.05 0.000083 ***
## cond_label2.RelD-In 0.17288 0.27628 0.63 0.53246
## cond_label3.RelD-Out 0.17623 0.29171 0.60 0.54670
## cond_label4.LowD-In -0.08298 0.36572 -0.23 0.82081
## cond_label5.LowD-Out 0.36724 0.36189 1.01 0.31190
## gender -0.05202 0.18382 -0.28 0.77759
## Age -0.00412 0.00833 -0.50 0.62113
## Race1,2 -1.81646 0.92318 -1.97 0.05102 .
## Race1,3 -0.41280 0.75097 -0.55 0.58338
## Race1,5 -1.40706 0.93429 -1.51 0.13424
## Race1,7 -0.40607 0.91970 -0.44 0.65949
## Race2 -0.27645 0.36239 -0.76 0.44679
## Race3 -0.66042 0.46206 -1.43 0.15507
## morality"Achievement" -3.13428 1.32299 -2.37 0.01915 *
## morality"Benevolence" -3.45176 1.30022 -2.65 0.00882 **
## morality"Conformity" -3.14997 1.45361 -2.17 0.03187 *
## morality"Hedonism" -3.73846 1.59832 -2.34 0.02070 *
## morality"Power" -2.24041 1.57167 -1.43 0.15616
## morality"Security" -3.50078 1.29082 -2.71 0.00750 **
## morality"Self-direction" -3.57719 1.29947 -2.75 0.00666 **
## morality"Stimulation" -3.50327 1.38656 -2.53 0.01259 *
## morality"Tradition" -3.11821 1.33710 -2.33 0.02107 *
## morality"Universalism" -3.19898 1.32335 -2.42 0.01688 *
## check_1 0.25779 0.07322 3.52 0.00058 ***
## check_3 -0.07737 0.08818 -0.88 0.38171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 145 degrees of freedom
## Multiple R-squared: 0.231, Adjusted R-squared: 0.104
## F-statistic: 1.81 on 24 and 145 DF, p-value: 0.0173
##
##
## $Monitor
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1415 -0.9501 0.0872 1.1032 2.9304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.27381 1.71537 3.07 0.0025 **
## cond_label2.RelD-In 0.40718 0.33292 1.22 0.2233
## cond_label3.RelD-Out 0.01108 0.35151 0.03 0.9749
## cond_label4.LowD-In 0.32868 0.44069 0.75 0.4570
## cond_label5.LowD-Out 0.67251 0.43608 1.54 0.1252
## gender -0.00253 0.22151 -0.01 0.9909
## Age 0.00483 0.01003 0.48 0.6307
## Race1,2 1.14626 1.11244 1.03 0.3045
## Race1,3 0.50672 0.90492 0.56 0.5764
## Race1,5 -1.19988 1.12582 -1.07 0.2883
## Race1,7 2.41989 1.10824 2.18 0.0306 *
## Race2 -0.27852 0.43668 -0.64 0.5246
## Race3 0.86809 0.55679 1.56 0.1212
## morality"Achievement" -2.89954 1.59420 -1.82 0.0710 .
## morality"Benevolence" -3.02211 1.56676 -1.93 0.0557 .
## morality"Conformity" -3.30943 1.75160 -1.89 0.0608 .
## morality"Hedonism" -4.42805 1.92597 -2.30 0.0229 *
## morality"Power" -0.91294 1.89386 -0.48 0.6305
## morality"Security" -2.97776 1.55543 -1.91 0.0575 .
## morality"Self-direction" -3.13285 1.56586 -2.00 0.0473 *
## morality"Stimulation" -2.61092 1.67081 -1.56 0.1203
## morality"Tradition" -3.09287 1.61121 -1.92 0.0569 .
## morality"Universalism" -3.54889 1.59464 -2.23 0.0276 *
## check_1 0.05230 0.08823 0.59 0.5542
## check_3 0.16752 0.10626 1.58 0.1171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 145 degrees of freedom
## Multiple R-squared: 0.181, Adjusted R-squared: 0.0458
## F-statistic: 1.34 on 24 and 145 DF, p-value: 0.15
##
##
## $SOP
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.539 -0.799 0.049 0.752 3.170
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.95391 1.40354 2.10 0.0371 *
## cond_label2.RelD-In -0.28023 0.27240 -1.03 0.3053
## cond_label3.RelD-Out 0.23021 0.28761 0.80 0.4248
## cond_label4.LowD-In -0.95935 0.36058 -2.66 0.0087 **
## cond_label5.LowD-Out -0.18980 0.35680 -0.53 0.5956
## gender 0.29924 0.18124 1.65 0.1009
## Age 0.00842 0.00821 1.03 0.3069
## Race1,2 0.03995 0.91021 0.04 0.9651
## Race1,3 0.83979 0.74042 1.13 0.2586
## Race1,5 -0.61862 0.92116 -0.67 0.5029
## Race1,7 -0.37801 0.90678 -0.42 0.6774
## Race2 0.21682 0.35730 0.61 0.5449
## Race3 0.11280 0.45557 0.25 0.8048
## morality"Achievement" -0.14251 1.30440 -0.11 0.9132
## morality"Benevolence" 0.10951 1.28195 0.09 0.9320
## morality"Conformity" 0.23216 1.43319 0.16 0.8715
## morality"Hedonism" 1.11009 1.57586 0.70 0.4823
## morality"Power" 0.34761 1.54959 0.22 0.8228
## morality"Security" 0.22224 1.27268 0.17 0.8616
## morality"Self-direction" -0.00198 1.28121 0.00 0.9988
## morality"Stimulation" 0.02744 1.36708 0.02 0.9840
## morality"Tradition" -0.36834 1.31831 -0.28 0.7803
## morality"Universalism" -0.22548 1.30476 -0.17 0.8630
## check_1 0.09464 0.07219 1.31 0.1919
## check_3 -0.00410 0.08694 -0.05 0.9624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 145 degrees of freedom
## Multiple R-squared: 0.133, Adjusted R-squared: -0.0109
## F-statistic: 0.924 on 24 and 145 DF, p-value: 0.569
##
##
## $Moraliz.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.467 -0.647 0.089 0.872 2.667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.00521 1.55425 3.22 0.0016 **
## cond_label2.RelD-In -0.23813 0.30165 -0.79 0.4312
## cond_label3.RelD-Out -0.07901 0.31849 -0.25 0.8044
## cond_label4.LowD-In -0.00951 0.39929 -0.02 0.9810
## cond_label5.LowD-Out -0.07086 0.39512 -0.18 0.8579
## gender 0.01987 0.20070 0.10 0.9213
## Age -0.00456 0.00909 -0.50 0.6167
## Race1,2 0.05368 1.00795 0.05 0.9576
## Race1,3 0.84333 0.81992 1.03 0.3054
## Race1,5 -2.51969 1.02007 -2.47 0.0147 *
## Race1,7 1.23341 1.00414 1.23 0.2213
## Race2 -0.14348 0.39567 -0.36 0.7174
## Race3 0.43469 0.50449 0.86 0.3903
## morality"Achievement" -1.91943 1.44446 -1.33 0.1860
## morality"Benevolence" -1.89961 1.41960 -1.34 0.1829
## morality"Conformity" -1.64089 1.58708 -1.03 0.3029
## morality"Hedonism" -0.79500 1.74507 -0.46 0.6494
## morality"Power" -1.93528 1.71598 -1.13 0.2613
## morality"Security" -2.28238 1.40933 -1.62 0.1075
## morality"Self-direction" -2.05062 1.41878 -1.45 0.1505
## morality"Stimulation" -2.62423 1.51387 -1.73 0.0851 .
## morality"Tradition" -2.18179 1.45987 -1.49 0.1372
## morality"Universalism" -2.42747 1.44486 -1.68 0.0951 .
## check_1 0.17279 0.07994 2.16 0.0323 *
## check_3 0.14473 0.09628 1.50 0.1349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 145 degrees of freedom
## Multiple R-squared: 0.205, Adjusted R-squared: 0.0731
## F-statistic: 1.55 on 24 and 145 DF, p-value: 0.0594
##
##
## $Affil.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.932 -0.703 -0.062 0.504 3.802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.48740 1.18556 8.00 0.00000000000036 ***
## cond_label2.RelD-In -0.06655 0.23009 -0.29 0.77282
## cond_label3.RelD-Out 0.09086 0.24294 0.37 0.70895
## cond_label4.LowD-In 0.43420 0.30458 1.43 0.15613
## cond_label5.LowD-Out -0.13808 0.30139 -0.46 0.64753
## gender -0.20740 0.15309 -1.35 0.17761
## Age 0.01108 0.00693 1.60 0.11234
## Race1,2 -0.65716 0.76885 -0.85 0.39411
## Race1,3 -0.25402 0.62543 -0.41 0.68523
## Race1,5 -0.70001 0.77810 -0.90 0.36980
## Race1,7 -0.24863 0.76595 -0.32 0.74594
## Race2 0.19981 0.30181 0.66 0.50900
## Race3 0.26228 0.38482 0.68 0.49660
## morality"Achievement" -4.24775 1.10182 -3.86 0.00017 ***
## morality"Benevolence" -5.07324 1.08285 -4.69 0.00000638327513 ***
## morality"Conformity" -4.36159 1.21060 -3.60 0.00043 ***
## morality"Hedonism" -6.17673 1.33111 -4.64 0.00000771369567 ***
## morality"Power" -5.31826 1.30892 -4.06 0.00007903543568 ***
## morality"Security" -5.32612 1.07502 -4.95 0.00000199494825 ***
## morality"Self-direction" -4.95341 1.08223 -4.58 0.00001005457409 ***
## morality"Stimulation" -5.09005 1.15476 -4.41 0.00002019024551 ***
## morality"Tradition" -4.55452 1.11357 -4.09 0.00007123865242 ***
## morality"Universalism" -5.35680 1.10212 -4.86 0.00000300771234 ***
## check_1 -0.22034 0.06098 -3.61 0.00042 ***
## check_3 -0.17691 0.07344 -2.41 0.01725 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 145 degrees of freedom
## Multiple R-squared: 0.412, Adjusted R-squared: 0.314
## F-statistic: 4.23 on 24 and 145 DF, p-value: 0.0000000246
##
##
## $`Rep. Police`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.665 -0.295 -0.126 0.350 0.893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36323 0.48825 0.74 0.458
## cond_label2.RelD-In 0.08134 0.09476 0.86 0.392
## cond_label3.RelD-Out 0.01396 0.10005 0.14 0.889
## cond_label4.LowD-In -0.11627 0.12543 -0.93 0.356
## cond_label5.LowD-Out 0.18412 0.12412 1.48 0.140
## gender -0.15296 0.06305 -2.43 0.016 *
## Age 0.00295 0.00286 1.03 0.304
## Race1,2 0.49009 0.31664 1.55 0.124
## Race1,3 -0.01355 0.25757 -0.05 0.958
## Race1,5 -0.30359 0.32044 -0.95 0.345
## Race1,7 0.63289 0.31544 2.01 0.047 *
## Race2 0.13842 0.12429 1.11 0.267
## Race3 -0.05672 0.15848 -0.36 0.721
## morality"Achievement" -0.37327 0.45376 -0.82 0.412
## morality"Benevolence" -0.51288 0.44595 -1.15 0.252
## morality"Conformity" -0.33210 0.49856 -0.67 0.506
## morality"Hedonism" -0.34509 0.54819 -0.63 0.530
## morality"Power" -0.33801 0.53906 -0.63 0.532
## morality"Security" -0.52479 0.44273 -1.19 0.238
## morality"Self-direction" -0.56155 0.44569 -1.26 0.210
## morality"Stimulation" -0.37481 0.47557 -0.79 0.432
## morality"Tradition" -0.84806 0.45860 -1.85 0.066 .
## morality"Universalism" -0.56695 0.45389 -1.25 0.214
## check_1 0.03838 0.02511 1.53 0.129
## check_3 0.05687 0.03024 1.88 0.062 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.43 on 145 degrees of freedom
## Multiple R-squared: 0.246, Adjusted R-squared: 0.122
## F-statistic: 1.98 on 24 and 145 DF, p-value: 0.00759
##
##
## $`Rep. Admin`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0342 -0.0527 0.0534 0.1665 0.6596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19410 0.37153 0.52 0.602
## cond_label2.RelD-In 0.02423 0.07211 0.34 0.737
## cond_label3.RelD-Out -0.02838 0.07613 -0.37 0.710
## cond_label4.LowD-In -0.08989 0.09545 -0.94 0.348
## cond_label5.LowD-Out -0.02095 0.09445 -0.22 0.825
## gender 0.00590 0.04798 0.12 0.902
## Age 0.00172 0.00217 0.79 0.431
## Race1,2 -0.15046 0.24094 -0.62 0.533
## Race1,3 0.17526 0.19600 0.89 0.373
## Race1,5 -0.58301 0.24384 -2.39 0.018 *
## Race1,7 0.15136 0.24003 0.63 0.529
## Race2 -0.03973 0.09458 -0.42 0.675
## Race3 0.26652 0.12059 2.21 0.029 *
## morality"Achievement" -0.01592 0.34529 -0.05 0.963
## morality"Benevolence" 0.00045 0.33935 0.00 0.999
## morality"Conformity" 0.13581 0.37938 0.36 0.721
## morality"Hedonism" -0.62424 0.41715 -1.50 0.137
## morality"Power" 0.18374 0.41019 0.45 0.655
## morality"Security" -0.11568 0.33689 -0.34 0.732
## morality"Self-direction" -0.10405 0.33915 -0.31 0.759
## morality"Stimulation" -0.05925 0.36188 -0.16 0.870
## morality"Tradition" -0.06492 0.34897 -0.19 0.853
## morality"Universalism" -0.18573 0.34538 -0.54 0.592
## check_1 -0.02975 0.01911 -1.56 0.122
## check_3 0.13902 0.02301 6.04 0.000000012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.33 on 145 degrees of freedom
## Multiple R-squared: 0.301, Adjusted R-squared: 0.185
## F-statistic: 2.6 on 24 and 145 DF, p-value: 0.000257
##
##
## $Block
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.954 -0.123 0.107 0.228 0.638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.23476 0.44686 0.53 0.60014
## cond_label2.RelD-In 0.12364 0.08673 1.43 0.15611
## cond_label3.RelD-Out 0.05425 0.09157 0.59 0.55448
## cond_label4.LowD-In 0.00815 0.11480 0.07 0.94353
## cond_label5.LowD-Out 0.31446 0.11360 2.77 0.00637 **
## gender 0.09136 0.05770 1.58 0.11555
## Age 0.00129 0.00261 0.49 0.62168
## Race1,2 0.30008 0.28979 1.04 0.30215
## Race1,3 -0.13934 0.23573 -0.59 0.55539
## Race1,5 -0.38750 0.29328 -1.32 0.18849
## Race1,7 0.13949 0.28870 0.48 0.62970
## Race2 0.04247 0.11376 0.37 0.70946
## Race3 -0.21458 0.14504 -1.48 0.14119
## morality"Achievement" -0.30666 0.41529 -0.74 0.46145
## morality"Benevolence" -0.18167 0.40814 -0.45 0.65690
## morality"Conformity" -0.28462 0.45630 -0.62 0.53377
## morality"Hedonism" -0.50115 0.50172 -1.00 0.31953
## morality"Power" 0.14148 0.49335 0.29 0.77470
## morality"Security" -0.17127 0.40519 -0.42 0.67315
## morality"Self-direction" -0.23936 0.40791 -0.59 0.55825
## morality"Stimulation" -0.07728 0.43525 -0.18 0.85932
## morality"Tradition" -0.24799 0.41972 -0.59 0.55555
## morality"Universalism" -0.27855 0.41541 -0.67 0.50357
## check_1 -0.01741 0.02298 -0.76 0.45003
## check_3 0.09910 0.02768 3.58 0.00047 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.39 on 145 degrees of freedom
## Multiple R-squared: 0.215, Adjusted R-squared: 0.085
## F-statistic: 1.65 on 24 and 145 DF, p-value: 0.0376Moderations
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_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.78 0.34 1.21 0.00
## 3. Low-1. High 0.86 0.35 1.37 0.00
## 3. Low-2. Relative 0.08 -0.37 0.53 0.91
##
##
## $Status
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.0832 -0.49 0.66 0.94
## 3. Low-1. High 0.0027 -0.67 0.68 1.00
## 3. Low-2. Relative -0.0805 -0.67 0.51 0.94
##
##
## $Domin.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.166 -0.41 0.75 0.78
## 3. Low-1. High 0.191 -0.49 0.88 0.79
## 3. Low-2. Relative 0.024 -0.58 0.62 0.99
##
##
## $Monitor
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.36 -0.31 1.04 0.41
## 3. Low-1. High 0.50 -0.29 1.30 0.29
## 3. Low-2. Relative 0.14 -0.56 0.84 0.88
##
##
## $SOP
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High -0.0057 -0.54 0.527 1.00
## 3. Low-1. High -0.4672 -1.10 0.161 0.19
## 3. Low-2. Relative -0.4615 -1.01 0.089 0.12
##
##
## $Moraliz.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.032 -0.59 0.66 0.99
## 3. Low-1. High 0.264 -0.47 1.00 0.67
## 3. Low-2. Relative 0.232 -0.41 0.87 0.67
##
##
## $Affil.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High -0.045 -0.60 0.51 0.98
## 3. Low-1. High 0.067 -0.59 0.72 0.97
## 3. Low-2. Relative 0.112 -0.46 0.68 0.89
##
##
## $`Rep. Police`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.052 -0.15 0.25 0.81
## 3. Low-1. High 0.075 -0.16 0.31 0.74
## 3. Low-2. Relative 0.023 -0.19 0.23 0.96
##
##
## $`Rep. Admin`
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.066 -0.093 0.22 0.59
## 3. Low-1. High -0.018 -0.205 0.17 0.97
## 3. Low-2. Relative -0.084 -0.247 0.08 0.45
##
##
## $Block
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = y ~ cond_broad, data = exp1_5condclean)
##
## $cond_broad
## diff lwr upr p adj
## 2. Relative-1. High 0.132 -0.046 0.31 0.19
## 3. Low-1. High 0.168 -0.042 0.38 0.14
## 3. Low-2. Relative 0.036 -0.148 0.22 0.89Logistic regression
Police
##
## Call:
## glm(formula = report_police ~ cond_broad, family = "binomial",
## data = exp1_5condclean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.099 0.348 -3.16 0.0016 **
## cond_broad2. Relative 0.262 0.420 0.62 0.5321
## cond_broad3. Low 0.368 0.485 0.76 0.4483
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 205.97 on 169 degrees of freedom
## Residual deviance: 205.34 on 167 degrees of freedom
## AIC: 211.3
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_broad2. Relative cond_broad3. Low
## 0.33 1.30 1.44Admin
##
## Call:
## glm(formula = report_admin ~ cond_broad, family = "binomial",
## data = exp1_5condclean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.504 0.391 3.85 0.00012 ***
## cond_broad2. Relative 0.524 0.516 1.02 0.30951
## cond_broad3. Low -0.118 0.556 -0.21 0.83220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 145.44 on 169 degrees of freedom
## Residual deviance: 143.58 on 167 degrees of freedom
## AIC: 149.6
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_broad2. Relative cond_broad3. Low
## 4.50 1.69 0.89Block
##
## Call:
## glm(formula = block ~ cond_broad, family = "binomial", data = exp1_5condclean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.762 0.324 2.35 0.019 *
## cond_broad2. Relative 0.714 0.426 1.68 0.094 .
## cond_broad3. Low 0.972 0.548 1.77 0.076 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.54 on 169 degrees of freedom
## Residual deviance: 171.50 on 167 degrees of freedom
## AIC: 177.5
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_broad2. Relative cond_broad3. Low
## 2.1 2.0 2.6Graphs
Full scale measures
Single Item measures
Controls
Full list
## $Tightness
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8346 -0.5922 0.0124 0.5780 2.3192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.48039 1.04872 5.23 0.00000058 ***
## cond_broad2. Relative 0.79840 0.19271 4.14 0.00005726 ***
## cond_broad3. Low 0.89498 0.23168 3.86 0.00017 ***
## gender -0.19268 0.14433 -1.34 0.18391
## Age -0.00773 0.00639 -1.21 0.22815
## Race1,2 -0.27541 0.72220 -0.38 0.70349
## Race1,3 0.59240 0.59489 1.00 0.32095
## Race1,5 -0.20562 0.73832 -0.28 0.78102
## Race1,7 0.18901 0.72684 0.26 0.79519
## Race2 -0.05469 0.28382 -0.19 0.84746
## Race3 0.11192 0.36259 0.31 0.75801
## morality"Achievement" -1.12452 1.03869 -1.08 0.28072
## morality"Benevolence" -1.41311 1.02827 -1.37 0.17142
## morality"Conformity" -1.17314 1.13076 -1.04 0.30119
## morality"Hedonism" -2.26027 1.24960 -1.81 0.07250 .
## morality"Power" -0.51401 1.23669 -0.42 0.67827
## morality"Security" -1.33906 1.01461 -1.32 0.18893
## morality"Self-direction" -1.52186 1.01750 -1.50 0.13685
## morality"Stimulation" -0.90731 1.08632 -0.84 0.40494
## morality"Tradition" -1.00567 1.05257 -0.96 0.34090
## morality"Universalism" -1.28903 1.04021 -1.24 0.21722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 149 degrees of freedom
## Multiple R-squared: 0.198, Adjusted R-squared: 0.0899
## F-statistic: 1.83 on 20 and 149 DF, p-value: 0.0216
##
##
## $Status
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.446 -0.901 -0.165 0.481 3.719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.723481 1.352371 4.23 0.00004 ***
## cond_broad2. Relative 0.147173 0.248513 0.59 0.5546
## cond_broad3. Low -0.031455 0.298765 -0.11 0.9163
## gender -0.106055 0.186117 -0.57 0.5697
## Age 0.000957 0.008240 0.12 0.9077
## Race1,2 -0.684788 0.931311 -0.74 0.4633
## Race1,3 -1.083537 0.767135 -1.41 0.1599
## Race1,5 0.190504 0.952093 0.20 0.8417
## Race1,7 -0.827058 0.937291 -0.88 0.3790
## Race2 0.695870 0.365996 1.90 0.0592 .
## Race3 -0.146064 0.467581 -0.31 0.7552
## morality"Achievement" -2.981740 1.339440 -2.23 0.0275 *
## morality"Benevolence" -3.719707 1.326001 -2.81 0.0057 **
## morality"Conformity" -2.730312 1.458164 -1.87 0.0631 .
## morality"Hedonism" -4.047383 1.611419 -2.51 0.0131 *
## morality"Power" -2.285991 1.594759 -1.43 0.1538
## morality"Security" -3.414174 1.308385 -2.61 0.0100 **
## morality"Self-direction" -3.413074 1.312112 -2.60 0.0102 *
## morality"Stimulation" -3.648538 1.400853 -2.60 0.0101 *
## morality"Tradition" -3.054955 1.357332 -2.25 0.0259 *
## morality"Universalism" -3.435361 1.341390 -2.56 0.0114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 149 degrees of freedom
## Multiple R-squared: 0.142, Adjusted R-squared: 0.0271
## F-statistic: 1.24 on 20 and 149 DF, p-value: 0.233
##
##
## $Domin.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.271 -0.923 -0.029 0.818 3.951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.81264 1.36634 4.99 0.0000017 ***
## cond_broad2. Relative 0.11299 0.25108 0.45 0.6534
## cond_broad3. Low 0.17146 0.30185 0.57 0.5709
## gender -0.05959 0.18804 -0.32 0.7518
## Age 0.00362 0.00833 0.43 0.6643
## Race1,2 -1.82342 0.94093 -1.94 0.0545 .
## Race1,3 -0.12879 0.77506 -0.17 0.8683
## Race1,5 -1.43225 0.96193 -1.49 0.1386
## Race1,7 0.01960 0.94697 0.02 0.9835
## Race2 -0.13971 0.36978 -0.38 0.7061
## Race3 -0.43929 0.47241 -0.93 0.3539
## morality"Achievement" -3.54393 1.35327 -2.62 0.0097 **
## morality"Benevolence" -3.88901 1.33970 -2.90 0.0043 **
## morality"Conformity" -3.66872 1.47322 -2.49 0.0139 *
## morality"Hedonism" -4.66854 1.62806 -2.87 0.0047 **
## morality"Power" -2.90650 1.61123 -1.80 0.0733 .
## morality"Security" -3.96808 1.32190 -3.00 0.0031 **
## morality"Self-direction" -4.27731 1.32566 -3.23 0.0015 **
## morality"Stimulation" -4.22701 1.41532 -2.99 0.0033 **
## morality"Tradition" -3.58465 1.37135 -2.61 0.0099 **
## morality"Universalism" -3.80880 1.35524 -2.81 0.0056 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 149 degrees of freedom
## Multiple R-squared: 0.147, Adjusted R-squared: 0.033
## F-statistic: 1.29 on 20 and 149 DF, p-value: 0.195
##
##
## $Monitor
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.892 -0.916 0.164 0.957 3.284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.34605 1.60558 3.95 0.00012 ***
## cond_broad2. Relative 0.28255 0.29504 0.96 0.33978
## cond_broad3. Low 0.56230 0.35470 1.59 0.11502
## gender 0.07332 0.22096 0.33 0.74049
## Age 0.00806 0.00978 0.82 0.41154
## Race1,2 0.76587 1.10568 0.69 0.48959
## Race1,3 0.60543 0.91077 0.66 0.50724
## Race1,5 -0.87178 1.13035 -0.77 0.44178
## Race1,7 2.53177 1.11278 2.28 0.02432 *
## Race2 -0.22423 0.43452 -0.52 0.60659
## Race3 0.85808 0.55513 1.55 0.12429
## morality"Achievement" -3.00371 1.59022 -1.89 0.06085 .
## morality"Benevolence" -3.17129 1.57427 -2.01 0.04576 *
## morality"Conformity" -3.49321 1.73118 -2.02 0.04540 *
## morality"Hedonism" -4.45073 1.91313 -2.33 0.02134 *
## morality"Power" -1.14454 1.89335 -0.60 0.54643
## morality"Security" -3.02009 1.55335 -1.94 0.05375 .
## morality"Self-direction" -3.33331 1.55778 -2.14 0.03400 *
## morality"Stimulation" -2.84561 1.66314 -1.71 0.08916 .
## morality"Tradition" -3.27066 1.61147 -2.03 0.04417 *
## morality"Universalism" -3.78792 1.59254 -2.38 0.01865 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 149 degrees of freedom
## Multiple R-squared: 0.137, Adjusted R-squared: 0.021
## F-statistic: 1.18 on 20 and 149 DF, p-value: 0.278
##
##
## $SOP
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.503 -0.738 0.049 0.783 2.728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.89709 1.31605 2.96 0.0036 **
## cond_broad2. Relative -0.07312 0.24184 -0.30 0.7628
## cond_broad3. Low -0.55852 0.29074 -1.92 0.0566 .
## gender 0.24719 0.18112 1.36 0.1744
## Age 0.00966 0.00802 1.20 0.2303
## Race1,2 0.02555 0.90630 0.03 0.9775
## Race1,3 0.88464 0.74653 1.18 0.2379
## Race1,5 -0.74288 0.92652 -0.80 0.4240
## Race1,7 -0.18977 0.91212 -0.21 0.8355
## Race2 0.19510 0.35617 0.55 0.5847
## Race3 0.28550 0.45502 0.63 0.5313
## morality"Achievement" -0.53047 1.30347 -0.41 0.6846
## morality"Benevolence" -0.27509 1.29039 -0.21 0.8315
## morality"Conformity" -0.11956 1.41900 -0.08 0.9330
## morality"Hedonism" 0.42374 1.56814 0.27 0.7874
## morality"Power" -0.32964 1.55193 -0.21 0.8321
## morality"Security" -0.23301 1.27325 -0.18 0.8550
## morality"Self-direction" -0.55863 1.27687 -0.44 0.6624
## morality"Stimulation" -0.52626 1.36323 -0.39 0.7000
## morality"Tradition" -0.74708 1.32088 -0.57 0.5725
## morality"Universalism" -0.71984 1.30537 -0.55 0.5822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 149 degrees of freedom
## Multiple R-squared: 0.0824, Adjusted R-squared: -0.0408
## F-statistic: 0.669 on 20 and 149 DF, p-value: 0.852
##
##
## $Moraliz.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.349 -0.669 0.097 0.888 3.133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.068265 1.492855 4.73 0.0000051 ***
## cond_broad2. Relative -0.127705 0.274329 -0.47 0.642
## cond_broad3. Low 0.036948 0.329801 0.11 0.911
## gender 0.053493 0.205451 0.26 0.795
## Age 0.000161 0.009096 0.02 0.986
## Race1,2 -0.030378 1.028055 -0.03 0.976
## Race1,3 1.008989 0.846825 1.19 0.235
## Race1,5 -2.303887 1.050996 -2.19 0.030 *
## Race1,7 1.595563 1.034657 1.54 0.125
## Race2 0.029545 0.404016 0.07 0.942
## Race3 0.463515 0.516153 0.90 0.371
## morality"Achievement" -2.615759 1.478581 -1.77 0.079 .
## morality"Benevolence" -2.433990 1.463746 -1.66 0.098 .
## morality"Conformity" -2.641758 1.609638 -1.64 0.103
## morality"Hedonism" -1.521250 1.778813 -0.86 0.394
## morality"Power" -2.661112 1.760423 -1.51 0.133
## morality"Security" -2.871345 1.444300 -1.99 0.049 *
## morality"Self-direction" -2.837449 1.448414 -1.96 0.052 .
## morality"Stimulation" -3.486829 1.546375 -2.25 0.026 *
## morality"Tradition" -2.897309 1.498332 -1.93 0.055 .
## morality"Universalism" -3.190005 1.480734 -2.15 0.033 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 149 degrees of freedom
## Multiple R-squared: 0.117, Adjusted R-squared: -0.00145
## F-statistic: 0.988 on 20 and 149 DF, p-value: 0.48
##
##
## $Affil.
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.436 -0.759 -0.027 0.575 3.807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.17167 1.24040 5.78 0.000000042 ***
## cond_broad2. Relative -0.03285 0.22794 -0.14 0.88559
## cond_broad3. Low 0.05048 0.27403 0.18 0.85410
## gender -0.27217 0.17071 -1.59 0.11298
## Age 0.00360 0.00756 0.48 0.63417
## Race1,2 -0.29529 0.85420 -0.35 0.73006
## Race1,3 -0.50836 0.70362 -0.72 0.47112
## Race1,5 -1.03380 0.87326 -1.18 0.23836
## Race1,7 -0.67332 0.85968 -0.78 0.43474
## Race2 0.04133 0.33569 0.12 0.90218
## Race3 0.15073 0.42887 0.35 0.72574
## morality"Achievement" -3.65077 1.22854 -2.97 0.00345 **
## morality"Benevolence" -4.49872 1.21621 -3.70 0.00030 ***
## morality"Conformity" -3.54102 1.33743 -2.65 0.00898 **
## morality"Hedonism" -5.40509 1.47800 -3.66 0.00035 ***
## morality"Power" -4.45736 1.46272 -3.05 0.00273 **
## morality"Security" -4.79397 1.20005 -3.99 0.00010 ***
## morality"Self-direction" -4.10760 1.20347 -3.41 0.00083 ***
## morality"Stimulation" -4.17629 1.28487 -3.25 0.00143 **
## morality"Tradition" -3.87097 1.24495 -3.11 0.00225 **
## morality"Universalism" -4.53101 1.23033 -3.68 0.00032 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 149 degrees of freedom
## Multiple R-squared: 0.225, Adjusted R-squared: 0.121
## F-statistic: 2.17 on 20 and 149 DF, p-value: 0.00465
##
##
## $`Rep. Police`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.590 -0.302 -0.194 0.429 0.961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.90459 0.47114 1.92 0.057 .
## cond_broad2. Relative 0.06442 0.08658 0.74 0.458
## cond_broad3. Low 0.05881 0.10408 0.57 0.573
## gender -0.13452 0.06484 -2.07 0.040 *
## Age 0.00447 0.00287 1.56 0.121
## Race1,2 0.33957 0.32445 1.05 0.297
## Race1,3 0.03488 0.26726 0.13 0.896
## Race1,5 -0.20759 0.33169 -0.63 0.532
## Race1,7 0.70893 0.32654 2.17 0.032 *
## Race2 0.15175 0.12751 1.19 0.236
## Race3 -0.02251 0.16290 -0.14 0.890
## morality"Achievement" -0.46832 0.46664 -1.00 0.317
## morality"Benevolence" -0.63364 0.46196 -1.37 0.172
## morality"Conformity" -0.44079 0.50800 -0.87 0.387
## morality"Hedonism" -0.47866 0.56139 -0.85 0.395
## morality"Power" -0.54533 0.55559 -0.98 0.328
## morality"Security" -0.61483 0.45582 -1.35 0.179
## morality"Self-direction" -0.73150 0.45712 -1.60 0.112
## morality"Stimulation" -0.55497 0.48803 -1.14 0.257
## morality"Tradition" -0.97142 0.47287 -2.05 0.042 *
## morality"Universalism" -0.73884 0.46732 -1.58 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.45 on 149 degrees of freedom
## Multiple R-squared: 0.156, Adjusted R-squared: 0.0423
## F-statistic: 1.37 on 20 and 149 DF, p-value: 0.144
##
##
## $`Rep. Admin`
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0029 0.0186 0.1200 0.1841 0.5428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.85999 0.38567 2.23 0.027 *
## cond_broad2. Relative 0.05529 0.07087 0.78 0.437
## cond_broad3. Low -0.02012 0.08520 -0.24 0.814
## gender 0.04154 0.05308 0.78 0.435
## Age 0.00117 0.00235 0.50 0.620
## Race1,2 -0.33471 0.26559 -1.26 0.210
## Race1,3 0.14479 0.21877 0.66 0.509
## Race1,5 -0.39898 0.27152 -1.47 0.144
## Race1,7 0.13673 0.26729 0.51 0.610
## Race2 -0.03630 0.10437 -0.35 0.728
## Race3 0.20364 0.13334 1.53 0.129
## morality"Achievement" -0.11652 0.38198 -0.31 0.761
## morality"Benevolence" -0.06895 0.37815 -0.18 0.856
## morality"Conformity" -0.01979 0.41584 -0.05 0.962
## morality"Hedonism" -0.52524 0.45954 -1.14 0.255
## morality"Power" 0.08233 0.45479 0.18 0.857
## morality"Security" -0.15050 0.37312 -0.40 0.687
## morality"Self-direction" -0.17258 0.37419 -0.46 0.645
## morality"Stimulation" -0.15019 0.39949 -0.38 0.707
## morality"Tradition" -0.17652 0.38708 -0.46 0.649
## morality"Universalism" -0.29324 0.38253 -0.77 0.445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.37 on 149 degrees of freedom
## Multiple R-squared: 0.0933, Adjusted R-squared: -0.0285
## F-statistic: 0.766 on 20 and 149 DF, p-value: 0.75
##
##
## $Block
##
## Call:
## lm(formula = formula_list[[variable]], data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9996 0.0076 0.1367 0.2473 0.6063
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.71200 0.43303 1.64 0.102
## cond_broad2. Relative 0.12725 0.07957 1.60 0.112
## cond_broad3. Low 0.18739 0.09566 1.96 0.052 .
## gender 0.11629 0.05959 1.95 0.053 .
## Age 0.00120 0.00264 0.46 0.650
## Race1,2 0.09889 0.29820 0.33 0.741
## Race1,3 -0.15286 0.24563 -0.62 0.535
## Race1,5 -0.25542 0.30486 -0.84 0.403
## Race1,7 0.12949 0.30012 0.43 0.667
## Race2 0.02122 0.11719 0.18 0.857
## Race3 -0.22460 0.14972 -1.50 0.136
## morality"Achievement" -0.33680 0.42889 -0.79 0.434
## morality"Benevolence" -0.23746 0.42458 -0.56 0.577
## morality"Conformity" -0.30191 0.46690 -0.65 0.519
## morality"Hedonism" -0.44664 0.51597 -0.87 0.388
## morality"Power" 0.02175 0.51064 0.04 0.966
## morality"Security" -0.18043 0.41894 -0.43 0.667
## morality"Self-direction" -0.29445 0.42013 -0.70 0.484
## morality"Stimulation" -0.13958 0.44855 -0.31 0.756
## morality"Tradition" -0.29962 0.43461 -0.69 0.492
## morality"Universalism" -0.35937 0.42951 -0.84 0.404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.41 on 149 degrees of freedom
## Multiple R-squared: 0.113, Adjusted R-squared: -0.00627
## F-statistic: 0.947 on 20 and 149 DF, p-value: 0.529