Load Data
##
## ********************* PROCESS for R Version 4.0.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## PROCESS is now ready for use.
## Copyright 2021 by Andrew F. Hayes ALL RIGHTS RESERVED
## Alphas
##
## Pearson's product-moment correlation
##
## data: vq_1 and vq_2
## t = 34, df = 612, p-value <0.0000000000000002
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.78 0.83
## sample estimates:
## cor
## 0.81Graphs
Main effects
Likert measures
Binary solicitation
##
## Call:
## glm(formula = solicit1 ~ condgraph, family = "binomial", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.362 0.290 8.14 0.00000000000000039 ***
## condgraph0. Half -0.347 0.384 -0.91 0.37
## condgraph1. Control 0.689 0.483 1.43 0.15
## condgraph2. Very similar -0.333 0.383 -0.87 0.39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 367.82 on 610 degrees of freedom
## AIC: 375.8
##
## Number of Fisher Scoring iterations: 5Limiting discussions
##
## Call:
## glm(formula = limitdiscussions ~ condgraph, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.055 0.186 -5.67 0.000000014 ***
## condgraph0. Half -0.198 0.269 -0.74 0.462
## condgraph1. Control -0.594 0.287 -2.07 0.038 *
## condgraph2. Very similar -0.414 0.277 -1.49 0.136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 620.98 on 610 degrees of freedom
## AIC: 629
##
## Number of Fisher Scoring iterations: 4Discouraging suggestions
##
## Call:
## glm(formula = discouragesuggestion ~ condgraph, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.759 0.344 -8.03 0.000000000000001 ***
## condgraph0. Half -0.139 0.500 -0.28 0.78
## condgraph1. Control -0.152 0.500 -0.30 0.76
## condgraph2. Very similar 0.449 0.443 1.01 0.31
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 288.03 on 610 degrees of freedom
## AIC: 296
##
## Number of Fisher Scoring iterations: 5Main effect results
This is a table of posthoc p-values, with the columns as the comparisons between conditions.
Comparing the “very diverse” condition to the “similar” and “no information” conditions.
Prohibitive vs. Promotive voice
If your shift leaders offered suggestions, how would you characterize them?
suggestions that help the unit.
##
## Call:
## glm(formula = prom1 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7132 0.1162 -6.14 0.00000000085 ***
## condgraph0. Half 0.2154 0.1558 1.38 0.17
## condgraph1. Control 0.0886 0.1599 0.55 0.58
## condgraph2. Very similar 0.0765 0.1603 0.48 0.63
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 408.27 on 613 degrees of freedom
## Residual deviance: 406.25 on 610 degrees of freedom
## AIC: 1078
##
## Number of Fisher Scoring iterations: 5suggesting new projects that help the unit.
##
## Call:
## glm(formula = prom2 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.90641 0.12804 -7.08 0.0000000000014 ***
## condgraph0. Half 0.18003 0.17294 1.04 0.30
## condgraph1. Control 0.00612 0.17963 0.03 0.97
## condgraph2. Very similar -0.00988 0.18034 -0.05 0.96
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 446.84 on 613 degrees of freedom
## Residual deviance: 445.14 on 610 degrees of freedom
## AIC: 973.1
##
## Number of Fisher Scoring iterations: 5improving working procedure.
##
## Call:
## glm(formula = prom3 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6106 0.1104 -5.53 0.000000032 ***
## condgraph0. Half 0.1548 0.1500 1.03 0.30
## condgraph1. Control 0.1104 0.1511 0.73 0.46
## condgraph2. Very similar 0.0445 0.1535 0.29 0.77
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 383.47 on 613 degrees of freedom
## Residual deviance: 382.20 on 610 degrees of freedom
## AIC: 1112
##
## Number of Fisher Scoring iterations: 5constructive suggestions that help the unit reach its goals.
##
## Call:
## glm(formula = prom4 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.61056 0.11043 -5.53 0.000000032 ***
## condgraph0. Half 0.12342 0.15111 0.82 0.41
## condgraph1. Control 0.08892 0.15187 0.59 0.56
## condgraph2. Very similar -0.00205 0.15524 -0.01 0.99
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 391.68 on 613 degrees of freedom
## Residual deviance: 390.61 on 610 degrees of freedom
## AIC: 1103
##
## Number of Fisher Scoring iterations: 5constructive suggestions to improve the unit’s operation.
##
## Call:
## glm(formula = prom5 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7688 0.1195 -6.43 0.00000000013 ***
## condgraph0. Half 0.2043 0.1606 1.27 0.20
## condgraph1. Control 0.0692 0.1651 0.42 0.68
## condgraph2. Very similar 0.0561 0.1657 0.34 0.73
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 423.72 on 613 degrees of freedom
## Residual deviance: 421.93 on 610 degrees of freedom
## AIC: 1050
##
## Number of Fisher Scoring iterations: 5advice against undesirable behaviors that would hamper job performance.
##
## Call:
## glm(formula = proh1 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5515 0.1768 -8.78 <0.0000000000000002 ***
## condgraph0. Half -0.0449 0.2520 -0.18 0.86
## condgraph1. Control -0.4008 0.2770 -1.45 0.15
## condgraph2. Very similar -0.0579 0.2520 -0.23 0.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 386.61 on 613 degrees of freedom
## Residual deviance: 383.97 on 610 degrees of freedom
## AIC: 624
##
## Number of Fisher Scoring iterations: 6honest opinions about problems that might cause serious loss to the work unit, even when/though dissenting opinions exist.
##
## Call:
## glm(formula = proh2 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.04699 0.13736 -7.62 0.000000000000025 ***
## condgraph0. Half 0.00553 0.19335 0.03 0.98
## condgraph1. Control -0.12524 0.19925 -0.63 0.53
## condgraph2. Very similar -0.10462 0.19818 -0.53 0.60
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 449.56 on 613 degrees of freedom
## Residual deviance: 448.85 on 610 degrees of freedom
## AIC: 864.8
##
## Number of Fisher Scoring iterations: 5opinions on things that might affect efficiency in the work unit, even if that would embarrass others.
##
## Call:
## glm(formula = proh3 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6161 0.1826 -8.85 <0.0000000000000002 ***
## condgraph0. Half 0.0196 0.2561 0.08 0.94
## condgraph1. Control -0.2085 0.2708 -0.77 0.44
## condgraph2. Very similar -0.3363 0.2807 -1.20 0.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 375.38 on 613 degrees of freedom
## Residual deviance: 373.08 on 610 degrees of freedom
## AIC: 597.1
##
## Number of Fisher Scoring iterations: 6identifying problems, even if that would hamper relationships with other colleagues.
##
## Call:
## glm(formula = proh4 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8818 0.2085 -9.02 <0.0000000000000002 ***
## condgraph0. Half 0.4349 0.2669 1.63 0.10
## condgraph1. Control 0.0164 0.2918 0.06 0.96
## condgraph2. Very similar -0.2713 0.3147 -0.86 0.39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 364.58 on 613 degrees of freedom
## Residual deviance: 357.79 on 610 degrees of freedom
## AIC: 567.8
##
## Number of Fisher Scoring iterations: 6identifying coordination problems in the workplace.
##
## Call:
## glm(formula = proh5 ~ condgraph, family = "poisson", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -27.302585092993076898437720956281 41883.725940553456894122064113616943 0 1
## condgraph0. Half 0.000000000000162743668737362619 59038.645568360792822204530239105225 0 1
## condgraph1. Control -0.000000000000000000000000000637 58849.147147107767523266375064849854 0 1
## condgraph2. Very similar -0.000000000000000000000000000654 58849.147147107134514953941106796265 0 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 0.0000000000000 on 613 degrees of freedom
## Residual deviance: 0.0000000017054 on 610 degrees of freedom
## AIC: 8
##
## Number of Fisher Scoring iterations: 25Moderation
DV: Limit Discussions
##
## Call:
## glm(formula = limitdiscussions ~ condgraph * comp, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0761 0.8961 -0.08 0.93
## condgraph0. Half 0.4438 1.3810 0.32 0.75
## condgraph1. Control 0.4653 1.6079 0.29 0.77
## condgraph2. Very similar 0.7809 1.6651 0.47 0.64
## comp -0.1827 0.1651 -1.11 0.27
## condgraph0. Half:comp -0.1061 0.2492 -0.43 0.67
## condgraph1. Control:comp -0.1799 0.2900 -0.62 0.53
## condgraph2. Very similar:comp -0.1957 0.2954 -0.66 0.51
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 612.74 on 606 degrees of freedom
## AIC: 628.7
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = limitdiscussions ~ condgraph * auto, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.72895 0.82363 -0.89 0.38
## condgraph0. Half -0.85363 1.27415 -0.67 0.50
## condgraph1. Control -1.48662 1.37963 -1.08 0.28
## condgraph2. Very similar -0.40530 1.24646 -0.33 0.75
## auto -0.06564 0.16223 -0.40 0.69
## condgraph0. Half:auto 0.12803 0.24192 0.53 0.60
## condgraph1. Control:auto 0.17213 0.25920 0.66 0.51
## condgraph2. Very similar:auto 0.00147 0.23926 0.01 1.00
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 620.28 on 606 degrees of freedom
## AIC: 636.3
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = limitdiscussions ~ condgraph * emp, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.19694 0.80506 -0.24 0.81
## condgraph0. Half 0.69766 1.34955 0.52 0.61
## condgraph1. Control -0.26179 1.48117 -0.18 0.86
## condgraph2. Very similar -0.00532 1.36636 0.00 1.00
## emp -0.16693 0.15400 -1.08 0.28
## condgraph0. Half:emp -0.15171 0.24950 -0.61 0.54
## condgraph1. Control:emp -0.04366 0.26789 -0.16 0.87
## condgraph2. Very similar:emp -0.06382 0.25252 -0.25 0.80
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 614.96 on 606 degrees of freedom
## AIC: 631
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = limitdiscussions ~ condgraph * personalcontrol,
## family = "binomial", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.37425 0.86254 -0.43 0.66
## condgraph0. Half -0.17752 1.40258 -0.13 0.90
## condgraph1. Control -1.03777 1.54437 -0.67 0.50
## condgraph2. Very similar -0.20007 1.41340 -0.14 0.89
## personalcontrol -0.13471 0.16793 -0.80 0.42
## condgraph0. Half:personalcontrol 0.00472 0.26351 0.02 0.99
## condgraph1. Control:personalcontrol 0.09157 0.28530 0.32 0.75
## condgraph2. Very similar:personalcontrol -0.03245 0.26697 -0.12 0.90
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 619.26 on 606 degrees of freedom
## AIC: 635.3
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = limitdiscussions ~ condgraph * vq, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.5611 1.0044 2.55 0.01077 *
## condgraph0. Half -0.9359 1.4580 -0.64 0.52094
## condgraph1. Control -0.8970 1.7531 -0.51 0.60888
## condgraph2. Very similar -2.1967 1.5535 -1.41 0.15734
## vq -0.6390 0.1771 -3.61 0.00031 ***
## condgraph0. Half:vq 0.1432 0.2544 0.56 0.57361
## condgraph1. Control:vq 0.0953 0.2966 0.32 0.74801
## condgraph2. Very similar:vq 0.3331 0.2653 1.26 0.20916
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 591.06 on 606 degrees of freedom
## AIC: 607.1
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = limitdiscussions ~ condgraph * lto, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.158 1.197 0.97 0.333
## condgraph0. Half 1.346 1.786 0.75 0.451
## condgraph1. Control 0.442 1.882 0.23 0.814
## condgraph2. Very similar 2.690 1.873 1.44 0.151
## lto -0.417 0.226 -1.85 0.065 .
## condgraph0. Half:lto -0.301 0.342 -0.88 0.378
## condgraph1. Control:lto -0.196 0.358 -0.55 0.584
## condgraph2. Very similar:lto -0.611 0.364 -1.68 0.093 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 588.77 on 606 degrees of freedom
## AIC: 604.8
##
## Number of Fisher Scoring iterations: 5##
## Call:
## glm(formula = limitdiscussions ~ condgraph * vs, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.615 1.242 2.11 0.0353 *
## condgraph0. Half -1.044 1.664 -0.63 0.5303
## condgraph1. Control -0.778 1.833 -0.42 0.6712
## condgraph2. Very similar -1.107 1.738 -0.64 0.5243
## vs -0.642 0.218 -2.95 0.0032 **
## condgraph0. Half:vs 0.147 0.292 0.50 0.6159
## condgraph1. Control:vs 0.031 0.323 0.10 0.9237
## condgraph2. Very similar:vs 0.108 0.309 0.35 0.7263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 591.83 on 606 degrees of freedom
## AIC: 607.8
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = limitdiscussions ~ condgraph * difficulty, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.9110 0.6271 -4.64 0.0000034 ***
## condgraph0. Half 0.7462 0.8224 0.91 0.3642
## condgraph1. Control -0.4482 0.9170 -0.49 0.6250
## condgraph2. Very similar -0.0563 0.8360 -0.07 0.9463
## difficulty 0.4451 0.1365 3.26 0.0011 **
## condgraph0. Half:difficulty -0.2061 0.1847 -1.12 0.2646
## condgraph1. Control:difficulty 0.0132 0.2069 0.06 0.9490
## condgraph2. Very similar:difficulty 0.0051 0.1961 0.03 0.9793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 585.06 on 606 degrees of freedom
## AIC: 601.1
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = limitdiscussions ~ condgraph * solicitfreq, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.159 0.802 0.20 0.84
## condgraph0. Half -0.673 1.190 -0.57 0.57
## condgraph1. Control -1.782 1.281 -1.39 0.16
## condgraph2. Very similar 0.426 1.148 0.37 0.71
## solicitfreq -0.336 0.219 -1.53 0.13
## condgraph0. Half:solicitfreq 0.136 0.321 0.42 0.67
## condgraph1. Control:solicitfreq 0.329 0.344 0.96 0.34
## condgraph2. Very similar:solicitfreq -0.266 0.327 -0.81 0.42
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 625.96 on 613 degrees of freedom
## Residual deviance: 611.31 on 606 degrees of freedom
## AIC: 627.3
##
## Number of Fisher Scoring iterations: 4DV: Discourage Suggestions
##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * comp, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.610 1.375 -0.44 0.66
## condgraph0. Half 3.466 2.194 1.58 0.11
## condgraph1. Control -1.938 2.710 -0.72 0.47
## condgraph2. Very similar -3.248 2.569 -1.26 0.21
## comp -0.416 0.271 -1.54 0.12
## condgraph0. Half:comp -0.703 0.448 -1.57 0.12
## condgraph1. Control:comp 0.352 0.489 0.72 0.47
## condgraph2. Very similar:comp 0.678 0.451 1.50 0.13
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 273.81 on 606 degrees of freedom
## AIC: 289.8
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * auto, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.542 1.406 -1.10 0.27
## condgraph0. Half 1.559 2.006 0.78 0.44
## condgraph1. Control -3.942 2.627 -1.50 0.13
## condgraph2. Very similar -1.993 2.030 -0.98 0.33
## auto -0.251 0.291 -0.87 0.39
## condgraph0. Half:auto -0.340 0.419 -0.81 0.42
## condgraph1. Control:auto 0.718 0.480 1.50 0.13
## condgraph2. Very similar:auto 0.480 0.392 1.23 0.22
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 281.09 on 606 degrees of freedom
## AIC: 297.1
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * emp, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.181 1.336 -0.88 0.377
## condgraph0. Half 4.143 2.198 1.89 0.059 .
## condgraph1. Control -3.096 2.725 -1.14 0.256
## condgraph2. Very similar -1.647 2.124 -0.78 0.438
## emp -0.316 0.270 -1.17 0.242
## condgraph0. Half:emp -0.839 0.456 -1.84 0.066 .
## condgraph1. Control:emp 0.551 0.480 1.15 0.251
## condgraph2. Very similar:emp 0.409 0.395 1.03 0.301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 274.93 on 606 degrees of freedom
## AIC: 290.9
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * personalcontrol,
## family = "binomial", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.185 1.435 -0.83 0.41
## condgraph0. Half 3.326 2.233 1.49 0.14
## condgraph1. Control -4.201 2.893 -1.45 0.15
## condgraph2. Very similar -2.238 2.215 -1.01 0.31
## personalcontrol -0.320 0.294 -1.09 0.28
## condgraph0. Half:personalcontrol -0.687 0.466 -1.47 0.14
## condgraph1. Control:personalcontrol 0.757 0.519 1.46 0.14
## condgraph2. Very similar:personalcontrol 0.523 0.420 1.25 0.21
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 277.13 on 606 degrees of freedom
## AIC: 293.1
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * vq, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0442 1.2997 0.03 0.973
## condgraph0. Half 2.4498 2.1152 1.16 0.247
## condgraph1. Control 0.5300 2.5159 0.21 0.833
## condgraph2. Very similar -5.1073 2.6446 -1.93 0.053 .
## vq -0.5114 0.2438 -2.10 0.036 *
## condgraph0. Half:vq -0.4858 0.4111 -1.18 0.237
## condgraph1. Control:vq -0.0677 0.4401 -0.15 0.878
## condgraph2. Very similar:vq 0.9529 0.4338 2.20 0.028 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 269.71 on 606 degrees of freedom
## AIC: 285.7
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * lto, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.312 2.136 -0.15 0.88
## condgraph0. Half 0.684 3.163 0.22 0.83
## condgraph1. Control 4.868 3.344 1.46 0.15
## condgraph2. Very similar 1.895 2.790 0.68 0.50
## lto -0.468 0.415 -1.13 0.26
## condgraph0. Half:lto -0.165 0.621 -0.27 0.79
## condgraph1. Control:lto -1.022 0.686 -1.49 0.14
## condgraph2. Very similar:lto -0.285 0.547 -0.52 0.60
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 270.59 on 606 degrees of freedom
## AIC: 286.6
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * vs, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1357 1.6429 0.08 0.934
## condgraph0. Half -0.7267 2.4925 -0.29 0.771
## condgraph1. Control 0.0156 2.5420 0.01 0.995
## condgraph2. Very similar -0.9818 2.3154 -0.42 0.672
## vs -0.5169 0.2985 -1.73 0.083 .
## condgraph0. Half:vs 0.1075 0.4510 0.24 0.812
## condgraph1. Control:vs -0.0253 0.4607 -0.05 0.956
## condgraph2. Very similar:vs 0.2564 0.4168 0.62 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 280.95 on 606 degrees of freedom
## AIC: 297
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * difficulty,
## family = "binomial", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.0311 1.4167 -4.26 0.000021 ***
## condgraph0. Half 0.0421 1.9748 0.02 0.983
## condgraph1. Control -4.3016 2.8698 -1.50 0.134
## condgraph2. Very similar 2.2852 1.6077 1.42 0.155
## difficulty 0.7095 0.2633 2.69 0.007 **
## condgraph0. Half:difficulty -0.0103 0.3690 -0.03 0.978
## condgraph1. Control:difficulty 0.8788 0.5245 1.68 0.094 .
## condgraph2. Very similar:difficulty -0.2892 0.3216 -0.90 0.368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 241.94 on 606 degrees of freedom
## AIC: 257.9
##
## Number of Fisher Scoring iterations: 7##
## Call:
## glm(formula = discouragesuggestion ~ condgraph * solicitfreq,
## family = "binomial", data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.626 1.395 -1.17 0.244
## condgraph0. Half -0.709 2.142 -0.33 0.741
## condgraph1. Control -3.219 2.325 -1.38 0.166
## condgraph2. Very similar -2.690 1.924 -1.40 0.162
## solicitfreq -0.318 0.390 -0.81 0.416
## condgraph0. Half:solicitfreq 0.165 0.586 0.28 0.778
## condgraph1. Control:solicitfreq 0.822 0.602 1.37 0.172
## condgraph2. Very similar:solicitfreq 0.856 0.513 1.67 0.095 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 290.47 on 613 degrees of freedom
## Residual deviance: 283.24 on 606 degrees of freedom
## AIC: 299.2
##
## Number of Fisher Scoring iterations: 6DV: Solicitation (Binary)
##
## Call:
## glm(formula = solicit1 ~ condgraph * comp, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0842 1.1976 0.07 0.944
## condgraph0. Half 0.4017 1.7701 0.23 0.820
## condgraph1. Control 3.3476 2.8744 1.16 0.244
## condgraph2. Very similar 0.8167 2.0933 0.39 0.696
## comp 0.4401 0.2348 1.87 0.061 .
## condgraph0. Half:comp -0.1661 0.3314 -0.50 0.616
## condgraph1. Control:comp -0.5064 0.5064 -1.00 0.317
## condgraph2. Very similar:comp -0.2446 0.3785 -0.65 0.518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 362.70 on 606 degrees of freedom
## AIC: 378.7
##
## Number of Fisher Scoring iterations: 5##
## Call:
## glm(formula = solicit1 ~ condgraph * auto, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.915 1.184 0.77 0.44
## condgraph0. Half 1.539 1.739 0.89 0.38
## condgraph1. Control 3.875 2.500 1.55 0.12
## condgraph2. Very similar 2.274 1.756 1.30 0.20
## auto 0.300 0.246 1.22 0.22
## condgraph0. Half:auto -0.383 0.340 -1.13 0.26
## condgraph1. Control:auto -0.619 0.459 -1.35 0.18
## condgraph2. Very similar:auto -0.517 0.339 -1.52 0.13
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 364.59 on 606 degrees of freedom
## AIC: 380.6
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = solicit1 ~ condgraph * emp, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9007 1.2451 1.53 0.13
## condgraph0. Half -1.5552 1.8276 -0.85 0.39
## condgraph1. Control 2.0781 2.7540 0.75 0.45
## condgraph2. Very similar 0.3296 1.9046 0.17 0.86
## emp 0.0898 0.2379 0.38 0.71
## condgraph0. Half:emp 0.2153 0.3416 0.63 0.53
## condgraph1. Control:emp -0.2501 0.4780 -0.52 0.60
## condgraph2. Very similar:emp -0.1258 0.3480 -0.36 0.72
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 366.00 on 606 degrees of freedom
## AIC: 382
##
## Number of Fisher Scoring iterations: 5##
## Call:
## glm(formula = solicit1 ~ condgraph * personalcontrol, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.296 1.272 1.02 0.31
## condgraph0. Half 0.106 1.902 0.06 0.96
## condgraph1. Control 3.445 2.846 1.21 0.23
## condgraph2. Very similar 1.635 1.956 0.84 0.40
## personalcontrol 0.214 0.254 0.84 0.40
## condgraph0. Half:personalcontrol -0.100 0.364 -0.28 0.78
## condgraph1. Control:personalcontrol -0.515 0.507 -1.01 0.31
## condgraph2. Very similar:personalcontrol -0.379 0.367 -1.03 0.30
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 366.05 on 606 degrees of freedom
## AIC: 382.1
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = solicit1 ~ condgraph * vq, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.232 1.206 0.19 0.85
## condgraph0. Half -1.151 1.735 -0.66 0.51
## condgraph1. Control 2.105 2.968 0.71 0.48
## condgraph2. Very similar 0.684 1.885 0.36 0.72
## vq 0.381 0.218 1.75 0.08 .
## condgraph0. Half:vq 0.131 0.311 0.42 0.67
## condgraph1. Control:vq -0.266 0.488 -0.54 0.59
## condgraph2. Very similar:vq -0.196 0.324 -0.60 0.55
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 359.10 on 606 degrees of freedom
## AIC: 375.1
##
## Number of Fisher Scoring iterations: 5##
## Call:
## glm(formula = solicit1 ~ condgraph * lto, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8134 1.8506 -0.98 0.327
## condgraph0. Half 0.0720 2.4848 0.03 0.977
## condgraph1. Control 2.7909 3.1137 0.90 0.370
## condgraph2. Very similar -1.9138 2.5279 -0.76 0.449
## lto 0.8122 0.3711 2.19 0.029 *
## condgraph0. Half:lto -0.0864 0.4949 -0.17 0.861
## condgraph1. Control:lto -0.4218 0.6036 -0.70 0.485
## condgraph2. Very similar:lto 0.3174 0.5109 0.62 0.534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 344.41 on 606 degrees of freedom
## AIC: 360.4
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = solicit1 ~ condgraph * vs, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.089 1.494 -0.73 0.466
## condgraph0. Half 1.347 2.030 0.66 0.507
## condgraph1. Control 5.016 3.134 1.60 0.110
## condgraph2. Very similar 1.139 2.077 0.55 0.583
## vs 0.618 0.272 2.27 0.023 *
## condgraph0. Half:vs -0.310 0.364 -0.85 0.395
## condgraph1. Control:vs -0.766 0.533 -1.44 0.150
## condgraph2. Very similar:vs -0.264 0.376 -0.70 0.483
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 359.22 on 606 degrees of freedom
## AIC: 375.2
##
## Number of Fisher Scoring iterations: 5##
## Call:
## glm(formula = solicit1 ~ condgraph * difficulty, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.974 1.250 4.78 0.0000017 ***
## condgraph0. Half -2.535 1.454 -1.74 0.08125 .
## condgraph1. Control -0.726 1.803 -0.40 0.68720
## condgraph2. Very similar -2.094 1.445 -1.45 0.14734
## difficulty -0.782 0.233 -3.35 0.00081 ***
## condgraph0. Half:difficulty 0.425 0.284 1.50 0.13415
## condgraph1. Control:difficulty 0.227 0.361 0.63 0.52869
## condgraph2. Very similar:difficulty 0.251 0.291 0.86 0.38784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 334.20 on 606 degrees of freedom
## AIC: 350.2
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = solicit1 ~ condgraph * solicitfreq, family = "binomial",
## data = censorship_v2_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.4930 1.2959 1.92 0.054 .
## condgraph0. Half -1.1655 1.7163 -0.68 0.497
## condgraph1. Control 0.7315 2.2056 0.33 0.740
## condgraph2. Very similar -3.1445 1.6175 -1.94 0.052 .
## solicitfreq -0.0355 0.3423 -0.10 0.917
## condgraph0. Half:solicitfreq 0.2225 0.4568 0.49 0.626
## condgraph1. Control:solicitfreq -0.0113 0.5808 -0.02 0.984
## condgraph2. Very similar:solicitfreq 0.8440 0.4557 1.85 0.064 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 374.93 on 613 degrees of freedom
## Residual deviance: 359.53 on 606 degrees of freedom
## AIC: 375.5
##
## Number of Fisher Scoring iterations: 5DV: Solicitation (Likert)
##
## Call:
## lm(formula = vs ~ condgraph * comp, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.971 -0.522 0.045 0.584 2.347
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7715 0.3352 11.25 < 0.0000000000000002 ***
## condgraph0. Half -0.8625 0.5061 -1.70 0.089 .
## condgraph1. Control -0.1601 0.5487 -0.29 0.771
## condgraph2. Very similar -1.2390 0.5797 -2.14 0.033 *
## comp 0.3778 0.0607 6.22 0.00000000092 ***
## condgraph0. Half:comp 0.1338 0.0894 1.50 0.135
## condgraph1. Control:comp 0.0154 0.0966 0.16 0.873
## condgraph2. Very similar:comp 0.1678 0.1008 1.67 0.096 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.82 on 606 degrees of freedom
## Multiple R-squared: 0.225, Adjusted R-squared: 0.216
## F-statistic: 25.1 on 7 and 606 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = vs ~ condgraph * auto, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.063 -0.496 0.096 0.626 2.171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.8740 0.3225 15.11 <0.0000000000000002 ***
## condgraph0. Half -0.8340 0.4760 -1.75 0.0802 .
## condgraph1. Control -0.4193 0.4725 -0.89 0.3752
## condgraph2. Very similar -0.3631 0.4614 -0.79 0.4315
## auto 0.1886 0.0630 2.99 0.0029 **
## condgraph0. Half:auto 0.1495 0.0906 1.65 0.0995 .
## condgraph1. Control:auto 0.0772 0.0898 0.86 0.3905
## condgraph2. Very similar:auto 0.0400 0.0880 0.45 0.6494
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.88 on 606 degrees of freedom
## Multiple R-squared: 0.103, Adjusted R-squared: 0.0924
## F-statistic: 9.92 on 7 and 606 DF, p-value: 0.00000000000945##
## Call:
## lm(formula = vs ~ condgraph * emp, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.202 -0.489 0.050 0.573 2.067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3047 0.3075 14.00 < 0.0000000000000002 ***
## condgraph0. Half -1.6116 0.5049 -3.19 0.0015 **
## condgraph1. Control 0.0200 0.5092 0.04 0.9687
## condgraph2. Very similar -0.4828 0.4930 -0.98 0.3278
## emp 0.2906 0.0577 5.04 0.00000062 ***
## condgraph0. Half:emp 0.2695 0.0912 2.95 0.0033 **
## condgraph1. Control:emp -0.0224 0.0907 -0.25 0.8049
## condgraph2. Very similar:emp 0.0489 0.0893 0.55 0.5842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.85 on 606 degrees of freedom
## Multiple R-squared: 0.177, Adjusted R-squared: 0.167
## F-statistic: 18.6 on 7 and 606 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = vs ~ condgraph * personalcontrol, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.749 -0.469 0.063 0.610 2.187
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4153 0.3307 13.35 < 0.0000000000000002 ***
## condgraph0. Half -1.4348 0.5216 -2.75 0.0061 **
## condgraph1. Control -0.3553 0.5244 -0.68 0.4983
## condgraph2. Very similar -0.5235 0.5083 -1.03 0.3034
## personalcontrol 0.2748 0.0635 4.33 0.000018 ***
## condgraph0. Half:personalcontrol 0.2487 0.0969 2.56 0.0106 *
## condgraph1. Control:personalcontrol 0.0522 0.0967 0.54 0.5898
## condgraph2. Very similar:personalcontrol 0.0617 0.0947 0.65 0.5152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.85 on 606 degrees of freedom
## Multiple R-squared: 0.16, Adjusted R-squared: 0.15
## F-statistic: 16.5 on 7 and 606 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = vs ~ condgraph * vq, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.235 -0.424 0.076 0.579 2.914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7267 0.3441 10.83 < 0.0000000000000002 ***
## condgraph0. Half -1.2883 0.5105 -2.52 0.012 *
## condgraph1. Control -0.9003 0.5900 -1.53 0.128
## condgraph2. Very similar -0.6218 0.5349 -1.16 0.246
## vq 0.3595 0.0582 6.18 0.0000000012 ***
## condgraph0. Half:vq 0.2094 0.0854 2.45 0.014 *
## condgraph1. Control:vq 0.1275 0.0959 1.33 0.184
## condgraph2. Very similar:vq 0.0687 0.0883 0.78 0.437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.81 on 606 degrees of freedom
## Multiple R-squared: 0.254, Adjusted R-squared: 0.245
## F-statistic: 29.5 on 7 and 606 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = vs ~ condgraph * lto, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.520 -0.474 0.094 0.655 1.768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4804 0.4600 7.57 0.00000000000014 ***
## condgraph0. Half 0.4662 0.6571 0.71 0.48
## condgraph1. Control 0.5588 0.6656 0.84 0.40
## condgraph2. Very similar 0.1576 0.6476 0.24 0.81
## lto 0.4343 0.0846 5.14 0.00000037809485 ***
## condgraph0. Half:lto -0.0845 0.1210 -0.70 0.49
## condgraph1. Control:lto -0.0992 0.1218 -0.81 0.42
## condgraph2. Very similar:lto -0.0495 0.1189 -0.42 0.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.88 on 606 degrees of freedom
## Multiple R-squared: 0.118, Adjusted R-squared: 0.108
## F-statistic: 11.6 on 7 and 606 DF, p-value: 0.0000000000000793##
## Call:
## lm(formula = vs ~ condgraph * vs, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.82 0.00 0.00 0.00 1.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8146 0.0372 156.2 <0.0000000000000002 ***
## condgraph0. Half -5.8146 0.2262 -25.7 <0.0000000000000002 ***
## condgraph1. Control -5.8146 0.2479 -23.5 <0.0000000000000002 ***
## condgraph2. Very similar -5.8146 0.2354 -24.7 <0.0000000000000002 ***
## vs:condgraph0. Half 1.0000 0.0378 26.4 <0.0000000000000002 ***
## vs:condgraph1. Control 1.0000 0.0414 24.2 <0.0000000000000002 ***
## vs:condgraph2. Very similar 1.0000 0.0402 24.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.46 on 607 degrees of freedom
## Multiple R-squared: 0.759, Adjusted R-squared: 0.757
## F-statistic: 319 on 6 and 607 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = vs ~ condgraph * difficulty, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.412 -0.559 0.112 0.679 1.660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.3350 0.2127 29.78 <0.0000000000000002 ***
## condgraph0. Half -0.5529 0.2838 -1.95 0.0519 .
## condgraph1. Control -0.2113 0.2793 -0.76 0.4497
## condgraph2. Very similar -0.3462 0.2688 -1.29 0.1983
## difficulty -0.1319 0.0504 -2.61 0.0092 **
## condgraph0. Half:difficulty 0.1423 0.0691 2.06 0.0399 *
## condgraph1. Control:difficulty 0.0534 0.0702 0.76 0.4468
## condgraph2. Very similar:difficulty 0.0392 0.0701 0.56 0.5763
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.92 on 606 degrees of freedom
## Multiple R-squared: 0.0246, Adjusted R-squared: 0.0134
## F-statistic: 2.19 on 7 and 606 DF, p-value: 0.0338##
## Call:
## lm(formula = vs ~ condgraph * solicitfreq, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.933 -0.494 0.066 0.582 1.989
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.49114 0.31530 14.24 < 0.0000000000000002 ***
## condgraph0. Half -0.41870 0.45212 -0.93 0.35
## condgraph1. Control 0.49248 0.45057 1.09 0.27
## condgraph2. Very similar -0.09625 0.42703 -0.23 0.82
## solicitfreq 0.36072 0.08372 4.31 0.000019 ***
## condgraph0. Half:solicitfreq 0.10843 0.11921 0.91 0.36
## condgraph1. Control:solicitfreq -0.12335 0.11949 -1.03 0.30
## condgraph2. Very similar:solicitfreq 0.00914 0.11480 0.08 0.94
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.88 on 606 degrees of freedom
## Multiple R-squared: 0.119, Adjusted R-squared: 0.108
## F-statistic: 11.6 on 7 and 606 DF, p-value: 0.0000000000000627##
## Call:
## lm(formula = solicitfreq ~ condgraph * comp, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.873 -0.672 0.146 0.426 1.824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9765 0.3429 8.68 <0.0000000000000002 ***
## condgraph0. Half -0.5344 0.5179 -1.03 0.30
## condgraph1. Control 0.6124 0.5615 1.09 0.28
## condgraph2. Very similar -0.9013 0.5931 -1.52 0.13
## comp 0.1280 0.0621 2.06 0.04 *
## condgraph0. Half:comp 0.0975 0.0915 1.07 0.29
## condgraph1. Control:comp -0.1114 0.0989 -1.13 0.26
## condgraph2. Very similar:comp 0.1261 0.1031 1.22 0.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.84 on 606 degrees of freedom
## Multiple R-squared: 0.0451, Adjusted R-squared: 0.034
## F-statistic: 4.09 on 7 and 606 DF, p-value: 0.000214##
## Call:
## lm(formula = solicitfreq ~ condgraph * auto, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.734 -0.648 0.225 0.406 1.614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5080 0.3115 11.26 <0.0000000000000002 ***
## condgraph0. Half -0.4348 0.4598 -0.95 0.34
## condgraph1. Control 0.5075 0.4564 1.11 0.27
## condgraph2. Very similar -0.2262 0.4456 -0.51 0.61
## auto 0.0323 0.0609 0.53 0.60
## condgraph0. Half:auto 0.0916 0.0875 1.05 0.30
## condgraph1. Control:auto -0.0951 0.0868 -1.10 0.27
## condgraph2. Very similar:auto 0.0198 0.0850 0.23 0.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.85 on 606 degrees of freedom
## Multiple R-squared: 0.0152, Adjusted R-squared: 0.00383
## F-statistic: 1.34 on 7 and 606 DF, p-value: 0.23##
## Call:
## lm(formula = solicitfreq ~ condgraph * emp, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.735 -0.677 0.193 0.403 1.786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2389 0.3054 10.61 <0.0000000000000002 ***
## condgraph0. Half -0.4683 0.5015 -0.93 0.351
## condgraph1. Control 0.5025 0.5057 0.99 0.321
## condgraph2. Very similar -1.2276 0.4897 -2.51 0.012 *
## emp 0.0827 0.0573 1.44 0.149
## condgraph0. Half:emp 0.0883 0.0906 0.97 0.330
## condgraph1. Control:emp -0.0928 0.0901 -1.03 0.303
## condgraph2. Very similar:emp 0.1948 0.0887 2.20 0.028 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.84 on 606 degrees of freedom
## Multiple R-squared: 0.0447, Adjusted R-squared: 0.0336
## F-statistic: 4.05 on 7 and 606 DF, p-value: 0.00024##
## Call:
## lm(formula = solicitfreq ~ condgraph * personalcontrol, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.740 -0.659 0.213 0.397 1.729
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3295 0.3280 10.15 <0.0000000000000002 ***
## condgraph0. Half -0.5469 0.5173 -1.06 0.29
## condgraph1. Control 0.6232 0.5200 1.20 0.23
## condgraph2. Very similar -0.7518 0.5041 -1.49 0.14
## personalcontrol 0.0666 0.0630 1.06 0.29
## condgraph0. Half:personalcontrol 0.1072 0.0961 1.11 0.27
## condgraph1. Control:personalcontrol -0.1155 0.0959 -1.20 0.23
## condgraph2. Very similar:personalcontrol 0.1142 0.0940 1.22 0.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.85 on 606 degrees of freedom
## Multiple R-squared: 0.028, Adjusted R-squared: 0.0168
## F-statistic: 2.5 on 7 and 606 DF, p-value: 0.0156##
## Call:
## lm(formula = solicitfreq ~ condgraph * vq, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.680 -0.679 0.256 0.405 1.709
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3391 0.3599 9.28 <0.0000000000000002 ***
## condgraph0. Half -0.9423 0.5340 -1.76 0.078 .
## condgraph1. Control 0.2017 0.6171 0.33 0.744
## condgraph2. Very similar -1.0385 0.5596 -1.86 0.064 .
## vq 0.0568 0.0608 0.93 0.351
## condgraph0. Half:vq 0.1667 0.0893 1.87 0.062 .
## condgraph1. Control:vq -0.0338 0.1003 -0.34 0.736
## condgraph2. Very similar:vq 0.1494 0.0924 1.62 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.84 on 606 degrees of freedom
## Multiple R-squared: 0.0395, Adjusted R-squared: 0.0284
## F-statistic: 3.56 on 7 and 606 DF, p-value: 0.000925##
## Call:
## lm(formula = solicitfreq ~ condgraph * lto, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.727 -0.684 0.275 0.343 1.652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7538 0.4487 8.37 0.00000000000000041 ***
## condgraph0. Half -0.0497 0.6410 -0.08 0.938
## condgraph1. Control -0.1243 0.6493 -0.19 0.848
## condgraph2. Very similar -1.0976 0.6317 -1.74 0.083 .
## lto -0.0158 0.0825 -0.19 0.848
## condgraph0. Half:lto 0.0198 0.1181 0.17 0.867
## condgraph1. Control:lto 0.0258 0.1188 0.22 0.828
## condgraph2. Very similar:lto 0.1827 0.1160 1.57 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.85 on 606 degrees of freedom
## Multiple R-squared: 0.0124, Adjusted R-squared: 0.00101
## F-statistic: 1.09 on 7 and 606 DF, p-value: 0.369##
## Call:
## lm(formula = solicitfreq ~ condgraph * vs, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7868 -0.6162 0.0518 0.5065 1.8744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8622 0.4216 4.42 0.000012 ***
## condgraph0. Half -0.1221 0.5769 -0.21 0.83
## condgraph1. Control 0.6228 0.6039 1.03 0.30
## condgraph2. Very similar -0.3332 0.5882 -0.57 0.57
## vs 0.3107 0.0716 4.34 0.000017 ***
## condgraph0. Half:vs 0.0304 0.0979 0.31 0.76
## condgraph1. Control:vs -0.1061 0.1023 -1.04 0.30
## condgraph2. Very similar:vs 0.0441 0.1008 0.44 0.66
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.81 on 606 degrees of freedom
## Multiple R-squared: 0.119, Adjusted R-squared: 0.109
## F-statistic: 11.7 on 7 and 606 DF, p-value: 0.0000000000000589##
## Call:
## lm(formula = solicitfreq ~ condgraph * difficulty, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.700 -0.669 0.259 0.355 1.721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6292 0.1971 18.41 <0.0000000000000002 ***
## condgraph0. Half -0.0808 0.2630 -0.31 0.76
## condgraph1. Control 0.0929 0.2589 0.36 0.72
## condgraph2. Very similar 0.2043 0.2491 0.82 0.41
## difficulty 0.0100 0.0467 0.21 0.83
## condgraph0. Half:difficulty 0.0384 0.0640 0.60 0.55
## condgraph1. Control:difficulty -0.0213 0.0650 -0.33 0.74
## condgraph2. Very similar:difficulty -0.1025 0.0650 -1.58 0.12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.85 on 606 degrees of freedom
## Multiple R-squared: 0.0145, Adjusted R-squared: 0.00313
## F-statistic: 1.27 on 7 and 606 DF, p-value: 0.26##
## Call:
## lm(formula = solicitfreq ~ condgraph * solicitfreq, data = censorship_v2_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.67 0.00 0.00 0.00 1.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6689 0.0346 106.2 <0.0000000000000002 ***
## condgraph0. Half -3.6689 0.1609 -22.8 <0.0000000000000002 ***
## condgraph1. Control -3.6689 0.1598 -22.9 <0.0000000000000002 ***
## condgraph2. Very similar -3.6689 0.1438 -25.5 <0.0000000000000002 ***
## solicitfreq:condgraph0. Half 1.0000 0.0412 24.3 <0.0000000000000002 ***
## solicitfreq:condgraph1. Control 1.0000 0.0413 24.2 <0.0000000000000002 ***
## solicitfreq:condgraph2. Very similar 1.0000 0.0381 26.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.42 on 607 degrees of freedom
## Multiple R-squared: 0.756, Adjusted R-squared: 0.753
## F-statistic: 313 on 6 and 607 DF, p-value: <0.0000000000000002Moderation
VS~condgraph*personalcontrol
## SIMPLE SLOPES ANALYSIS
##
## Slope of personalcontrol when condgraph = 2. Very similar:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.34 0.07 4.79 0.00
##
## Slope of personalcontrol when condgraph = 1. Control:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.33 0.07 4.48 0.00
##
## Slope of personalcontrol when condgraph = -1. Very diverse:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.27 0.06 4.33 0.00
##
## Slope of personalcontrol when condgraph = 0. Half:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.52 0.07 7.15 0.00c2_vd_cont <- censorship_v2_clean %>% as.data.frame() %>% filter(cond == "very_diverse" | cond == "noinformation")
c2_vd_sim <- censorship_v2_clean %>% as.data.frame() %>% filter(cond == "very_diverse" | cond == "very_similar")JNplots::jnt_cat(
X = "personalcontrol",
Y = "vs",
m = "condgraph",
data = c2_vd_cont,
plot.full = TRUE
)
## $coeff
## Generalized least squares fit by REML
## Model: Yi ~ Xi * gi
## Data: NULL
## AIC BIC logLik
## 793 812 -391
##
## Coefficients:
## Value Std.Error t-value p-value
## (Intercept) 4.4 0.33 13.3 0.00
## Xi 0.3 0.06 4.3 0.00
## gi1. Control -0.4 0.52 -0.7 0.50
## Xi:gi1. Control 0.1 0.10 0.5 0.59
##
## Correlation:
## (Intr) Xi g1.Cnt
## Xi -0.98
## gi1. Control -0.63 0.62
## Xi:gi1. Control 0.64 -0.66 -0.98
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -4.382 -0.595 0.057 0.761 2.165
##
## Residual standard error: 0.86
## Degrees of freedom: 306 total; 302 residual
##
## $`lower limit in X`
## (Intercept)
## 1.7
##
## $`upper limit in X`
## (Intercept)
## 12
##
## [[4]]JNplots::jnt_cat(
X = "personalcontrol",
Y = "vs",
m = "condgraph",
data = c2_vd_sim,
plot.full = TRUE
)
## $coeff
## Generalized least squares fit by REML
## Model: Yi ~ Xi * gi
## Data: NULL
## AIC BIC logLik
## 800 819 -395
##
## Coefficients:
## Value Std.Error t-value p-value
## (Intercept) 4.4 0.33 13.2 0.00
## Xi 0.3 0.06 4.3 0.00
## gi2. Very similar -0.5 0.51 -1.0 0.31
## Xi:gi2. Very similar 0.1 0.10 0.6 0.52
##
## Correlation:
## (Intr) Xi g2.Vrs
## Xi -0.98
## gi2. Very similar -0.65 0.64
## Xi:gi2. Very similar 0.66 -0.67 -0.98
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -4.264 -0.528 0.073 0.704 2.360
##
## Residual standard error: 0.87
## Degrees of freedom: 306 total; 302 residual
##
## $`lower limit in X`
## (Intercept)
## 4.1
##
## $`upper limit in X`
## (Intercept)
## 13
##
## [[4]]