25_09.24-DifferentialIDPilot1 - vignette

Person

Manipulation text explanation
Jeff Man was confronter/seeker
Jane Woman was confronter/seeker

Solicitation (cond_person)

All participants saw “Your comment about women is pretty messed up” before the text below.

Manipulation label
noseeking [participants didn’t see any additional text
indirect [participant] probably doesn’t appreciate it either.
general What did others think about it?
direct_think “[participant]…what do you think of Paul’s comment?”
direct_describe “[participant]…will you describe how Paul’s comment made you feel?”
direct_explain “[participant]…could you explain why Paul’s comment was discriminatory?”

Items

Competence

When {he/she} responded to Paul, did you think that {Jeff/Jane} was…(1 = Not at all, 4 = Somewhat, 7 = Very much so)

Item label Item text
Comp1 competent at reducing sexism?
Comp2 competent in managing difficult conversations?

Tokenism

{Jeff/Jane} led me to feel…(1 = Not at all, 4 = Somewhat, 7 = Very much so)

Item label Item text
token_1 worried that I stood out because I am a woman
token_2 feel like my skills and knowledge as a woman were made salient
token_3 feel like a “token” representative of women

Fear of Social Retaliation

{Jeff/Jane} led me to feel…(1 = Not at all, 4 = Somewhat, 7 = Very much so)

Item label Item text
fsr_1 shunned or excluded by others at work
fsr_3 gossiped about in an unkind way
fsr_5 criticized for complaining

Empowerment

{Jeff/Jane} led me to feel…(1 = Not at all, 4 = Somewhat, 7 = Very much so)

Item label Item text
empower_exp_2 supported
empower_exp_3 empowered
empower_exp_4 like I have influence
empower_exp_4.1 inspired

Agency

When {Jeff/Jane} responded to Paul, did you think that {Jeff/Jane} was…(1 = Not at all, 4 = Somewhat, 7 = Very much so)

Item label Item text
agency_1 powerful
agency_2 capable
agency_3 agentic

Warm

When {Jeff/Jane} responded to Paul, did you think that {Jeff/Jane} was…(1 = Not at all, 4 = Somewhat, 7 = Very much so)

Item label Item text
warm_1 warm
warm_2 friendly
warm_3 caring

Status

Do you think that {Jeff/Jane} should experience any of the following changes after {his/her} response to Paul?

Item label Item text - 3 0 3
j_posstat1 After {his/her} response back to Paul, I think{Jeff/Jane} is worthy of…: -3. A lot of disrespect 0. Neither disrespect nor respect 3. A lot of respect
j_posstat2 After {his/her} response back to Paul, I hold{Jeff/Jane}… -3. In very low regard 0. In neither low regard nor high regard 3. In very high regard
j_posstat3 After {his/her} response back to Paul, in terms of being like{Jeff/Jane}…: -3. I want to be very different from him -3. A lot of disrespect 0. I don’t want to be like him, or different from him

Rewards

Do you think that {Jeff/Jane} should experience any of the following changes after {his/her} response to Paul?

Item label Item text - 3 0 3
j_reward3 | change in visibility of {his/her} project assignments: |-3. Should be assigned to projects with very low visibility | 0. Should remain on projects with the same visibility as before | 3. Should be assigned to projects with high visibility|
j_reward4 | change in {his/her} public recognition: | -3. Should definitely be decreased | 0. Should be kept the same | 3. Should definitely be increased |

Social Rewards

Do you think that{Jeff/Jane} should experience any of the following changes after {his/her} response to Paul?

Item label Item text - 3 0 3
j_socreward1 at the next work event: -3. I would avoid{Jeff/Jane} 0. I would neither avoid nor approach{Jeff/Jane} 3. I would approach{Jeff/Jane}
j_socreward2 how much closer did you feel to{Jeff/Jane}?: -3. I felt much more distant from him 0. The amount of closeness I felt towards him did not change 3. I felt much closer to him
j_socreward3 how would the amount of time that you want to spend with{Jeff/Jane} change?: -3. I would want to spend much less time with him 0. I would not want to change the amount of time I spend with him 3. I would want to spend much more time with him

Manipulations

## 
##   Solicitation NoSolicitation 
##            210            221
## 
## Woman   Man 
##   216   215

Analyses

Means and SDs

Significant interactions?

Because this is exploratory, I included all items in each scale.

Coded all interactions @ p<.05 w/ “”, all interactions @ p< .01 w/ ””, all interactions @ p< .001 w/ ””

Significant comparisons (Interactions)

effect that correspond to a significant estimate are coded with asterices

What this says:

  1. women’s (versus men’s) solicitation is more agentic, and worthier of status.
  2. women’s solicitation (versus not solicitation) is not rewarded differently, but is generally not seen as better.
  3. men (versus women) not soliciting are more empowering, and rewarded more.

Graphs

Competence

graphs[["comp"]]

FSR

graphs[["fsr"]]

Token

graphs[["token"]]

Empower

graphs[["empower"]]

Agency

graphs[["agency"]]

Warm

graphs[["warm"]]

Reward

graphs[["reward"]]

Social Reward

graphs[["socreward"]]

Positive status

graphs[["posstatus"]]

Interactions

sjPlot::plot_model(lm(empower_exp_2~confronter*solicitation, diffidp3raw), type = "int", axis.title = "I feel supported", title = "I feel supported")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(empower_exp_3~confronter*solicitation, diffidp3raw), type = "int", axis.title = "I feel empowered", title = "I feel empowered")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(empower_exp_4~confronter*solicitation, diffidp3raw), type = "int", axis.title = "I feel like I have influence", title = "I feel like I have influence")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(empower_exp_4.1~confronter*solicitation, diffidp3raw), type = "int", axis.title = "I feel inspired", title = "I feel inspired")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(warm_1~confronter*solicitation, diffidp3raw), type = "int", axis.title = "Confronter was warm", title = "Confronter was warm")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(warm_2~confronter*solicitation, diffidp3raw), type = "int", axis.title = "Confronter was friendly", title = "Confronter was friendly")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(warm_3~confronter*solicitation, diffidp3raw), type = "int", axis.title = "Confronter was caring", title = "Confronter was caring")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(agency_1~confronter*solicitation, diffidp3raw), type = "int", axis.title = "Confronter was powerful", title = "Confronter was powerful")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(agency_2~confronter*solicitation, diffidp3raw), type = "int", axis.title = "Confronter was capable", title = "Confronter was capable")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

sjPlot::plot_model(lm(agency_3~confronter*solicitation, diffidp3raw), type = "int", axis.title = "Confronter was agentic", title = "Confronter was agentic")
## Ignoring unknown labels:
## • linetype : "solicitation"
## • shape : "solicitation"

# Moderated mediation

bruceR::PROCESS(
  data = diffidp3raw,
  x = "solicitation",
  mods = "confronter",
  meds = "agency",
  y = "posstatus",
  mod.path = "all", seed = 1, nsim = 1000
)
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model ID : 59
## Model Type : Moderated Mediation
## -    Outcome (Y) : posstatus
## -  Predictor (X) : solicitation (recoded: Solicitation=0, NoSolicitation=1)
## -  Mediators (M) : agency
## - Moderators (W) : confronter
## - Covariates (C) : -
## -   HLM Clusters : -
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    agency ~ solicitation*confronter
## Formula of Outcome:
## -    posstatus ~ solicitation*confronter + agency*confronter
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect be interpreted as "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────
##                             (1) posstatus  (2) agency   (3) posstatus
## ─────────────────────────────────────────────────────────────────────
## (Intercept)                   1.625 ***      5.172 ***    1.688 ***  
##                              (0.062)        (0.100)      (0.059)     
## solicitation                  0.634 ***      0.546 **     0.366 **   
##                              (0.123)        (0.199)      (0.117)     
## confronterMan                               -0.628 ***   -0.085      
##                                             (0.141)      (0.083)     
## solicitation:confronterMan                   0.165       -0.249      
##                                             (0.282)      (0.167)     
## agency                                                    0.573 ***  
##                                                          (0.041)     
## confronterMan:agency                                      0.124 *    
##                                                          (0.056)     
## ─────────────────────────────────────────────────────────────────────
## R^2                           0.058          0.084        0.594      
## Adj. R^2                      0.056          0.077        0.590      
## Num. obs.                   431            431          431          
## ─────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.1), ‘interactions’ (v1.2.0)
## Effect Type : Moderated Mediation (Model 59)
## Sample Size : 431
## Random Seed : set.seed(1)
## Simulations : 1000 (Bootstrap)
## 
## Interaction Effect on "posstatus" (Y)
## ─────────────────────────────────────────────────
##                               F df1 df2     p    
## ─────────────────────────────────────────────────
## solicitation * confronter  2.23   1 425  .136    
## confronter * agency        4.90   1 425  .027 *  
## (All Interactions)         3.00   2 425  .051 .  
## ─────────────────────────────────────────────────
## 
## Simple Slopes: "solicitation" (X) ==> "posstatus" (Y)
## (Conditional Direct Effects [c'] of X on Y)
## ────────────────────────────────────────────────────────────
##  "confronter" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────
##  Man           0.117 (0.119) 0.987  .324     [-0.116, 0.350]
##  Woman         0.366 (0.117) 3.125  .002 **  [ 0.136, 0.596]
## ────────────────────────────────────────────────────────────
## 
## Interaction Effect on "agency" (M)
## ─────────────────────────────────────────────────
##                               F df1 df2     p    
## ─────────────────────────────────────────────────
## solicitation * confronter  0.34   1 427  .559    
## ─────────────────────────────────────────────────
## 
## Simple Slopes: "solicitation" (X) ==> "agency" (M)
## (Conditional Effects [a] of X on M)
## ───────────────────────────────────────────────────────────
##  "confronter" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────
##  Man           0.711 (0.200) 3.560 <.001 *** [0.318, 1.104]
##  Woman         0.546 (0.199) 2.745  .006 **  [0.155, 0.938]
## ───────────────────────────────────────────────────────────
## 
## Interaction Effect on "posstatus" (Y)
## ─────────────────────────────────────────────────
##                               F df1 df2     p    
## ─────────────────────────────────────────────────
## solicitation * confronter  2.23   1 425  .136    
## confronter * agency        4.90   1 425  .027 *  
## (All Interactions)         3.00   2 425  .051 .  
## ─────────────────────────────────────────────────
## 
## Simple Slopes: "agency" (M) ==> "posstatus" (Y)
## (Conditional Effects [b] of M on Y)
## ────────────────────────────────────────────────────────────
##  "confronter" Effect    S.E.      t     p           [95% CI]
## ────────────────────────────────────────────────────────────
##  Man           0.697 (0.039) 18.059 <.001 *** [0.621, 0.773]
##  Woman         0.573 (0.041) 14.154 <.001 *** [0.494, 0.653]
## ────────────────────────────────────────────────────────────
## 
## Running 1000 * 2 simulations...
## Indirect Path: "solicitation" (X) ==> "agency" (M) ==> "posstatus" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────
##  "confronter" Effect    S.E.     z     p       [Boot 95% CI]
## ────────────────────────────────────────────────────────────
##  Man           0.496 (0.146) 3.407 <.001 *** [ 0.222, 0.793]
##  Woman         0.313 (0.111) 2.833  .005 **  [0.111, 0.529] 
## ────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 1000 Bootstrap samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)
bruceR::PROCESS(
  data = diffidp3raw,
  mods = "solicitation",
  x = "confronter",
  meds = "agency",
  y = "posstatus",
  mod.path = "all", seed = 1, nsim = 1000
)
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model ID : 59
## Model Type : Moderated Mediation
## -    Outcome (Y) : posstatus
## -  Predictor (X) : confronter (recoded: Woman=0, Man=1)
## -  Mediators (M) : agency
## - Moderators (W) : solicitation
## - Covariates (C) : -
## -   HLM Clusters : -
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    agency ~ confronter*solicitation
## Formula of Outcome:
## -    posstatus ~ confronter*solicitation + agency*solicitation
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect be interpreted as "main effect"!
##   
## Model Summary
## 
## ────────────────────────────────────────────────────────────────────────────────
##                                        (1) posstatus  (2) agency   (3) posstatus
## ────────────────────────────────────────────────────────────────────────────────
## (Intercept)                              1.625 ***      4.537 ***    1.517 ***  
##                                         (0.063)        (0.101)      (0.060)     
## confronter                              -0.467 ***     -0.712 ***    0.042      
##                                         (0.125)        (0.202)      (0.120)     
## solicitationNoSolicitation                              0.629 ***    0.245 **   
##                                                        (0.141)      (0.083)     
## confronter:solicitationNoSolicitation                   0.165       -0.238      
##                                                        (0.282)      (0.167)     
## agency                                                               0.690 ***  
##                                                                     (0.039)     
## solicitationNoSolicitation:agency                                   -0.106      
##                                                                     (0.056)     
## ────────────────────────────────────────────────────────────────────────────────
## R^2                                      0.031          0.084        0.593      
## Adj. R^2                                 0.029          0.077        0.588      
## Num. obs.                              431            431          431          
## ────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.1), ‘interactions’ (v1.2.0)
## Effect Type : Moderated Mediation (Model 59)
## Sample Size : 431
## Random Seed : set.seed(1)
## Simulations : 1000 (Bootstrap)
## 
## Interaction Effect on "posstatus" (Y)
## ─────────────────────────────────────────────────
##                               F df1 df2     p    
## ─────────────────────────────────────────────────
## confronter * solicitation  2.04   1 425  .154    
## solicitation * agency      3.62   1 425  .058 .  
## (All Interactions)         2.36   2 425  .096 .  
## ─────────────────────────────────────────────────
## 
## Simple Slopes: "confronter" (X) ==> "posstatus" (Y)
## (Conditional Direct Effects [c'] of X on Y)
## ───────────────────────────────────────────────────────────────
##  "solicitation" Effect    S.E.      t     p            [95% CI]
## ───────────────────────────────────────────────────────────────
##  NoSolicitation -0.197 (0.116) -1.696  .091 .   [-0.424, 0.031]
##  Solicitation    0.042 (0.120)  0.347  .729     [-0.194, 0.278]
## ───────────────────────────────────────────────────────────────
## 
## Interaction Effect on "agency" (M)
## ─────────────────────────────────────────────────
##                               F df1 df2     p    
## ─────────────────────────────────────────────────
## confronter * solicitation  0.34   1 427  .559    
## ─────────────────────────────────────────────────
## 
## Simple Slopes: "confronter" (X) ==> "agency" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────────
##  "solicitation" Effect    S.E.      t     p             [95% CI]
## ────────────────────────────────────────────────────────────────
##  NoSolicitation -0.547 (0.197) -2.781  .006 **  [-0.934, -0.161]
##  Solicitation   -0.712 (0.202) -3.527 <.001 *** [-1.109, -0.315]
## ────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "posstatus" (Y)
## ─────────────────────────────────────────────────
##                               F df1 df2     p    
## ─────────────────────────────────────────────────
## confronter * solicitation  2.04   1 425  .154    
## solicitation * agency      3.62   1 425  .058 .  
## (All Interactions)         2.36   2 425  .096 .  
## ─────────────────────────────────────────────────
## 
## Simple Slopes: "agency" (M) ==> "posstatus" (Y)
## (Conditional Effects [b] of M on Y)
## ──────────────────────────────────────────────────────────────
##  "solicitation" Effect    S.E.      t     p           [95% CI]
## ──────────────────────────────────────────────────────────────
##  NoSolicitation  0.584 (0.040) 14.565 <.001 *** [0.505, 0.662]
##  Solicitation    0.690 (0.039) 17.649 <.001 *** [0.613, 0.767]
## ──────────────────────────────────────────────────────────────
## 
## Running 1000 * 2 simulations...
## Indirect Path: "confronter" (X) ==> "agency" (M) ==> "posstatus" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────────
##  "solicitation" Effect    S.E.      z     p        [Boot 95% CI]
## ────────────────────────────────────────────────────────────────
##  NoSolicitation -0.319 (0.117) -2.725  .006 **  [-0.554, -0.096]
##  Solicitation   -0.492 (0.156) -3.150  .002 **  [-0.814, -0.209]
## ────────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 1000 Bootstrap samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

Main effects (controlling)

VariableLevel

comp

fsr

token

empower

agency

warm

reward

socreward

posstatus

(Intercept)

4.81***

2.51***

2.88***

4.7***

4.85***

4.05***

0.61***

1.07***

1.54***

solicitationNoSolicitation

0.47**

-0.32*

-0.43**

0.65***

0.63***

0.71***

0.43***

0.63***

0.65***

confronterMan

-0.34*

0.34*

0.39**

-0.46**

-0.63***

-0.04

-0.18

-0.47***

-0.48***

Responses

Paul

All comparisons

## 
## Call:
## glm(formula = response_paul ~ confronter * solicitation, family = "binomial", 
##     data = diffidp3raw)
## 
## Coefficients:
##                                                      Estimate           Std. Error z value Pr(>|z|)
## (Intercept)                              -0.00000000000000158  0.19245008972987687    0.00     1.00
## confronterMan                            -0.29924289485285316  0.27844969070526332   -1.07     0.28
## solicitationNoSolicitation               -0.03704127168034663  0.27218886778697915   -0.14     0.89
## confronterMan:solicitationNoSolicitation -0.26529582050120976  0.39151321422434310   -0.68     0.50
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 590.28  on 429  degrees of freedom
## Residual deviance: 584.05  on 426  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 592.1
## 
## Number of Fisher Scoring iterations: 4

Controls

## 
## Call:
## glm(formula = response_paul ~ confronter * empower_exp_4.1 + 
##     solicitation, family = "binomial", data = diffidp3raw)
## 
## Coefficients:
##                               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                    -1.5214     0.4413   -3.45  0.00057 ***
## confronterMan                   0.6943     0.5680    1.22  0.22155    
## empower_exp_4.1                 0.3396     0.0846    4.02 0.000059 ***
## solicitationNoSolicitation     -0.3889     0.2078   -1.87  0.06127 .  
## confronterMan:empower_exp_4.1  -0.2090     0.1108   -1.89  0.05921 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 590.28  on 429  degrees of freedom
## Residual deviance: 564.05  on 425  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 574.1
## 
## Number of Fisher Scoring iterations: 4
##                (Intercept)              confronterMan solicitationNoSolicitation 
##                     1.0663                     0.6480                     0.8475
## 
## Call:
## glm(formula = response_paul ~ solicitation, family = "binomial", 
##     data = diffidp3raw %>% filter(confronter == "Woman"))
## 
## Coefficients:
##                                         Estimate            Std. Error z value Pr(>|z|)
## (Intercept)                -0.000000000000000986  0.192450089729875234    0.00     1.00
## solicitationNoSolicitation -0.037041271680342908  0.272188866933747442   -0.14     0.89
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299.42  on 215  degrees of freedom
## Residual deviance: 299.40  on 214  degrees of freedom
## AIC: 303.4
## 
## Number of Fisher Scoring iterations: 3
## 
## Call:
## glm(formula = response_paul ~ solicitation, family = "binomial", 
##     data = diffidp3raw %>% filter(confronter == "Man"))
## 
## Coefficients:
##                            Estimate Std. Error z value Pr(>|z|)
## (Intercept)                  -0.299      0.201   -1.49     0.14
## solicitationNoSolicitation   -0.302      0.281   -1.07     0.28
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 285.81  on 213  degrees of freedom
## Residual deviance: 284.65  on 212  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 288.7
## 
## Number of Fisher Scoring iterations: 4

Confronter

All comparisons

## 
## Call:
## glm(formula = response_confronter ~ confronter * solicitation, 
##     family = "binomial", data = diffidp3raw)
## 
## Coefficients:
##                                          Estimate Std. Error z value Pr(>|z|)
## (Intercept)                                0.2607     0.1941    1.34     0.18
## confronterMan                              0.0556     0.2791    0.20     0.84
## solicitationNoSolicitation                 0.1525     0.2762    0.55     0.58
## confronterMan:solicitationNoSolicitation  -0.2735     0.3902   -0.70     0.48
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 588.25  on 430  degrees of freedom
## Residual deviance: 587.57  on 427  degrees of freedom
## AIC: 595.6
## 
## Number of Fisher Scoring iterations: 4

Controls

## 
## Call:
## glm(formula = response_confronter ~ confronter + solicitation, 
##     family = "binomial", data = diffidp3raw)
## 
## Coefficients:
##                            Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                  0.3288     0.1689    1.95    0.052 .
## confronterMan               -0.0844     0.1949   -0.43    0.665  
## solicitationNoSolicitation   0.0154     0.1949    0.08    0.937  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 588.25  on 430  degrees of freedom
## Residual deviance: 588.06  on 428  degrees of freedom
## AIC: 594.1
## 
## Number of Fisher Scoring iterations: 4
##                (Intercept)              confronterMan solicitationNoSolicitation 
##                     1.3893                     0.9191                     1.0155
## 
## Call:
## glm(formula = response_confronter ~ solicitation, family = "binomial", 
##     data = diffidp3raw %>% filter(confronter == "Woman"))
## 
## Coefficients:
##                            Estimate Std. Error z value Pr(>|z|)
## (Intercept)                   0.261      0.194    1.34     0.18
## solicitationNoSolicitation    0.152      0.276    0.55     0.58
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 293.41  on 215  degrees of freedom
## Residual deviance: 293.11  on 214  degrees of freedom
## AIC: 297.1
## 
## Number of Fisher Scoring iterations: 4
## 
## Call:
## glm(formula = response_confronter ~ solicitation, family = "binomial", 
##     data = diffidp3raw %>% filter(confronter == "Man"))
## 
## Coefficients:
##                            Estimate Std. Error z value Pr(>|z|)
## (Intercept)                   0.316      0.201    1.58     0.11
## solicitationNoSolicitation   -0.121      0.276   -0.44     0.66
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 294.65  on 214  degrees of freedom
## Residual deviance: 294.46  on 213  degrees of freedom
## AIC: 298.5
## 
## Number of Fisher Scoring iterations: 4