Censorship Study

## 
## ********************* 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:  symbol1 and symbol2
## t = 18, df = 536, p-value <0.0000000000000002
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.56 0.67
## sample estimates:
##  cor 
## 0.62
## Some items ( proactive3 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( tightness_3 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

Graphs

Main Effects

Panas - Negative

Panas - Positive

Symbolic Threat

Competence

Autonomy

Empowerment

Proactive

Tightness

Voice Solicitation

Censorship

Censorship - Multicategorical

with(managervig_clean, table(cond, manipmediationrecode))
##               manipmediationrecode
## cond            -1   0   1
##   half         182   0   0
##   very_diverse   0   0 180
##   very_similar   0 176   0

Mediator: PANAS Neg

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : censor              
##     X : manipmediationrecode
##     M : panas_neg           
## 
## Sample size: 538
## 
## Random seed: 524667
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: panas_neg
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.3647    0.1330    1.0366   41.0433    2.0000  535.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    2.2593    0.0755   29.9379    0.0000    2.1111    2.4076
## X1         -0.8377    0.1076   -7.7834    0.0000   -1.0492   -0.6263
## X2          0.0195    0.1070    0.1827    0.8551   -0.1907    0.2298
## 
## *********************************************************************** 
## Outcome Variable: censor
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.4530    0.2052    0.5514   45.9619    3.0000  534.0000    0.0000
## 
## Model: 
##               coeff        se         t         p      LLCI      ULCI
## constant     1.2330    0.0900   13.6961    0.0000    1.0562    1.4099
## X1          -0.0118    0.0828   -0.1424    0.8868   -0.1745    0.1509
## X2          -0.0830    0.0781   -1.0630    0.2882   -0.2363    0.0704
## panas_neg    0.3480    0.0315   11.0363    0.0000    0.2860    0.4099
## 
## *********************************************************************** 
## Bootstrapping progress:
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1   -0.0118    0.0828   -0.1424    0.8868   -0.1745    0.1509
## X2   -0.0830    0.0781   -1.0630    0.2882   -0.2363    0.0704
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0019    0.6446    2.0000  534.0000    0.5253
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    panas_neg    ->    censor
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1   -0.2915    0.0501   -0.3995   -0.1990
## X2    0.0068    0.0423   -0.0784    0.0853
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: PANAS Pos

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : censor              
##     X : manipmediationrecode
##     M : panas_pos           
## 
## Sample size: 538
## 
## Random seed: 66564
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: panas_pos
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.1277    0.0163    1.1964    4.4321    2.0000  535.0000    0.0123
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.0286    0.0811   62.0217    0.0000    4.8693    5.1878
## X1          0.3180    0.1156    2.7502    0.0062    0.0909    0.5452
## X2          0.2703    0.1150    2.3510    0.0191    0.0445    0.4962
## 
## *********************************************************************** 
## Outcome Variable: censor
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2738    0.0750    0.6417   14.4280    3.0000  534.0000    0.0000
## 
## Model: 
##               coeff        se         t         p      LLCI      ULCI
## constant     2.8835    0.1699   16.9683    0.0000    2.5497    3.2173
## X1          -0.2487    0.0853   -2.9157    0.0037   -0.4162   -0.0811
## X2          -0.0297    0.0846   -0.3511    0.7257   -0.1960    0.1366
## panas_pos   -0.1719    0.0317   -5.4279    0.0000   -0.2341   -0.1097
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1   -0.2487    0.0853   -2.9157    0.0037   -0.4162   -0.0811
## X2   -0.0297    0.0846   -0.3511    0.7257   -0.1960    0.1366
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0176    5.0694    2.0000  534.0000    0.0066
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    panas_pos    ->    censor
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1   -0.0547    0.0220   -0.1018   -0.0158
## X2   -0.0465    0.0226   -0.0951   -0.0068
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Symbol

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : censor              
##     X : manipmediationrecode
##     M : symbol              
## 
## Sample size: 538
## 
## Random seed: 593567
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: symbol
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.4570    0.2088    2.0459   70.6008    2.0000  535.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    3.5467    0.1060   33.4519    0.0000    3.3384    3.7550
## X1         -1.4132    0.1512   -9.3456    0.0000   -1.7102   -1.1161
## X2          0.2644    0.1504    1.7585    0.0792   -0.0310    0.5598
## 
## *********************************************************************** 
## Outcome Variable: censor
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.3799    0.1443    0.5936   30.0273    3.0000  534.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    1.3033    0.1004   12.9787    0.0000    1.1060    1.5005
## X1         -0.0180    0.0878   -0.2054    0.8373   -0.1906    0.1545
## X2         -0.1296    0.0812   -1.5950    0.1113   -0.2891    0.0300
## symbol      0.2019    0.0233    8.6683    0.0000    0.1561    0.2476
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1   -0.0180    0.0878   -0.2054    0.8373   -0.1906    0.1545
## X2   -0.1296    0.0812   -1.5950    0.1113   -0.2891    0.0300
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0045    1.4122    2.0000  534.0000    0.2445
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    symbol    ->    censor
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1   -0.2853    0.0483   -0.3833   -0.1948
## X2    0.0534    0.0328   -0.0087    0.1203
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Competence

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : censor              
##     X : manipmediationrecode
##     M : comp                
## 
## Sample size: 538
## 
## Random seed: 170599
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: comp
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2526    0.0638    1.1911   18.2288    2.0000  535.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.5165    0.0809   68.1899    0.0000    5.3576    5.6754
## X1          0.5953    0.1154    5.1592    0.0000    0.3686    0.8219
## X2         -0.0202    0.1147   -0.1760    0.8604   -0.2456    0.2052
## 
## *********************************************************************** 
## Outcome Variable: censor
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2660    0.0708    0.6446   13.5550    3.0000  534.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    2.9293    0.1853   15.8109    0.0000    2.5654    3.2933
## X1         -0.2051    0.0870   -2.3586    0.0187   -0.3760   -0.0343
## X2         -0.0795    0.0844   -0.9420    0.3466   -0.2453    0.0863
## comp       -0.1650    0.0318   -5.1871    0.0000   -0.2275   -0.1025
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1   -0.2051    0.0870   -2.3586    0.0187   -0.3760   -0.0343
## X2   -0.0795    0.0844   -0.9420    0.3466   -0.2453    0.0863
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0098    2.8055    2.0000  534.0000    0.0614
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    comp    ->    censor
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1   -0.0982    0.0256   -0.1529   -0.0531
## X2    0.0033    0.0212   -0.0384    0.0466
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Autonomy

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : censor              
##     X : manipmediationrecode
##     M : auto                
## 
## Sample size: 538
## 
## Random seed: 813005
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: auto
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.1494    0.0223    1.5743    6.1068    2.0000  535.0000    0.0024
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.1850    0.0930   55.7500    0.0000    5.0023    5.3677
## X1          0.4305    0.1326    3.2459    0.0012    0.1700    0.6911
## X2          0.0632    0.1319    0.4789    0.6322   -0.1959    0.3223
## 
## *********************************************************************** 
## Outcome Variable: censor
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2009    0.0404    0.6657    7.4867    3.0000  534.0000    0.0001
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    2.4599    0.1578   15.5864    0.0000    2.1499    2.7699
## X1         -0.2667    0.0871   -3.0623    0.0023   -0.4378   -0.0956
## X2         -0.0708    0.0858   -0.8254    0.4095   -0.2393    0.0977
## auto       -0.0850    0.0281   -3.0228    0.0026   -0.1402   -0.0298
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1   -0.2667    0.0871   -3.0623    0.0023   -0.4378   -0.0956
## X2   -0.0708    0.0858   -0.8254    0.4095   -0.2393    0.0977
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0180    4.9961    2.0000  534.0000    0.0071
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    auto    ->    censor
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1   -0.0366    0.0152   -0.0703   -0.0116
## X2   -0.0054    0.0120   -0.0286    0.0193
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Empowerment

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : censor              
##     X : manipmediationrecode
##     M : emp                 
## 
## Sample size: 538
## 
## Random seed: 235380
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: emp
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.1855    0.0344    1.5294    9.5300    2.0000  535.0000    0.0001
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.1319    0.0917   55.9824    0.0000    4.9518    5.3119
## X1          0.3473    0.1307    2.6564    0.0081    0.0905    0.6041
## X2         -0.2208    0.1300   -1.6981    0.0901   -0.4761    0.0346
## 
## *********************************************************************** 
## Outcome Variable: censor
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2405    0.0578    0.6536   10.9230    3.0000  534.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    2.6548    0.1569   16.9162    0.0000    2.3465    2.9631
## X1         -0.2603    0.0860   -3.0258    0.0026   -0.4293   -0.0913
## X2         -0.1035    0.0852   -1.2148    0.2250   -0.2709    0.0639
## emp        -0.1238    0.0283   -4.3817    0.0000   -0.1794   -0.0683
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1   -0.2603    0.0860   -3.0258    0.0026   -0.4293   -0.0913
## X2   -0.1035    0.0852   -1.2148    0.2250   -0.2709    0.0639
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0163    4.6158    2.0000  534.0000    0.0103
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    emp    ->    censor
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1   -0.0430    0.0181   -0.0808   -0.0115
## X2    0.0273    0.0196   -0.0061    0.0710
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Proactive

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : censor              
##     X : manipmediationrecode
##     M : proactive           
## 
## Sample size: 538
## 
## Random seed: 143914
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: proactive
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.0763    0.0058    0.8480    1.5676    2.0000  535.0000    0.2095
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    4.8166    0.0683   70.5629    0.0000    4.6825    4.9507
## X1          0.1198    0.0974    1.2307    0.2190   -0.0714    0.3111
## X2          0.1660    0.0968    1.7150    0.0869   -0.0241    0.3562
## 
## *********************************************************************** 
## Outcome Variable: censor
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2267    0.0514    0.6581    9.6400    3.0000  534.0000    0.0000
## 
## Model: 
##               coeff        se         t         p      LLCI      ULCI
## constant     2.7401    0.1930   14.1939    0.0000    2.3609    3.1193
## X1          -0.2854    0.0859   -3.3230    0.0010   -0.4541   -0.1167
## X2          -0.0513    0.0855   -0.6003    0.5486   -0.2193    0.1166
## proactive   -0.1497    0.0381   -3.9297    0.0001   -0.2245   -0.0748
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1   -0.2854    0.0859   -3.3230    0.0010   -0.4541   -0.1167
## X2   -0.0513    0.0855   -0.6003    0.5486   -0.2193    0.1166
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0222    6.2512    2.0000  534.0000    0.0021
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    proactive    ->    censor
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1   -0.0179    0.0164   -0.0523    0.0123
## X2   -0.0248    0.0159   -0.0589    0.0032
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Tightness

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : censor              
##     X : manipmediationrecode
##     M : tightness           
## 
## Sample size: 538
## 
## Random seed: 948794
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: tightness
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.0418    0.0017    0.7268    0.4675    2.0000  535.0000    0.6268
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    4.9956    0.0632   79.0519    0.0000    4.8715    5.1197
## X1          0.0624    0.0901    0.6918    0.4894   -0.1147    0.2394
## X2          0.0833    0.0896    0.9293    0.3531   -0.0928    0.2593
## 
## *********************************************************************** 
## Outcome Variable: censor
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2320    0.0538    0.6564   10.1293    3.0000  534.0000    0.0000
## 
## Model: 
##               coeff        se         t         p      LLCI      ULCI
## constant     2.8624    0.2139   13.3848    0.0000    2.4423    3.2825
## X1          -0.2928    0.0857   -3.4170    0.0007   -0.4611   -0.1245
## X2          -0.0621    0.0852   -0.7288    0.4664   -0.2296    0.1053
## tightness   -0.1688    0.0411   -4.1080    0.0000   -0.2495   -0.0881
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1   -0.2928    0.0857   -3.4170    0.0007   -0.4611   -0.1245
## X2   -0.0621    0.0852   -0.7288    0.4664   -0.2296    0.1053
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0229    6.4517    2.0000  534.0000    0.0017
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    tightness    ->    censor
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1   -0.0105    0.0167   -0.0490    0.0179
## X2   -0.0141    0.0174   -0.0554    0.0148
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Voice Solicitation - Multicategorical

Mediator: PANAS Neg

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : vs                  
##     X : manipmediationrecode
##     M : panas_neg           
## 
## Sample size: 538
## 
## Random seed: 294890
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: panas_neg
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.3647    0.1330    1.0366   41.0433    2.0000  535.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    2.2593    0.0755   29.9379    0.0000    2.1111    2.4076
## X1         -0.8377    0.1076   -7.7834    0.0000   -1.0492   -0.6263
## X2          0.0195    0.1070    0.1827    0.8551   -0.1907    0.2298
## 
## *********************************************************************** 
## Outcome Variable: vs
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2054    0.0422    1.0523    7.8398    3.0000  534.0000    0.0000
## 
## Model: 
##               coeff        se         t         p      LLCI      ULCI
## constant     5.9251    0.1244   47.6414    0.0000    5.6808    6.1694
## X1           0.1118    0.1144    0.9775    0.3288   -0.1129    0.3366
## X2           0.1912    0.1078    1.7733    0.0768   -0.0206    0.4030
## panas_neg   -0.1808    0.0436   -4.1517    0.0000   -0.2664   -0.0953
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1    0.1118    0.1144    0.9775    0.3288   -0.1129    0.3366
## X2    0.1912    0.1078    1.7733    0.0768   -0.0206    0.4030
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0057    1.5868    2.0000  534.0000    0.2055
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    panas_neg    ->    vs
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1    0.1515    0.0439    0.0696    0.2423
## X2   -0.0035    0.0226   -0.0501    0.0420
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: PANAS Pos

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : vs                  
##     X : manipmediationrecode
##     M : panas_pos           
## 
## Sample size: 538
## 
## Random seed: 8128
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: panas_pos
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.1277    0.0163    1.1964    4.4321    2.0000  535.0000    0.0123
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.0286    0.0811   62.0217    0.0000    4.8693    5.1878
## X1          0.3180    0.1156    2.7502    0.0062    0.0909    0.5452
## X2          0.2703    0.1150    2.3510    0.0191    0.0445    0.4962
## 
## *********************************************************************** 
## Outcome Variable: vs
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.3668    0.1346    0.9508   27.6749    3.0000  534.0000    0.0000
## 
## Model: 
##               coeff        se         t         p      LLCI      ULCI
## constant     3.8261    0.2068   18.4974    0.0000    3.4198    4.2325
## X1           0.1564    0.1038    1.5070    0.1324   -0.0475    0.3604
## X2           0.0968    0.1030    0.9397    0.3478   -0.1056    0.2992
## panas_pos    0.3362    0.0385    8.7219    0.0000    0.2604    0.4119
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1    0.1564    0.1038    1.5070    0.1324   -0.0475    0.3604
## X2    0.0968    0.1030    0.9397    0.3478   -0.1056    0.2992
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0038    1.1585    2.0000  534.0000    0.3147
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    panas_pos    ->    vs
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1    0.1069    0.0415    0.0321    0.1927
## X2    0.0909    0.0445    0.0121    0.1866
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Symbol

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : vs                  
##     X : manipmediationrecode
##     M : symbol              
## 
## Sample size: 538
## 
## Random seed: 138549
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: symbol
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.4570    0.2088    2.0459   70.6008    2.0000  535.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    3.5467    0.1060   33.4519    0.0000    3.3384    3.7550
## X1         -1.4132    0.1512   -9.3456    0.0000   -1.7102   -1.1161
## X2          0.2644    0.1504    1.7585    0.0792   -0.0310    0.5598
## 
## *********************************************************************** 
## Outcome Variable: vs
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.1434    0.0206    1.0760    3.7381    3.0000  534.0000    0.0112
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.7669    0.1352   42.6555    0.0000    5.5013    6.0325
## X1          0.1636    0.1183    1.3830    0.1672   -0.0688    0.3959
## X2          0.2064    0.1094    1.8870    0.0597   -0.0085    0.4212
## symbol     -0.0706    0.0314   -2.2517    0.0247   -0.1322   -0.0090
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1    0.1636    0.1183    1.3830    0.1672   -0.0688    0.3959
## X2    0.2064    0.1094    1.8870    0.0597   -0.0085    0.4212
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0073    1.9822    2.0000  534.0000    0.1388
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    symbol    ->    vs
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1    0.0998    0.0497    0.0060    0.2037
## X2   -0.0187    0.0160   -0.0584    0.0031
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Competence

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : vs                  
##     X : manipmediationrecode
##     M : comp                
## 
## Sample size: 538
## 
## Random seed: 143443
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: comp
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2526    0.0638    1.1911   18.2288    2.0000  535.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.5165    0.0809   68.1899    0.0000    5.3576    5.6754
## X1          0.5953    0.1154    5.1592    0.0000    0.3686    0.8219
## X2         -0.0202    0.1147   -0.1760    0.8604   -0.2456    0.2052
## 
## *********************************************************************** 
## Outcome Variable: vs
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.3357    0.1127    0.9748   22.6038    3.0000  534.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    3.8310    0.2278   16.8148    0.0000    3.3834    4.2785
## X1          0.0815    0.1069    0.7618    0.4465   -0.1286    0.2915
## X2          0.1939    0.1038    1.8677    0.0623   -0.0100    0.3977
## comp        0.3055    0.0391    7.8121    0.0000    0.2287    0.3824
## 
## *********************************************************************** 
## Bootstrapping progress:
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1    0.0815    0.1069    0.7618    0.4465   -0.1286    0.2915
## X2    0.1939    0.1038    1.8677    0.0623   -0.0100    0.3977
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0058    1.7563    2.0000  534.0000    0.1737
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    comp    ->    vs
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1    0.1819    0.0427    0.1035    0.2725
## X2   -0.0062    0.0388   -0.0823    0.0703
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Autonomy

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : vs                  
##     X : manipmediationrecode
##     M : auto                
## 
## Sample size: 538
## 
## Random seed: 705104
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: auto
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.1494    0.0223    1.5743    6.1068    2.0000  535.0000    0.0024
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.1850    0.0930   55.7500    0.0000    5.0023    5.3677
## X1          0.4305    0.1326    3.2459    0.0012    0.1700    0.6911
## X2          0.0632    0.1319    0.4789    0.6322   -0.1959    0.3223
## 
## *********************************************************************** 
## Outcome Variable: vs
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2430    0.0591    1.0337   11.1740    3.0000  534.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    4.5704    0.1967   23.2399    0.0000    4.1841    4.9567
## X1          0.1848    0.1085    1.7025    0.0892   -0.0284    0.3980
## X2          0.1762    0.1069    1.6479    0.1000   -0.0338    0.3862
## auto        0.1825    0.0350    5.2083    0.0000    0.1136    0.2513
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1    0.1848    0.1085    1.7025    0.0892   -0.0284    0.3980
## X2    0.1762    0.1069    1.6479    0.1000   -0.0338    0.3862
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0066    1.8818    2.0000  534.0000    0.1533
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    auto    ->    vs
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1    0.0786    0.0279    0.0299    0.1394
## X2    0.0115    0.0253   -0.0378    0.0640
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Empowerment

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : vs                  
##     X : manipmediationrecode
##     M : emp                 
## 
## Sample size: 538
## 
## Random seed: 832225
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: emp
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.1855    0.0344    1.5294    9.5300    2.0000  535.0000    0.0001
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    5.1319    0.0917   55.9824    0.0000    4.9518    5.3119
## X1          0.3473    0.1307    2.6564    0.0081    0.0905    0.6041
## X2         -0.2208    0.1300   -1.6981    0.0901   -0.4761    0.0346
## 
## *********************************************************************** 
## Outcome Variable: vs
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2940    0.0864    1.0037   16.8394    3.0000  534.0000    0.0000
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    4.3252    0.1945   22.2409    0.0000    3.9432    4.7072
## X1          0.1827    0.1066    1.7140    0.0871   -0.0267    0.3921
## X2          0.2389    0.1056    2.2627    0.0241    0.0315    0.4464
## emp         0.2321    0.0350    6.6281    0.0000    0.1633    0.3009
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1    0.1827    0.1066    1.7140    0.0871   -0.0267    0.3921
## X2    0.2389    0.1056    2.2627    0.0241    0.0315    0.4464
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0096    2.8138    2.0000  534.0000    0.0609
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    emp    ->    vs
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1    0.0806    0.0301    0.0280    0.1468
## X2   -0.0512    0.0341   -0.1240    0.0125
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Proactive

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : vs                  
##     X : manipmediationrecode
##     M : proactive           
## 
## Sample size: 538
## 
## Random seed: 103175
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: proactive
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.0763    0.0058    0.8480    1.5676    2.0000  535.0000    0.2095
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    4.8166    0.0683   70.5629    0.0000    4.6825    4.9507
## X1          0.1198    0.0974    1.2307    0.2190   -0.0714    0.3111
## X2          0.1660    0.0968    1.7150    0.0869   -0.0241    0.3562
## 
## *********************************************************************** 
## Outcome Variable: vs
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.4342    0.1886    0.8914   41.3626    3.0000  534.0000    0.0000
## 
## Model: 
##               coeff        se         t         p      LLCI      ULCI
## constant     3.2103    0.2247   14.2880    0.0000    2.7689    3.6517
## X1           0.2060    0.1000    2.0607    0.0398    0.0096    0.4023
## X2           0.1082    0.0995    1.0871    0.2775   -0.0873    0.3037
## proactive    0.4788    0.0443   10.8015    0.0000    0.3917    0.5659
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1    0.2060    0.1000    2.0607    0.0398    0.0096    0.4023
## X2    0.1082    0.0995    1.0871    0.2775   -0.0873    0.3037
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0065    2.1260    2.0000  534.0000    0.1203
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    proactive    ->    vs
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1    0.0574    0.0468   -0.0316    0.1538
## X2    0.0795    0.0479   -0.0090    0.1780
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Mediator: Tightness

## 
## ********************* 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   
##  
## *********************************************************************** 
##                             
## Model : 4                   
##     Y : vs                  
##     X : manipmediationrecode
##     M : tightness           
## 
## Sample size: 538
## 
## Random seed: 295073
## 
## Coding of categorical X variable for analysis: 
##   manipmediationrecode        X1        X2
##                -1.0000    0.0000    0.0000
##                 0.0000    1.0000    0.0000
##                 1.0000    0.0000    1.0000
## 
## *********************************************************************** 
## Outcome Variable: tightness
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.0418    0.0017    0.7268    0.4675    2.0000  535.0000    0.6268
## 
## Model: 
##              coeff        se         t         p      LLCI      ULCI
## constant    4.9956    0.0632   79.0519    0.0000    4.8715    5.1197
## X1          0.0624    0.0901    0.6918    0.4894   -0.1147    0.2394
## X2          0.0833    0.0896    0.9293    0.3531   -0.0928    0.2593
## 
## *********************************************************************** 
## Outcome Variable: vs
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.2541    0.0646    1.0277   12.2854    3.0000  534.0000    0.0000
## 
## Model: 
##               coeff        se         t         p      LLCI      ULCI
## constant     4.0999    0.2676   15.3220    0.0000    3.5743    4.6256
## X1           0.2457    0.1072    2.2913    0.0223    0.0350    0.4563
## X2           0.1641    0.1066    1.5384    0.1245   -0.0454    0.3736
## tightness    0.2836    0.0514    5.5157    0.0000    0.1826    0.3845
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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  |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>         |  86%
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  |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>        |  86%
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  |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>     |  93%
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  |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>    |  93%
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  |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>    |  94%
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  |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> |  98%
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  |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> |  99%
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  |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>| 100%
## 
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
## 
## Relative direct effects of X on Y:
##       effect        se         t         p      LLCI      ULCI
## X1    0.2457    0.1072    2.2913    0.0223    0.0350    0.4563
## X2    0.1641    0.1066    1.5384    0.1245   -0.0454    0.3736
## 
## Omnibus test of direct effect of X on Y:
##     R2-chng         F       df1       df2         p
##      0.0096    2.7331    2.0000  534.0000    0.0659
## 
## ----------
## 
## Relative indirect effects of X on Y:
## 
## manipmediationrecode    ->    tightness    ->    vs
## 
##       Effect    BootSE  BootLLCI  BootULCI
## X1    0.0177    0.0261   -0.0300    0.0721
## X2    0.0236    0.0272   -0.0267    0.0821
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 5000

Moderation

Moderation information

veryd_half = as.data.frame(managervig_clean) %>% filter(condgraph == "1. Very Diverse" | condgraph == "2. Half")
half_sim = as.data.frame(managervig_clean) %>% filter(condgraph == "3. Very Similar" | condgraph == "2. Half")
veryd_sim = as.data.frame(managervig_clean) %>% filter(condgraph == "1. Very Diverse" | condgraph == "3. Very Similar")

Panas

summary(lm(censor~panas_neg*condgraph, managervig_clean))
## 
## Call:
## lm(formula = censor ~ panas_neg * condgraph, data = managervig_clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -2.13  -0.48  -0.14   0.35   3.48 
## 
## Coefficients:
##                                    Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                          1.4171     0.1200   11.81 < 0.0000000000000002 ***
## panas_neg                            0.2308     0.0468    4.93            0.0000011 ***
## condgraph2. Half                    -0.3871     0.1718   -2.25               0.0247 *  
## condgraph3. Very Similar            -0.3470     0.1767   -1.96               0.0501 .  
## panas_neg:condgraph2. Half           0.2070     0.0676    3.06               0.0023 ** 
## panas_neg:condgraph3. Very Similar   0.2235     0.0949    2.36               0.0188 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.74 on 532 degrees of freedom
## Multiple R-squared:  0.222,  Adjusted R-squared:  0.214 
## F-statistic: 30.3 on 5 and 532 DF,  p-value: <0.0000000000000002
interactions::interact_plot(lm(censor~panas_neg*condgraph, managervig_clean), modx = "condgraph", pred = "panas_neg")

interactions::sim_slopes(lm(censor~panas_neg*condgraph, managervig_clean), modx = "condgraph", pred = "panas_neg", digits = 4)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of panas_neg when condgraph = 1. Very Diverse: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.2308   0.0468   4.9275   0.0000
## 
## Slope of panas_neg when condgraph = 3. Very Similar: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.4543   0.0825   5.5073   0.0000
## 
## Slope of panas_neg when condgraph = 2. Half: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.4378   0.0488   8.9793   0.0000
JNplots::jnt_cat(
  X = "panas_neg",
  Y = "censor",
  m = "condgraph",
  data = veryd_half,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##   AIC BIC logLik
##   861 880   -425
## 
## Coefficients:
##              Value Std.Error t-value p-value
## (Intercept)   1.42     0.126    11.3  0.0000
## Xi            0.23     0.049     4.7  0.0000
## gi2. Half    -0.39     0.180    -2.2  0.0321
## Xi:gi2. Half  0.21     0.071     2.9  0.0037
## 
##  Correlation: 
##              (Intr) Xi    g2.Hlf
## Xi           -0.89              
## gi2. Half    -0.70   0.62       
## Xi:gi2. Half  0.62  -0.69 -0.89 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -2.77 -0.63 -0.16  0.46  4.52 
## 
## Residual standard error: 0.77 
## Degrees of freedom: 362 total; 358 residual
## 
## $`lower limit in X`
## (Intercept) 
##        0.88 
## 
## $`upper limit in X`
## (Intercept) 
##         2.9 
## 
## [[4]]
JNplots::jnt_cat(
  X = "panas_neg",
  Y = "censor",
  m = "condgraph",
  data = veryd_sim,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##   AIC BIC logLik
##   787 807   -389
## 
## Coefficients:
##                      Value Std.Error t-value p-value
## (Intercept)           1.42     0.116    12.3   0.000
## Xi                    0.23     0.045     5.1   0.000
## gi3. Very Similar    -0.35     0.170    -2.0   0.042
## Xi:gi3. Very Similar  0.22     0.091     2.4   0.015
## 
##  Correlation: 
##                      (Intr) Xi    g3.VrS
## Xi                   -0.89              
## gi3. Very Similar    -0.68   0.60       
## Xi:gi3. Very Similar  0.44  -0.49 -0.88 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -2.02 -0.67 -0.21  0.40  4.55 
## 
## Residual standard error: 0.71 
## Degrees of freedom: 356 total; 352 residual
## 
## $`lower limit in X`
## (Intercept) 
##        0.67 
## 
## $`upper limit in X`
## (Intercept) 
##         2.4 
## 
## [[4]]
JNplots::jnt_cat(
  X = "panas_neg",
  Y = "censor",
  m = "condgraph",
  data = half_sim,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##   AIC BIC logLik
##   809 828   -399
## 
## Coefficients:
##                      Value Std.Error t-value p-value
## (Intercept)           1.03     0.121     8.5    0.00
## Xi                    0.44     0.048     9.1    0.00
## gi3. Very Similar     0.04     0.176     0.2    0.82
## Xi:gi3. Very Similar  0.02     0.095     0.2    0.86
## 
##  Correlation: 
##                      (Intr) Xi    g3.VrS
## Xi                   -0.90              
## gi3. Very Similar    -0.69   0.62       
## Xi:gi3. Very Similar  0.46  -0.51 -0.88 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -2.93 -0.64 -0.16  0.49  4.79 
## 
## Residual standard error: 0.73 
## Degrees of freedom: 358 total; 354 residual
## 
## $`lower limit in X`
## (Intercept) 
##         -12 
## 
## $`upper limit in X`
## (Intercept) 
##         8.3 
## 
## [[4]]
summary(lm(vs~auto*condgraph, managervig_clean))
## 
## Call:
## lm(formula = vs ~ auto * condgraph, data = managervig_clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -0.496  0.116  0.741  2.111 
## 
## Coefficients:
##                               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)                     5.3186     0.3076   17.29 <0.0000000000000002 ***
## auto                            0.0735     0.0568    1.29              0.1966    
## condgraph2. Half               -0.8446     0.4466   -1.89              0.0591 .  
## condgraph3. Very Similar       -1.2304     0.4804   -2.56              0.0107 *  
## auto:condgraph2. Half           0.1276     0.0832    1.53              0.1256    
## auto:condgraph3. Very Similar   0.2278     0.0858    2.65              0.0082 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1 on 532 degrees of freedom
## Multiple R-squared:  0.0716, Adjusted R-squared:  0.0629 
## F-statistic: 8.21 on 5 and 532 DF,  p-value: 0.000000177
interactions::interact_plot(lm(vs~auto*condgraph, managervig_clean), modx = "condgraph", pred = "auto")

interactions::sim_slopes(lm(vs~auto*condgraph, managervig_clean), modx = "condgraph", pred = "auto", digits = 4)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of auto when condgraph = 1. Very Diverse: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.0735   0.0568   1.2929   0.1966
## 
## Slope of auto when condgraph = 3. Very Similar: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.3012   0.0643   4.6852   0.0000
## 
## Slope of auto when condgraph = 2. Half: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.2010   0.0607   3.3097   0.0010
JNplots::jnt_cat(
  X = "auto",
  Y = "vs",
  m = "condgraph",
  data = veryd_half,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##    AIC  BIC logLik
##   1088 1107   -539
## 
## Coefficients:
##              Value Std.Error t-value p-value
## (Intercept)    5.3      0.32    16.5   0.000
## Xi             0.1      0.06     1.2   0.217
## gi2. Half     -0.8      0.47    -1.8   0.071
## Xi:gi2. Half   0.1      0.09     1.5   0.143
## 
##  Correlation: 
##              (Intr) Xi    g2.Hlf
## Xi           -0.97              
## gi2. Half    -0.69   0.67       
## Xi:gi2. Half  0.66  -0.68 -0.97 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -4.10 -0.50  0.16  0.70  1.88 
## 
## Residual standard error: 1.1 
## Degrees of freedom: 362 total; 358 residual
## 
## $`lower limit in X`
## (Intercept) 
##           4 
## 
## $`upper limit in X`
## (Intercept) 
##         9.2 
## 
## [[4]]
JNplots::jnt_cat(
  X = "auto",
  Y = "vs",
  m = "condgraph",
  data = veryd_sim,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##    AIC  BIC logLik
##   1043 1062   -516
## 
## Coefficients:
##                      Value Std.Error t-value p-value
## (Intercept)            5.3      0.31    17.2  0.0000
## Xi                     0.1      0.06     1.3  0.1994
## gi3. Very Similar     -1.2      0.48    -2.5  0.0113
## Xi:gi3. Very Similar   0.2      0.09     2.6  0.0087
## 
##  Correlation: 
##                      (Intr) Xi    g3.VrS
## Xi                   -0.97              
## gi3. Very Similar    -0.64   0.62       
## Xi:gi3. Very Similar  0.64  -0.66 -0.97 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -4.26 -0.49  0.12  0.79  2.07 
## 
## Residual standard error: 1 
## Degrees of freedom: 356 total; 352 residual
## 
## $`lower limit in X`
## (Intercept) 
##           4 
## 
## $`upper limit in X`
## (Intercept) 
##         6.8 
## 
## [[4]]
JNplots::jnt_cat(
  X = "auto",
  Y = "vs",
  m = "condgraph",
  data = half_sim,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##    AIC  BIC logLik
##   1005 1025   -498
## 
## Coefficients:
##                      Value Std.Error t-value p-value
## (Intercept)            4.5      0.31    14.6  0.0000
## Xi                     0.2      0.06     3.5  0.0005
## gi3. Very Similar     -0.4      0.46    -0.8  0.4068
## Xi:gi3. Very Similar   0.1      0.08     1.2  0.2321
## 
##  Correlation: 
##                      (Intr) Xi    g3.VrS
## Xi                   -0.97              
## gi3. Very Similar    -0.66   0.64       
## Xi:gi3. Very Similar  0.67  -0.69 -0.97 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -4.47 -0.52  0.11  0.74  2.20 
## 
## Residual standard error: 0.96 
## Degrees of freedom: 358 total; 354 residual
## 
## $`lower limit in X`
## (Intercept) 
##           1 
## 
## $`upper limit in X`
## (Intercept) 
##         6.7 
## 
## [[4]]

Competence

summary(lm(vs~comp*condgraph, managervig_clean))
## 
## Call:
## lm(formula = vs ~ comp * condgraph, data = managervig_clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.529 -0.552  0.161  0.721  2.031 
## 
## Coefficients:
##                               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                     4.5177     0.3205   14.09 < 0.0000000000000002 ***
## comp                            0.2159     0.0568    3.80              0.00016 ***
## condgraph2. Half               -0.9910     0.5022   -1.97              0.04901 *  
## condgraph3. Very Similar       -1.3323     0.6186   -2.15              0.03172 *  
## comp:condgraph2. Half           0.1448     0.0892    1.62              0.10514    
## comp:condgraph3. Very Similar   0.2086     0.1028    2.03              0.04293 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.98 on 532 degrees of freedom
## Multiple R-squared:  0.121,  Adjusted R-squared:  0.113 
## F-statistic: 14.7 on 5 and 532 DF,  p-value: 0.000000000000179
interactions::interact_plot(lm(vs~comp*condgraph, managervig_clean), modx = "condgraph", pred = "comp")

interactions::sim_slopes(lm(vs~comp*condgraph, managervig_clean), modx = "condgraph", pred = "comp", digits = 4)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of comp when condgraph = 1. Very Diverse: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.2159   0.0568   3.8027   0.0002
## 
## Slope of comp when condgraph = 3. Very Similar: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.4245   0.0857   4.9522   0.0000
## 
## Slope of comp when condgraph = 2. Half: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.3607   0.0688   5.2400   0.0000
JNplots::jnt_cat(
  X = "comp",
  Y = "vs",
  m = "condgraph",
  data = veryd_half,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##    AIC  BIC logLik
##   1062 1081   -526
## 
## Coefficients:
##              Value Std.Error t-value p-value
## (Intercept)    4.5      0.33    13.6  0.0000
## Xi             0.2      0.06     3.7  0.0003
## gi2. Half     -1.0      0.52    -1.9  0.0577
## Xi:gi2. Half   0.1      0.09     1.6  0.1182
## 
##  Correlation: 
##              (Intr) Xi    g2.Hlf
## Xi           -0.97              
## gi2. Half    -0.64   0.62       
## Xi:gi2. Half  0.62  -0.64 -0.98 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -4.44 -0.55  0.17  0.71  1.99 
## 
## Residual standard error: 1 
## Degrees of freedom: 362 total; 358 residual
## 
## $`lower limit in X`
## (Intercept) 
##         4.9 
## 
## $`upper limit in X`
## (Intercept) 
##         8.8 
## 
## [[4]]
JNplots::jnt_cat(
  X = "comp",
  Y = "vs",
  m = "condgraph",
  data = veryd_sim,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##    AIC  BIC logLik
##   1029 1048   -509
## 
## Coefficients:
##                      Value Std.Error t-value p-value
## (Intercept)            4.5      0.33    13.9  0.0000
## Xi                     0.2      0.06     3.8  0.0002
## gi3. Very Similar     -1.3      0.63    -2.1  0.0344
## Xi:gi3. Very Similar   0.2      0.10     2.0  0.0461
## 
##  Correlation: 
##                      (Intr) Xi    g3.VrS
## Xi                   -0.97              
## gi3. Very Similar    -0.52   0.50       
## Xi:gi3. Very Similar  0.54  -0.55 -0.98 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -4.54 -0.50  0.19  0.72  2.02 
## 
## Residual standard error: 1 
## Degrees of freedom: 356 total; 352 residual
## 
## $`lower limit in X`
## (Intercept) 
##         5.1 
## 
## $`upper limit in X`
## (Intercept) 
##         7.7 
## 
## [[4]]
JNplots::jnt_cat(
  X = "comp",
  Y = "vs",
  m = "condgraph",
  data = half_sim,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##   AIC  BIC logLik
##   986 1005   -488
## 
## Coefficients:
##                      Value Std.Error t-value p-value
## (Intercept)            3.5      0.37     9.6    0.00
## Xi                     0.4      0.07     5.5    0.00
## gi3. Very Similar     -0.3      0.62    -0.5    0.58
## Xi:gi3. Very Similar   0.1      0.10     0.6    0.54
## 
##  Correlation: 
##                      (Intr) Xi    g3.VrS
## Xi                   -0.98              
## gi3. Very Similar    -0.59   0.58       
## Xi:gi3. Very Similar  0.61  -0.63 -0.99 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -4.26 -0.60  0.10  0.74  2.18 
## 
## Residual standard error: 0.93 
## Degrees of freedom: 358 total; 354 residual
## 
## $`lower limit in X`
## (Intercept) 
##         1.8 
## 
## $`upper limit in X`
## (Intercept) 
##         8.9 
## 
## [[4]]

Symbol

summary(lm(censor~symbol*condgraph, managervig_clean))
## 
## Call:
## lm(formula = censor ~ symbol * condgraph, data = managervig_clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.627 -0.469 -0.182  0.318  3.396 
## 
## Coefficients:
##                                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)                       1.4284     0.1489    9.59 <0.0000000000000002 ***
## symbol                            0.1351     0.0361    3.74              0.0002 ***
## condgraph2. Half                 -0.2883     0.2148   -1.34              0.1801    
## condgraph3. Very Similar         -0.2469     0.1870   -1.32              0.1871    
## symbol:condgraph2. Half           0.1128     0.0543    2.08              0.0382 *  
## symbol:condgraph3. Very Similar   0.1155     0.0581    1.99              0.0474 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.77 on 532 degrees of freedom
## Multiple R-squared:  0.154,  Adjusted R-squared:  0.146 
## F-statistic: 19.3 on 5 and 532 DF,  p-value: <0.0000000000000002
interactions::interact_plot(lm(censor~symbol*condgraph, managervig_clean), modx = "condgraph", pred = "symbol")

interactions::sim_slopes(lm(censor~symbol*condgraph, managervig_clean), modx = "condgraph", pred = "symbol", digits = 4)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of symbol when condgraph = 1. Very Diverse: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.1351   0.0361   3.7446   0.0002
## 
## Slope of symbol when condgraph = 3. Very Similar: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.2505   0.0455   5.5006   0.0000
## 
## Slope of symbol when condgraph = 2. Half: 
## 
##     Est.     S.E.   t val.        p
## -------- -------- -------- --------
##   0.2479   0.0406   6.1043   0.0000
JNplots::jnt_cat(
  X = "symbol",
  Y = "censor",
  m = "condgraph",
  data = veryd_half,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##   AIC BIC logLik
##   904 924   -447
## 
## Coefficients:
##              Value Std.Error t-value p-value
## (Intercept)   1.43     0.159     9.0  0.0000
## Xi            0.14     0.038     3.5  0.0005
## gi2. Half    -0.29     0.229    -1.3  0.2084
## Xi:gi2. Half  0.11     0.058     2.0  0.0519
## 
##  Correlation: 
##              (Intr) Xi    g2.Hlf
## Xi           -0.92              
## gi2. Half    -0.69   0.64       
## Xi:gi2. Half  0.61  -0.66 -0.93 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -1.99 -0.66 -0.17  0.43  4.15 
## 
## Residual standard error: 0.82 
## Degrees of freedom: 362 total; 358 residual
## 
## $`lower limit in X`
## (Intercept) 
##        0.77 
## 
## $`upper limit in X`
## (Intercept) 
##         4.3 
## 
## [[4]]
JNplots::jnt_cat(
  X = "symbol",
  Y = "censor",
  m = "condgraph",
  data = veryd_sim,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##   AIC BIC logLik
##   796 815   -393
## 
## Coefficients:
##                      Value Std.Error t-value p-value
## (Intercept)           1.43     0.139    10.3  0.0000
## Xi                    0.14     0.034     4.0  0.0001
## gi3. Very Similar    -0.25     0.174    -1.4  0.1577
## Xi:gi3. Very Similar  0.12     0.054     2.1  0.0338
## 
##  Correlation: 
##                      (Intr) Xi    g3.VrS
## Xi                   -0.92              
## gi3. Very Similar    -0.80   0.73       
## Xi:gi3. Very Similar  0.57  -0.62 -0.86 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -2.18 -0.60 -0.25  0.44  4.74 
## 
## Residual standard error: 0.72 
## Degrees of freedom: 356 total; 352 residual
## 
## $`lower limit in X`
## (Intercept) 
##        0.39 
## 
## $`upper limit in X`
## (Intercept) 
##         3.9 
## 
## [[4]]
JNplots::jnt_cat(
  X = "symbol",
  Y = "censor",
  m = "condgraph",
  data = half_sim,
  plot.full = TRUE
)

## $coeff
## Generalized least squares fit by REML
##   Model: Yi ~ Xi * gi 
##   Data: NULL 
##   AIC BIC logLik
##   847 866   -418
## 
## Coefficients:
##                      Value Std.Error t-value p-value
## (Intercept)           1.14     0.154     7.4    0.00
## Xi                    0.25     0.040     6.1    0.00
## gi3. Very Similar     0.04     0.191     0.2    0.83
## Xi:gi3. Very Similar  0.00     0.061     0.0    0.97
## 
##  Correlation: 
##                      (Intr) Xi    g3.VrS
## Xi                   -0.93              
## gi3. Very Similar    -0.81   0.75       
## Xi:gi3. Very Similar  0.62  -0.67 -0.88 
## 
## Standardized residuals:
##   Min    Q1   Med    Q3   Max 
## -2.13 -0.57 -0.24  0.42  4.25 
## 
## Residual standard error: 0.77 
## Degrees of freedom: 358 total; 354 residual
## 
## $`lower limit in X`
## (Intercept) 
##         -31 
## 
## $`upper limit in X`
## (Intercept) 
##          30 
## 
## [[4]]