Dissertation Analyses with WOMEN only

load(file="/Users/meganwilliams/Desktop/Dissertation/AllvarsWomen.rdata")
load(file="/Users/meganwilliams/Desktop/Dissertation/AllvarsMen.rdata")

library(effects)
library(interactions)
library(car)
library(rcompanion)

California Verbal Learning Test (Total Correct Trial A) - Psychological Aggression

Model 1

CVLTlog1 <- glm(PsychAggress ~ CVLtca + WRATtotal, data=AllvarsWomen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PsychAggress ~ CVLtca + WRATtotal, family = "binomial", 
##     data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3058   0.4192   0.4598   0.4995   0.7415  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.70042    1.01221   0.692    0.489
## CVLtca       0.01966    0.02323   0.846    0.397
## WRATtotal    0.02341    0.02504   0.935    0.350
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 227.67  on 330  degrees of freedom
## Residual deviance: 225.31  on 328  degrees of freedom
## AIC: 231.31
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog1))

Model 3

CVLTlog3 <- glm(PsychAggress ~ (CVLtca + PovStat)^2 + Age + WRATtotal, data = AllvarsWomen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PsychAggress ~ (CVLtca + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3421   0.4000   0.4507   0.5101   0.7210  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)
## (Intercept)          1.23485    1.42677   0.865    0.387
## CVLtca               0.03105    0.02843   1.092    0.275
## PovStatBelow         1.05637    1.05699   0.999    0.318
## Age                 -0.01752    0.02036  -0.860    0.390
## WRATtotal            0.02389    0.02519   0.948    0.343
## CVLtca:PovStatBelow -0.04651    0.04813  -0.966    0.334
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 227.67  on 330  degrees of freedom
## Residual deviance: 223.40  on 325  degrees of freedom
## AIC: 235.4
## 
## Number of Fisher Scoring iterations: 5
plot(allEffects(CVLTlog3))

# California Verbal Learning Test (Total Correct Trial A) - Physical Assault

Model 1

CVLTlog1 <- glm(PhysAssault ~ CVLtca + WRATtotal, data=AllvarsWomen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PhysAssault ~ CVLtca + WRATtotal, family = "binomial", 
##     data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6633  -0.5717  -0.5326  -0.4715   2.3280  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -3.37539    1.11251  -3.034  0.00241 **
## CVLtca       0.00146    0.02135   0.068  0.94547   
## WRATtotal    0.03400    0.02601   1.307  0.19114   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 263.18  on 330  degrees of freedom
## Residual deviance: 261.08  on 328  degrees of freedom
## AIC: 267.08
## 
## Number of Fisher Scoring iterations: 4
#Plots
plot(allEffects(CVLTlog1))

Model 3

CVLTlog3 <- glm(PhysAssault ~ (CVLtca + PovStat)^2 + Age + WRATtotal, data = AllvarsWomen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PhysAssault ~ (CVLtca + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8688  -0.5913  -0.4849  -0.3839   2.5728  
## 
## Coefficients:
##                      Estimate Std. Error z value Pr(>|z|)  
## (Intercept)         -2.319618   1.542354  -1.504   0.1326  
## CVLtca               0.006116   0.029249   0.209   0.8344  
## PovStatBelow         1.134367   0.942278   1.204   0.2286  
## Age                 -0.035971   0.019427  -1.852   0.0641 .
## WRATtotal            0.039962   0.027083   1.476   0.1401  
## CVLtca:PovStatBelow -0.030697   0.041043  -0.748   0.4545  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 263.18  on 330  degrees of freedom
## Residual deviance: 253.62  on 325  degrees of freedom
## AIC: 265.62
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

California Verbal Learning Test (Long Delayed Free Recall) - Psychological Aggression

Model 1

CVLTlog1 <- glm(PsychAggress ~ CVLfrl + WRATtotal, data=AllvarsWomen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PsychAggress ~ CVLfrl + WRATtotal, family = "binomial", 
##     data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4803   0.3862   0.4492   0.5159   0.7234  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.73329    1.00866   0.727   0.4672  
## CVLfrl       0.09338    0.05545   1.684   0.0922 .
## WRATtotal    0.01937    0.02432   0.797   0.4256  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 227.67  on 330  degrees of freedom
## Residual deviance: 223.14  on 328  degrees of freedom
## AIC: 229.14
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog1))

Model 3

CVLTlog3 <- glm(PsychAggress ~ (CVLfrl + PovStat)^2 + Age + WRATtotal, data = AllvarsWomen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PsychAggress ~ (CVLfrl + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4124   0.3775   0.4502   0.5191   0.7150  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)
## (Intercept)          1.22364    1.41274   0.866    0.386
## CVLfrl               0.07865    0.06695   1.175    0.240
## PovStatBelow         0.03364    0.73070   0.046    0.963
## Age                 -0.01162    0.02054  -0.566    0.572
## WRATtotal            0.02142    0.02444   0.877    0.381
## CVLfrl:PovStatBelow  0.02167    0.11885   0.182    0.855
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 227.67  on 330  degrees of freedom
## Residual deviance: 222.53  on 325  degrees of freedom
## AIC: 234.53
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

California Verbal Learning Test (Long Delayed Free Recall) - Physical Assault

Model 1

CVLTlog1 <- glm(PhysAssault ~ CVLfrl + WRATtotal, data=AllvarsWomen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PhysAssault ~ CVLfrl + WRATtotal, family = "binomial", 
##     data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6682  -0.5760  -0.5273  -0.4679   2.3326  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -3.36475    1.10909  -3.034  0.00242 **
## CVLfrl      -0.01173    0.04804  -0.244  0.80707   
## WRATtotal    0.03616    0.02546   1.420  0.15558   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 263.18  on 330  degrees of freedom
## Residual deviance: 261.03  on 328  degrees of freedom
## AIC: 267.03
## 
## Number of Fisher Scoring iterations: 4
#Plots
plot(allEffects(CVLTlog1))

Model 3

CVLTlog3 <- glm(PhysAssault ~ (CVLfrl + PovStat)^2 + Age + WRATtotal, data = AllvarsWomen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PhysAssault ~ (CVLfrl + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.0316  -0.5826  -0.4602  -0.3594   2.6101  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)  
## (Intercept)         -2.28542    1.51173  -1.512   0.1306  
## CVLfrl               0.03660    0.06460   0.567   0.5710  
## PovStatBelow         1.72157    0.69636   2.472   0.0134 *
## Age                 -0.03992    0.01976  -2.020   0.0434 *
## WRATtotal            0.04048    0.02667   1.518   0.1290  
## CVLfrl:PovStatBelow -0.20501    0.10056  -2.039   0.0415 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 263.18  on 330  degrees of freedom
## Residual deviance: 249.38  on 325  degrees of freedom
## AIC: 261.38
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

interact_plot(model = CVLTlog3, pred = CVLfrl, modx = PovStat)

sim_slopes(CVLTlog3, pred = CVLfrl, modx = PovStat, centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of CVLfrl when PovStat = Below: 
## 
##    Est.   S.E.   z val.      p
## ------- ------ -------- ------
##   -0.17   0.08    -2.04   0.04
## 
## Slope of CVLfrl when PovStat = Above: 
## 
##   Est.   S.E.   z val.      p
## ------ ------ -------- ------
##   0.04   0.06     0.57   0.57

California Verbal Learning Test (Short Delayed Free Recall) - Psychological Aggression

Model 1

CVLTlog1 <- glm(PsychAggress ~ CVLfrs + WRATtotal, data=AllvarsWomen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PsychAggress ~ CVLfrs + WRATtotal, family = "binomial", 
##     data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3490   0.4187   0.4608   0.4995   0.7633  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.79728    1.00493   0.793    0.428
## CVLfrs       0.04866    0.05574   0.873    0.383
## WRATtotal    0.02382    0.02471   0.964    0.335
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 227.67  on 330  degrees of freedom
## Residual deviance: 225.25  on 328  degrees of freedom
## AIC: 231.25
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog1))

## Model 3

CVLTlog3 <- glm(PsychAggress ~ (CVLfrs + PovStat)^2 + Age + WRATtotal, data = AllvarsWomen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PsychAggress ~ (CVLfrs + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4293   0.4038   0.4557   0.5044   0.7446  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)
## (Intercept)          1.44298    1.39951   1.031    0.303
## CVLfrs               0.05550    0.06667   0.832    0.405
## PovStatBelow         0.45016    0.77896   0.578    0.563
## Age                 -0.01719    0.02031  -0.847    0.397
## WRATtotal            0.02571    0.02476   1.039    0.299
## CVLfrs:PovStatBelow -0.05732    0.11752  -0.488    0.626
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 227.67  on 330  degrees of freedom
## Residual deviance: 224.08  on 325  degrees of freedom
## AIC: 236.08
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

California Verbal Learning Test (Short Delayed Free Recall) - Physical Assault

Model 1

CVLTlog1 <- glm(PhysAssault ~ CVLfrs + WRATtotal, data=AllvarsWomen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PhysAssault ~ CVLfrs + WRATtotal, family = "binomial", 
##     data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6706  -0.5755  -0.5239  -0.4674   2.3152  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -3.37285    1.10917  -3.041  0.00236 **
## CVLfrs      -0.01827    0.04934  -0.370  0.71120   
## WRATtotal    0.03724    0.02566   1.451  0.14669   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 263.18  on 330  degrees of freedom
## Residual deviance: 260.95  on 328  degrees of freedom
## AIC: 266.95
## 
## Number of Fisher Scoring iterations: 4
#Plots
plot(predictorEffect("CVLfrs",CVLTlog1))

Model 3

CVLTlog3 <- glm(PhysAssault ~ (CVLfrs + PovStat)^2 + Age + WRATtotal, data = AllvarsWomen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PhysAssault ~ (CVLfrs + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsWomen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1221  -0.5833  -0.4566  -0.3435   2.7156  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)   
## (Intercept)         -2.62705    1.54505  -1.700   0.0891 . 
## CVLfrs               0.05396    0.06545   0.825   0.4096   
## PovStatBelow         2.05736    0.70450   2.920   0.0035 **
## Age                 -0.03856    0.01959  -1.968   0.0491 * 
## WRATtotal            0.04399    0.02738   1.607   0.1081   
## CVLfrs:PovStatBelow -0.26429    0.10384  -2.545   0.0109 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 263.18  on 330  degrees of freedom
## Residual deviance: 246.84  on 325  degrees of freedom
## AIC: 258.84
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

interact_plot(model = CVLTlog3, pred = CVLfrs, modx = PovStat)

sim_slopes(CVLTlog3, pred = CVLfrs,modx = PovStat, centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of CVLfrs when PovStat = Below: 
## 
##    Est.   S.E.   z val.      p
## ------- ------ -------- ------
##   -0.21   0.09    -2.44   0.01
## 
## Slope of CVLfrs when PovStat = Above: 
## 
##   Est.   S.E.   z val.      p
## ------ ------ -------- ------
##   0.05   0.07     0.82   0.41

Dissertation Analyses with MEN only

load(file="/Users/meganwilliams/Desktop/Dissertation/Allvars.rdata")
library(effects)
library(interactions)
library(car)
library(rcompanion)

California Verbal Learning Test (Total Correct Trial A) - Psychological Aggression

Model 1

CVLTlog1 <- glm(PsychAggress ~ CVLtca + WRATtotal, data=AllvarsMen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PsychAggress ~ CVLtca + WRATtotal, family = "binomial", 
##     data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0301   0.5063   0.5755   0.6156   0.7730  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.798723   0.811071   0.985    0.325
## CVLtca      0.023950   0.020988   1.141    0.254
## WRATtotal   0.009861   0.019374   0.509    0.611
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 273.92  on 309  degrees of freedom
## Residual deviance: 271.77  on 307  degrees of freedom
## AIC: 277.77
## 
## Number of Fisher Scoring iterations: 4
#Plots
plot(allEffects(CVLTlog1))

Model 3

CVLTlog3 <- glm(PsychAggress ~ (CVLtca + PovStat)^2 + Age + WRATtotal, data = AllvarsMen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PsychAggress ~ (CVLtca + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2863   0.4517   0.5401   0.6220   0.9852  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)
## (Intercept)          1.23187    1.31908   0.934    0.350
## CVLtca               0.03582    0.02484   1.442    0.149
## PovStatBelow         1.41323    0.90600   1.560    0.119
## Age                 -0.02009    0.01814  -1.108    0.268
## WRATtotal            0.01428    0.01970   0.725    0.469
## CVLtca:PovStatBelow -0.05652    0.04932  -1.146    0.252
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 273.92  on 309  degrees of freedom
## Residual deviance: 266.67  on 304  degrees of freedom
## AIC: 278.67
## 
## Number of Fisher Scoring iterations: 4
plot(allEffects(CVLTlog3))

# California Verbal Learning Test (Total Correct Trial A) - Physical Assault

Model 1

CVLTlog1 <- glm(PhysAssault ~ CVLtca + WRATtotal, data=AllvarsMen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PhysAssault ~ CVLtca + WRATtotal, family = "binomial", 
##     data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5664  -0.5055  -0.4840  -0.4537   2.2332  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -1.97690    0.97190  -2.034   0.0419 *
## CVLtca       0.01956    0.02484   0.788   0.4309  
## WRATtotal   -0.01025    0.02310  -0.444   0.6572  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 218.58  on 309  degrees of freedom
## Residual deviance: 217.91  on 307  degrees of freedom
## AIC: 223.91
## 
## Number of Fisher Scoring iterations: 4
#Plots
plot(allEffects(CVLTlog1))

Model 3

CVLTlog3 <- glm(PhysAssault ~ (CVLtca + PovStat)^2 + Age + WRATtotal, data = AllvarsMen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PhysAssault ~ (CVLtca + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8662  -0.5126  -0.4469  -0.3872   2.3745  
## 
## Coefficients:
##                      Estimate Std. Error z value Pr(>|z|)
## (Intercept)         -0.530550   1.583204  -0.335    0.738
## CVLtca              -0.001519   0.031198  -0.049    0.961
## PovStatBelow        -0.428512   1.030214  -0.416    0.677
## Age                 -0.031025   0.020947  -1.481    0.139
## WRATtotal           -0.005073   0.023931  -0.212    0.832
## CVLtca:PovStatBelow  0.051337   0.051496   0.997    0.319
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 218.58  on 309  degrees of freedom
## Residual deviance: 212.28  on 304  degrees of freedom
## AIC: 224.28
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

California Verbal Learning Test (Long Delayed Free Recall) - Psychological Aggression

Model 1

CVLTlog1 <- glm(PsychAggress ~ CVLfrl + WRATtotal, data=AllvarsMen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PsychAggress ~ CVLfrl + WRATtotal, family = "binomial", 
##     data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1158   0.4350   0.5547   0.6375   0.7875  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) 0.898413   0.814265   1.103   0.2699  
## CVLfrl      0.115531   0.053391   2.164   0.0305 *
## WRATtotal   0.004929   0.019202   0.257   0.7974  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 273.92  on 309  degrees of freedom
## Residual deviance: 268.21  on 307  degrees of freedom
## AIC: 274.21
## 
## Number of Fisher Scoring iterations: 4
#Plots
plot(allEffects(CVLTlog1))

Model 3

CVLTlog3 <- glm(PsychAggress ~ (CVLfrl + PovStat)^2 + Age + WRATtotal, data = AllvarsMen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PsychAggress ~ (CVLfrl + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2511   0.4018   0.5502   0.6573   0.8892  
## 
## Coefficients:
##                      Estimate Std. Error z value Pr(>|z|)  
## (Intercept)          1.345967   1.287352   1.046   0.2958  
## CVLfrl               0.104609   0.061822   1.692   0.0906 .
## PovStatBelow         0.418750   0.613346   0.683   0.4948  
## Age                 -0.015575   0.018090  -0.861   0.3893  
## WRATtotal            0.009933   0.019492   0.510   0.6103  
## CVLfrl:PovStatBelow  0.025877   0.128988   0.201   0.8410  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 273.92  on 309  degrees of freedom
## Residual deviance: 265.01  on 304  degrees of freedom
## AIC: 277.01
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

California Verbal Learning Test (Long Delayed Free Recall) - Physical Assault

Model 1

CVLTlog1 <- glm(PhysAssault ~ CVLfrl + WRATtotal, data=AllvarsMen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PhysAssault ~ CVLfrl + WRATtotal, family = "binomial", 
##     data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6572  -0.5144  -0.4763  -0.4233   2.2608  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -1.84009    0.95850  -1.920   0.0549 .
## CVLfrl       0.07936    0.05874   1.351   0.1767  
## WRATtotal   -0.01476    0.02321  -0.636   0.5248  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 218.58  on 309  degrees of freedom
## Residual deviance: 216.70  on 307  degrees of freedom
## AIC: 222.7
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog1))

Model 3

CVLTlog3 <- glm(PhysAssault ~ (CVLfrl + PovStat)^2 + Age + WRATtotal, data = AllvarsMen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PhysAssault ~ (CVLfrl + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.9270  -0.5090  -0.4437  -0.3861   2.3657  
## 
## Coefficients:
##                      Estimate Std. Error z value Pr(>|z|)
## (Intercept)         -0.700367   1.527841  -0.458    0.647
## CVLfrl               0.030961   0.072110   0.429    0.668
## PovStatBelow        -0.020526   0.762861  -0.027    0.979
## Age                 -0.027406   0.021034  -1.303    0.193
## WRATtotal           -0.009513   0.024058  -0.395    0.693
## CVLfrl:PovStatBelow  0.096139   0.118420   0.812    0.417
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 218.58  on 309  degrees of freedom
## Residual deviance: 211.99  on 304  degrees of freedom
## AIC: 223.99
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

interact_plot(model = CVLTlog3, pred = CVLfrl, modx = PovStat)

sim_slopes(CVLTlog3, pred = CVLfrl, modx = PovStat, centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of CVLfrl when PovStat = Below: 
## 
##   Est.   S.E.   z val.      p
## ------ ------ -------- ------
##   0.13   0.10     1.26   0.21
## 
## Slope of CVLfrl when PovStat = Above: 
## 
##   Est.   S.E.   z val.      p
## ------ ------ -------- ------
##   0.03   0.07     0.43   0.67

California Verbal Learning Test (Short Delayed Free Recall) - Psychological Aggression

Model 1

CVLTlog1 <- glm(PsychAggress ~ CVLfrs + WRATtotal, data=AllvarsMen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PsychAggress ~ CVLfrs + WRATtotal, family = "binomial", 
##     data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1108   0.4675   0.5601   0.6326   0.8022  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) 0.870072   0.809092   1.075   0.2822  
## CVLfrs      0.095899   0.052147   1.839   0.0659 .
## WRATtotal   0.007047   0.019083   0.369   0.7119  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 273.92  on 309  degrees of freedom
## Residual deviance: 269.62  on 307  degrees of freedom
## AIC: 275.62
## 
## Number of Fisher Scoring iterations: 4
#Plots
plot(allEffects(CVLTlog1))

## Model 3

CVLTlog3 <- glm(PsychAggress ~ (CVLfrs + PovStat)^2 + Age + WRATtotal, data = AllvarsMen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PsychAggress ~ (CVLfrs + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2917   0.4332   0.5516   0.6529   0.8583  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)
## (Intercept)          1.44271    1.30139   1.109    0.268
## CVLfrs               0.07414    0.05948   1.247    0.213
## PovStatBelow         0.24603    0.65458   0.376    0.707
## Age                 -0.01674    0.01825  -0.917    0.359
## WRATtotal            0.01191    0.01941   0.614    0.539
## CVLfrs:PovStatBelow  0.05746    0.12912   0.445    0.656
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 273.92  on 309  degrees of freedom
## Residual deviance: 266.38  on 304  degrees of freedom
## AIC: 278.38
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

California Verbal Learning Test (Short Delayed Free Recall) - Physical Assault

Model 1

CVLTlog1 <- glm(PhysAssault ~ CVLfrs + WRATtotal, data=AllvarsMen,family = "binomial")
summary(CVLTlog1)
## 
## Call:
## glm(formula = PhysAssault ~ CVLfrs + WRATtotal, family = "binomial", 
##     data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5624  -0.5090  -0.4835  -0.4522   2.1895  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -1.884761   0.962994  -1.957   0.0503 .
## CVLfrs       0.045359   0.058779   0.772   0.4403  
## WRATtotal   -0.009693   0.022981  -0.422   0.6732  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 218.58  on 309  degrees of freedom
## Residual deviance: 217.94  on 307  degrees of freedom
## AIC: 223.94
## 
## Number of Fisher Scoring iterations: 4
#Plots
plot(predictorEffect("CVLfrs",CVLTlog1))

Model 3

CVLTlog3 <- glm(PhysAssault ~ (CVLfrs + PovStat)^2 + Age + WRATtotal, data = AllvarsMen, family = "binomial")
summary(CVLTlog3)
## 
## Call:
## glm(formula = PhysAssault ~ (CVLfrs + PovStat)^2 + Age + WRATtotal, 
##     family = "binomial", data = AllvarsMen)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8423  -0.5184  -0.4502  -0.3896   2.3706  
## 
## Coefficients:
##                      Estimate Std. Error z value Pr(>|z|)
## (Intercept)         -0.578499   1.542806  -0.375    0.708
## CVLfrs              -0.001410   0.072291  -0.020    0.984
## PovStatBelow         0.037239   0.778933   0.048    0.962
## Age                 -0.030677   0.021082  -1.455    0.146
## WRATtotal           -0.004841   0.023977  -0.202    0.840
## CVLfrs:PovStatBelow  0.080411   0.121807   0.660    0.509
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 218.58  on 309  degrees of freedom
## Residual deviance: 213.13  on 304  degrees of freedom
## AIC: 225.13
## 
## Number of Fisher Scoring iterations: 5
#Plots
plot(allEffects(CVLTlog3))

interact_plot(model = CVLTlog3, pred = CVLfrs,modx = PovStat)

sim_slopes(CVLTlog3, pred = CVLfrs,modx = PovStat, centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of CVLfrs when PovStat = Below: 
## 
##   Est.   S.E.   z val.      p
## ------ ------ -------- ------
##   0.08   0.10     0.76   0.45
## 
## Slope of CVLfrs when PovStat = Above: 
## 
##    Est.   S.E.   z val.      p
## ------- ------ -------- ------
##   -0.00   0.07    -0.02   0.98